Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis

Girma Nega; Migbar Sibhat; Altayework Mekonnen; Tesfaye Techane

doi:10.12688/f1000research.124586.2

Home Browse Survival status and predictors of mortality among mechanically ventilated...

ALL Metrics

Views

Downloads

Get PDF

Get XML

Export

▬

✚

Research Article

Revised

Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis

[version 2; peer review: 1 approved]

Girma Nega¹^*, Migbar Sibhat ²^*, Altayework Mekonnen¹, Tesfaye Techane¹

^* Equal contributors

PUBLISHED 29 Sep 2023

Author details Author details

¹ School of Nursing, Saint Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
² College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia

Girma Nega
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation

Migbar Sibhat
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Altayework Mekonnen
Roles: Formal Analysis, Funding Acquisition, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Tesfaye Techane
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Coronavirus (COVID-19) collection.

Abstract

Background: The COVID-19 pandemic has caused stress on the health care system of many countries worldwide. To accommodate the growing number of critically ill patients requiring mechanical ventilation, hospitals expanded and tried to meet overwhelming demands. Despite this, outcomes of patients after mechanical ventilation were devastating, with high mortality rates. Therefore, this study investigated the survival status and predictors of mortality among mechanically ventilated COVID-19 patients.
Methods: A retrospective cohort study was applied on the patient charts of 496 critically ill and mechanically ventilated COVID-19 patients at intensive care units of Addis Ababa COVID-19 Care Centers from September 2020 to October 2021. Data were collected using a data extraction checklist and entered into Epi data manager. Then, data were transferred to STATA V-14 for cleaning and analysis. The cox-proportional hazard regression model was used for analysis. Covariates with p-value ≤0.20 in the bivariate analysis were fitted to multivariate analysis after the model fitness test. Finally, statistical significance was decided at p-value <0.05, and hazard ratios were used to determine the strength of associations.
Results: Of the 496 patients, 63.3% had died. The incidence rate of mortality was 56.7 (95% CI: 50.80, 63.37) per 1,000 person-days of observation, with 5534 person-day observations recorded. Advanced age (>60 years old) (adjusted hazard ratio (AHR)=1.86; 95% CI: 1.09, 3.15) and being invasively ventilated (AHR=2.02; 95% CI: 1.25, 3.26) were associated with increased risk of mortality. Furthermore, presence of diabetes (AHR=1.50; 95% CI: 1.09, 2.08), shock (AHR=1.99; 95% CI: 1.12, 3.52), and delirium (AHR=1.60; 95% CI: 1.05, 2.44) were significantly associated with increased mortality.
Conclusions: Clear directions are needed in the recommendation of non-invasive versus invasive ventilation, especially among elderly patients. The controversy of when to intubate (early versus late) needs to be clarified as well. Early detection and prompt management of shock is paramount.

Keywords

Mechanical ventilator, COVID-19, Mortality, predictors, Ethiopia

Corresponding authors: Girma Nega, Migbar Sibhat

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2023 Nega G et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Nega G, Sibhat M, Mekonnen A and Techane T. Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis [version 2; peer review: 1 approved]. F1000Research 2023, 11:1329 (https://doi.org/10.12688/f1000research.124586.2) First published: 16 Nov 2022, 11:1329 (https://doi.org/10.12688/f1000research.124586.1) Latest published: 29 Sep 2023, 11:1329 (https://doi.org/10.12688/f1000research.124586.2)

Revised Amendments from Version 1

No major changes were made to this version. The changes made did not alter the study findings and could not influence the interpretation of results. The revisions made were mainly editorial and changes in the way of presentation. For example, the methods section of the abstract was shortened, patients' age and sex data were incorporated in Table 2, and "Sig." in Table 5 was replaced with "P-value".

See the authors' detailed response to the review by Fadi Aljamaan

Introduction

Coronavirus disease 2019 (COVID-19), a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) (One), imposed public health challenges within a short period.¹ Over the past two years, the COVID-19 pandemic caused stress on the health care system of many countries in the world. Hospitals were overcrowded, and many lives were lost worldwide. As of 29^th June 2022, about 545.2 million confirmed COVID-19 cases were reported globally, of which 6,334,728 have died, and around 12 billion people received vaccination (One).

In the first few months of the pandemic, 13.8% of the total confirmed cases were severe, and 5% were critically ill.¹ Severe respiratory failure was reported as the most common cause of death.² According to the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) report, the world's largest database, of all confirmed COVID-19 cases, 20% required intensive care unit (ICU) or highly dependent unit (HDU) admission during their illness.³

The mechanism of COVID-19 infection remains puzzling yet. It is hypothesized that it may affect the pulmonary, cardiac, nervous, hepatic, renal and the coagulation system.⁴^–⁶ COVID-19 can lead to different health consequences, including cardiomyopathy, shock, acute coronary syndrome, lung fibrosis, myocarditis, acute kidney injury, thromboembolism and arrhythmias.⁴^–⁷

Because of acute respiratory distress syndrome (ARDS) and diffuse parenchyma injury, about 60-90% of patients admitted to the ICU demand respiratory support with mechanical ventilators.⁸^–¹⁰ Mechanical ventilation (MV) is the medical term for artificial ventilation where machines are used to assist or replace spontaneous breathing either invasively or non-invasively. Invasive mechanical ventilation (IMV) is a method of ventilation that involves inserting a plastic/metal tube into the patient's airway. IMV is performed in the ICU or emergency room to meet the patient's oxygen demands. Conversely, non-invasive ventilation (NIV) is breathing support administered through a face mask, nasal mask, or a helmet without tracheal intubation.¹¹^–¹³

In the earliest stages of the pandemic, early intubation was encouraged over NIV due to the fear of increased viral transmission from aerosolization.¹⁴^,¹⁵ Lately, almost all studies conducted to investigate the outcome of mechanically ventilated patients reported devastating figures (mortality rate as high as 97%). Nonetheless, the proportion of mortality was even higher among invasively ventilated patients,¹⁶^–¹⁸ and NIV was associated with a significant survival benefit.¹⁹^–²¹ On the other hand, it was hypothesized that COVID-19 might not cause classic ARDS that question the use of mechanical ventilation in such patients.²²^,²³

Healthcare interventions undertaken in the initial times of the pandemic in line with community mobilization and considerable government commitment played a significant role to limit the number of new cases and deaths due to COVID-19. The pandemic resulted in remarkable economic crisis, especially in low and middle-income countries. This damage is estimated to take years to recover.²⁴

The number of incident cases, severity of the disease, and fatality rate in Ethiopia were lower than in the rest of the world in the earliest periods. Nonetheless, the COVID-19 infectivity, severity, and fatality rate have reached to peak after then. At present, it imposes an additional impact on the country's overburdened healthcare and socio-economic issues.²⁵ Therefore, in this study, we aimed to assess survival status and predictors of mortality of mechanically ventilated COVID-19 patients admitted to the ICU settings.

Methods

A retrospective cohort study was employed. Recordings of patients admitted to the ICU settings of the COVID-19 Care Centers in Addis Ababa, Ethiopia starting from September 2020 to October 2021 were reviewed retrospectively. There were three major COVID-19 Care Centers in Addis Ababa. Despite considerable resource delimitations, Millennium COVID-19 Care Center (MCCC) was recognized as an organized and relatively well-equipped COVID-19 care center compared to other centers countrywide. The center is the largest COVID-19 treatment center in the country, with a capacity of greater than 1,000 admission beds. It serves patients with confirmed mild, moderate, and critically ill COVID-19 infection in different treatment units, including outpatient departments, emergency service, wards, HDU, and ICU service (>40 ICU beds). Until November 2021, an overall 6,211 patients were admitted to the center. Of those, 1,537 were diagnosed with severe and critical illness, demanding ICU services. Among those who were admitted to the ICU settings of MCCC, 722 were admitted to the ICU from September 2020 to October 2021. Data collection was conducted from January 23, 2022, to March 07, 2022. We have taken efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers. The study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies.²⁶

Population and participant recruitment methods

All COVID-19 infected patients admitted to the ICU and received mechanical ventilation support at Addis Ababa COVID-19 care centers were the source population. The study population included recordings of all patients admitted to the ICU and received respiratory support with a mechanical ventilator at the Millennium COVID-19 Care Center from September 2020 to October 2021. The lists of medical records were obtained from the COVID-19 registry and the liaison office reports. Recordings of patients deceased in the therapeutic center were considered events, whereas recordings of patients who discharged alive, weaned off from mechanical ventilator, and transferred out, were considered censored cases. Patient recordings with complete information were included in the study. The details of participant recruitment procedures were described in the flowchart (Figure 1).

Figure 1. A flow diagram of sample recruitment methods among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

ICU, intensive care unit.

Operationalization of terms and measurements

An event was considered when a patient died while on mechanical ventilation. Time to event or censor refers to the time to occurrence of an event (death) or other outcomes measured from ICU admission to the date that the event or censor occurred. Censored were those cases discharged alive from the ICU, transferred to other institutions, or weaned off to mechanical ventilators. Mechanical ventilation refers to providing artificial respiratory support using a ventilator machine either invasively (IMV) or non-invasively (NIV). The follow-up was started when the patient was put on a mechanical ventilator for respiratory support and ended when the patient was weaned off from a machine, transferred out from ICU while on MV, or died while receiving MV support, and was measured in days.

Data collection tools and procedure

The data extraction checklist was developed from the WHO COVID-19 guideline, mechanical ventilation protocols, and related literature.²⁷^,²⁸ Before the actual study, recordings of all ICU mechanically ventilated patients were roughly reviewed (extracted) to identify the study subjects using medical record numbers (MRN) obtained from the ICU admission and discharge registration book. Then, all charts that fulfilled the inclusion criteria were reviewed, and relevant data were extracted. The data extraction tool comprised socio-demographic, disease-related, ventilator-related, and management-related variables. Data collectors and supervisors were recruited and trained on the data collection requirements. The checklist was pretested on 5%²⁵ of randomly selected charts at MCCC to check its clarity and consistency. Accordingly, variables not recorded in the patient recordings were excluded from the extraction checklist. The pretest data was not included in the actual analysis. During data collection, each filled checklist was crosschecked and revised daily by the investigator for completeness. Hence, incomplete tools and missing information were managed at the spot during the data collection procedure by cross-checking and communicating with the data collector at the end of each day.

Data processing, analysis, and presentation

After completion of data collection, the data was coded and entered into EpiData Manager 4.4.2.1 and exported to Stata 14 (an open-access alternative that can perform an equivalent function is Rstudio) for data cleaning and analysis. Then, an exploratory analysis was conducted to determine the level of missing values, influential outliers, and data distribution. The data were described using frequency tables, percentages, graphs, and means with standard deviations or medians with interquartile ranges based on the nature of data. A Kaplan-Meier curve was constructed to compare survival differences and to estimate the median survival time. Life tables were used to estimate cumulative probabilities of death at different time intervals.

Bivariate analysis was conducted using the Cox-proportional hazard model to determine the association between each independent variable with the outcome variable (death). Accordingly, variables with a p-value of ≤0.20 were considered for further analysis (multivariate analysis) to identify the net effect of each variable on mortality. The proportional hazard model assumption was checked using a global test (Schoenfeld residuals) (p=0.877). Besides, model fitness was checked using Cox-Snell residuals, and the graph showed the model fits the data well (Figure 2). Finally, statistical significance was declared at p≤0.05, and the strength of associations was summarized using an adjusted hazard ratio (AHR) with 95% confidence intervals.

Figure 2. Cox-Snell residual for the assessment of Cox proportional hazard model fitness among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Ethical consideration

The study was conducted after granting ethical approval (Reference=PM23/799; dated 06/12/2021) from the Saint Paul’s Hospital Millennium Medical College institutional review board (IRB). After approval, the IRB provided a waiver of patient consent since the study was conducted by chart review. Accordingly, permission for data collection was received from hospital directors on behalf of patients to get full access to patient recordings. Data coding and aggregate reporting were used so that names and other personal identifiers were not stated throughout the study process to ensure anonymity and confidentiality.

Results

Overall, 722 COVID-19 infected patients were admitted to ICU settings in the Millennium COVID-19 care center from September 2020 to October 2021. Consequently, 496 (n=496) mechanically ventilated patients with complete records were incorporated for analysis.

Description of socio-demographic characteristics of study participants

The study finding showed that more than 70% (350/496) of study participants were male. On the other hand, 79% of male patients and 60% of female patients died in the ICU (Table 2). The mean age of participants was 59 years (SD=14.5), and only one-fourth of the study subjects were below the age of 50 years (Table 1). The proportion of death was 41.5% (59/142) among patients below 50 years of age. In contrast, about 80% (n=200) of patients aged 65 years and over died during the follow-up period.

Table 1. Overall presentation of continuous variables using numeric measures among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Continuous variable	Statistical measure
Continuous variable	Mean	SD	Median	IQR	1^st quartile	3^rd quartile	Min.	Max.
Age (in years)	59.25	14.48	60	21	49	70	24	88
Days of Sx onset	7.38	2.76	8	3	6	9	1	27
SPO₂ at baseline	88	51.34	86	6	82	88	56	92
Baseline albumin	2.38	1.32	2.3	1.9	1.4	3.25	0.52	5.62
Average PIP	30.97	4.48	32	6	28	34	16	40
Average PEEP	10.50	1.64	10	2	10	12	6	14
Average FiO₂	87.46	13.89	90	17.5	82.5	100	50	100
Days on MV	10.69	5.43	10	7	7	14	1	39
WBC at the end	17.82	4.72	19	9.5	12.5	22	9	26
Cr at the end	2.54	1.65	2.6	2.9	0.8	3.7	0.2	8.8

Abbreviations: SD, standard deviation; IQR, inter quartile range; Sx, symptom; SPO₂, oxygen saturation; PIP, peak inspiratory pressure; PEEP, positive end expiratory pressure; FiO₂, fraction of inspired oxygen; MV, mechanical ventilator; WBC, white blood cell; Cr, creatinine.

Distribution of comorbidities and baseline investigation findings

Based on the findings, more than 55% (276/496) of patients were admitted to the center after seven days of symptom onset. The median time from symptom onset to hospital admission was eight days (IQR=3). Drastically, more than 75% of the cases were found to be hypoalbuminemic (<3.25g/dl) with a mean albumin level of 2.38g/dl (SD=1.32) at admission (Table 2). The findings also revealed that 314 patients had at least one chronic medical illness, of which 78.7% (247/314) have died. Conversely, 36.8% (67/182) of patients who did not have any chronic illness ended up with death due to COVID-19 infection. Diabetes mellitus (DM) (47%, n=496) and hypertension (HTN) (43%, n=496) were the most frequently recorded comorbidities. The proportion of death was much higher among those having DM (84.5%, n=233) and HTN (81.3%, 214) compared to patients who were free of DM (44.5%, n=263) and HTN (49.6%, n=282) (Table 2).

Table 2. Distribution of age, sex and comorbidities among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Independent variables	Categories	Outcome status
Independent variables	Categories	Died, n (%)	Censored, n (%)	Total, n (%)
Age	≤40yrs	17	38	55
	41-60yrs	117	98	215
	>60yrs	180	46	226
Sex	Male	227	123	350
Sex	Female	87	59	146
Comorbidities	No	67 (36.8)	115 (63.2)	182 (36.7)
Comorbidities	Yes	247 (78.67)	67 (21.3)	314 (63.3)
DM	No	117 (44.5)	146 (55.5)	263 (53)
DM	Yes	197 (84.5)	36 (15.5)	233 (47)
HTN	No	140 (49.6)	142 (50.4)	282 (56.9)
HTN	Yes	174 (81.3)	40 (18.7)	214 (43.1)
CKD	No	273 (61.8)	169 (38.2)	442 (89.1)
CKD	Yes	41 (75.9)	13 (24.1)	54 (10.9)
Cardiac	No	286 (62.2)	174 (37.8)	460 (92.7)
Cardiac	Yes	28 (77.8)	8 (22.2)	36 (7.3)
Asthma	No	254 (59.9)	170 (40.1)	424 (85.5)
Asthma	Yes	60 (83.3)	12 (16.7)	72 (14.5)
COPD	No	263 (60)	175 (40)	438 (88.3)
COPD	Yes	51 (87.9)	7 (12.1)	58 (11.7)
HIV/AIDS	No	196 (41.9)	272 (58.1)	468 (94.3)
HIV/AIDS	Yes	18 (64.3)	10 (35.7)	28 (5.7)
Smoking status	No	204 (54.5)	170 (45.5)	374 (75.4)
Smoking status	Yes	110 (90.16)	12 (9.83)	122 (24.6)

Abbreviations: DM, diabetes mellitus; HTN, hypertension; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; HIV/AIDS, human immunodeficiency virus/acquired immune deficiency syndrome.

Description of COVID-19 management and ventilator-related variables

After commencing artificial ventilation, patients remain on a mechanical ventilator for a median of 10 days (IQR=7). In-line with this, more than 55% (174/314) of deaths were recorded within the first 10 days of MV initiation (Table 1). According to the study result, 315 (63.5%) patients were intubated. Pressure regulated volume control (PRVC) was the most widely used (63.5%, n=315) mode of ventilation for intubated patients. An overall 181 (36.5%) patients were ventilated only with the non-invasive method of ventilation, of which 29 (16%) died. In opposition, 285 (90.5%, n=315) deaths were recorded among patients who received invasive ventilator support. The study results also showed that 71.8% (356/496) of study subjects developed ICU complications. All 496 (100%) study subjects took antibiotics, steroid therapy, and thromboprophylaxis (Table 3).

Table 3. Distribution of management-related characteristics among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Independent variable	Categories	Outcome status
Independent variable	Categories	Died, n (%)	Censored, n (%)	Total, n (%)
Method of ventilation	NIV	29 (16)	152 (84)	181 (36.5)
Method of ventilation	IV	285 (90.5)	30 (9.5)	315 (63.5)
Mode of ventilation	PRVC	178 (89)	22 (11)	200 (40.3)
	PCV	74 (92.5)	6 (7.5)	80 (16.1)
	VCV	33 (94.3)	2 (5.7)	35 (7.1)
	CPAP	29 (16)	152 (84)	181 (36.5)
Vasopressor use	No	23 (15.8)	123 (84.2)	146 (29.4)
Vasopressor use	Yes	291 (83.1)	59 (16.8)	350 (70.6)
Hemodialysis	No	312 (63.2)	182 (36.8)	494 (99.6)
Hemodialysis	Yes	2 (100)	0 (0)	2 (0.4)
Complication	No	13 (9.3)	127 (90.7)	140 (28.2)
Complication	Yes	301 (84.5)	55 (15.4)	356 (71.8)
Chest tube inserted	No	219 (56.7)	167 (43.3)	386 (77.8)
Chest tube inserted	Yes	95 (86.4)	15 (13.6)	110 (22.2)
Reason for Chest tube insertion	Pneumothorax	92 (86.8)	14 (13.2)	106 (21.4)
Reason for Chest tube insertion	Hemothorax	3 (75)	1 (25)	4 (0.8)
AKI	No	49 (24.6)	150 (75.4)	199 (40.1)
AKI	Yes	265 (89.2)	32 (10.8)	297 (59.9)
Sepsis/HAI	No	13 (9.3)	127 (90.7)	140 (28.2)
Sepsis/HAI	Yes	301 (84.5)	55 (15.4)	356 (71.8)
Electrolyte imbalance	No	20 (13.5)	128 (86.5)	148 (29.8)
Electrolyte imbalance	Yes	294 (84.5)	54 (15.5)	348 (70.2)
Delirium	No	40 (20.1)	159 (79.9)	199 (40.1)
Delirium	Yes	275 (92.6)	23 (7.4)	297 (59.9)
Shock	No	24 (14.8)	138 (85.2)	162 (32.7)
Shock	Yes	290 (86.8)	44 (13.2)	334 (67.3)
Coagulopathy	No	102 (40.5)	150 (59.5)	252 (50.8)
Coagulopathy	Yes	212 (86.9)	32 (13.1)	244 (49.2)

Abbreviations: NIV, non-invasive ventilation; IV, invasive ventilation; PRVC, pressure regulated volume control; PCV, pressure control ventilation; VCV, volume control ventilation; CPAP, continuous positive airway pressure; AKI, acute kidney injury; HAI, hospital-acquired infections.

Comparison of survival status using Kaplan Meier curve

The Kaplan-Meier survival curve decreases stepwise and crosses the survival function at a survival probability of 0.5 (50%) (Figure 3).

Figure 3. Overall Kaplan-Meier curve for mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Survival function and incidence rate of mortality

The overall mortality rate among mechanically ventilated COVID-19 patients was 63.3% (95% CI: 59.1, 67.6). Among 182 (36.7%) patients who were considered censored, 170 (93.4%) patients were weaned off from mechanical ventilators, and the rest 12 (6.6%) were transferred to other institutions. The total person-time observation was 5534 person-days. The incidence rate of mortality among mechanically ventilated COVID-19 infected patients was 56.7 (95% CI: 50.80, 63.37) per 1000 person-days of observation. The median survival time was found to be 13 days (95% CI: 8.0, 18.0). Thus, 50% of participants could remain on mechanical ventilation for up to 13 days of follow-up. The cumulative probabilities of mortality at the end of 7, 14, 21, and 28 days were 0.24, 0.72, 0.95, and 0.98 consecutively (Table 4).

Table 4. Life table of mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Time interval, months	Beginning, total number	Death, total number	Cumulative failure, Probability (95% CI)
0–7	496	120	0.24 (0.21–0.28)
7–14	376	237	0.72 (0.68–0.76)
14–21	139	115	0.95 (0.93–0.97)
21–28	24	14	0.98 (0.96–0.99)

Predictors of death among mechanically ventilated COVID-19 patients

After determining the nature of the data, the Kaplan-Meier curve and life tables were used for data description. Then, a log-rank statistic test was used to compare the survival difference between groups. Afterward, a bivariate analysis was conducted, and variables to be included in the final model for multivariable analysis were identified. Accordingly, patient's age, comorbidities, shock, albumin, peak inspiratory pressure (PIP), positive end expiratory pressure (PEEP), DM, chronic kidney disease (CKD), chronic obstructive pulmonary diseases (COPD), method of ventilation, coagulopathy, acute kidney injury (AKI) and delirium after admission were transferred for further analysis. However, sepsis, length of stay on MV, electrolyte level, and smoking status were excluded since they violate the proportional hazard assumptions with the value of less than 0.05 during the Schoenfield residual test.

In the multivariable analysis, advanced age, invasive ventilation, DM, delirium, and shock were found to be independent predictors of mortality among mechanically ventilated COVID-19 infected patients at a 95% confidence level (Table 5).

Table 5. Bivariate and multivariable analysis output of Cox proportional hazard regression among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Covariate	Category	Outcome variable		CHR (95% CI)	P-value	AHR (95% CI)	P-value
Covariate	Category	Death	Censored	CHR (95% CI)	P-value	AHR (95% CI)	P-value
Age	≤40 years	17	38	1	1	1	1
	41-60 years	117	98	1.61 (0.97, 2.68)	0.067	1.18 (0.67, 2.03)	0.548
	>60 years	180	46	3.06 (1.86, 5.04)	<0.001	1.86 (1.09, 3.15)	0.022*
Comorbidities	No	67	115	1	1	1	1
Comorbidities	Yes	247	67	1.83 (1.39, 2.41)	<0.001	0.71 (0.47, 1.07)	0.105
DM	No	117	146	1	1	1	1
DM	Yes	197	36	1.88 (1.49, 2.36)	0.001	1.50 (1.09, 2.08)	0.014*
HTN	No	140	142	1	1	1	1
HTN	Yes	174	40	1.49 (1.19, 1.88)	<0.001	0.86 (0.61, 1.22)	0.391
CKD	No	273	169	1	1	1	1
CKD	Yes	41	13	1.24 (0.89, 1.73)	0.195	0. 99 (0.68, 1.44)	0.974
COPD	No	263	175	1	1	1	1
COPD	Yes	51	7	1.22 (0.89, 1.65)	0.202	0.98 (0.67, 1.40)	0.908
Albumin at baseline	<3.4 g/dl	269	123	1.55 (1.13, 2.12)	0.007	0.89 (0.63, 1.26)	0.528
Albumin at baseline	≥3.4 g/dl	45	59	1	1	1	1
Method of ventilation	NIV	29	152	1	1	1	1
Method of ventilation	IV	285	30	5.68 (3.87, 8.33)	<0.001	2.02 (1.25, 3.26)	0.004*
PEEP	≤10 cmH₂O	163	122	1	1	1	1
PEEP	>10 cmH₂O	151	60	1.55 (1.24, 1.94)	<0.001	1.26 (0.99, 1.60)	0.062
PIP	≤35 cmH₂O	227	167	1	1	1	1
PIP	>35 cmH₂O	87	15	1.35 (1.05, 1.73)	0.019	1.04 (0.79, 1.37)	0.796
Shock after admission	No	24	138	1	1	1	1
Shock after admission	Yes	290	44	6.06 (3.99, 9.19)	<0.001	1.99 (1.12, 3.52)	0.019*
Coagulopathy	No	102	150	1	1	1	1
Coagulopathy	Yes	212	32	2.03 (1.60, 2.58)	<0.001	1.03 (0.78, 1.35)	0.838
AKI after admission	No	49	150	1	1	1	1
AKI after admission	Yes	265	32	3.97 (2.92, 5.39)	<0.001	1.43 (0.97, 2.12)	0.070
Delirium in ICU	No	40	159	1	1	1	1
Delirium in ICU	Yes	275	23	4.55 (3.26, 6.34)	<0.001	1.60 (1.05, 2.44)	0.029*

* Statistically significant at a 95% uncertainty interval in the multivariable analysis.

Abbreviations: CHR, crude hazard ratio; AHR, adjusted hazard ratio; CI, confidence interval; DM, diabetes mellitus; HTN, hypertension; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; PEEP, positive end-expiratory pressure; PIP, peak inspiratory pressure; AKI, acute kidney injury; ICU, intensive care unit.

Discussion

This study was conducted to determine survival status and predictors of mortality among COVID-19 patients who were mechanically ventilated. The incidence rate of mortality among mechanically ventilated COVID-19 patients was 56.7 (95% CI: 50.80, 63.37) per 1000 person-days of observation. The median survival time was found to be 13 days (95% CI: 8.0, 18.0). Older age (>60yrs), having DM, shock, and delirium while on MV were reported as predictors of mortality among mechanically ventilated COVID-19 patients. Moreover, being invasively ventilated was significantly associated with an increased mortality.

The overall mortality rate among mechanically ventilated COVID-19 patients was 63.3% (95% CI: 59.1, 67.6). The incidence rate of mortality among COVID-19 patients was 56.7 (95% CI: 50.80, 63.37) per 1000 person-days of observation with a total of 5534 person-day observations. This finding was much higher than reports of previous studies conducted in Belgium, 55%,¹³ Norway, 21%,²⁹ Singapore, 9.1%,²⁴ Kuwait, 45.6%,³⁰ and Saudi Arabia, 32.1%.³¹ The possible rationale might be that previous studies used a small sample size and included mild and moderate cases,¹³^,³⁰^,³² unlike the current study, which considered only critically ill patients. Besides, a significant delay in admission after symptom onset and shorter duration of stay on a mechanical ventilator (median=10 days) in our study could inflate the mortality rate compared to earlier studies¹³^,³¹ since literature evidenced an inverse relationship between the length of stay and chance of mortality.¹⁹^,³³ Furthermore, the difference in the level of readiness to halt the pandemic would also result in this discrepancy. ICU services require intensive resources in terms of equipment, medications, skilled professionals, space, and time, which is challenging for developing nations like Ethiopia. Here in Ethiopia, medical supplies, including lifesaving medications, were scarce.²⁵^,³⁴

Conversely, a study conducted in Pakistan reported a higher ICU mortality rate (77%).¹⁹ The possible justification could be that the Pakistan study was conducted in the earliest waves of the pandemic, where countries lack readiness and inconsistent management protocols that might significantly influence patient outcomes. Furthermore, a shorter duration of follow-up and a small sample size applied can contribute to this discrepancy.¹⁹

Patients aged above 60 years were 1.86 times at an increased hazard of death compared to their counterparts (AHR=1.86; 95% CI: 1.09, 3.15). This finding was conformable with reports of several studies conducted abroad.¹⁹^,²⁹^,³¹^,³²^,³⁵^,³⁶ The possible justification could be physiological responses decline with advanced age, which weakens host defense in acute infections resulting in an increased risk of complications and adverse health outcomes.³⁷ On the contrary, previous studies suggested that elders have increased differential gene expression for an inflammatory condition, with a reduction in type I interferon beta. This is because the innate host response to viral infections is stronger in elders than in younger age groups.³⁸

Furthermore, the hazard of death among diabetic patients was 50% (AHR=1.50; 95% CI: 1.09, 2.08) higher than those who were not diagnosed with DM. Results of former studies conducted in Brazil and Norway were in agreement with this finding.²⁹^,³⁹^,⁴⁰ This is because diabetic patients have immune suppression that makes them susceptible to severe infections. Hence, the COVID-19 virus might rapidly replicate and invade the lung parenchyma easily. Moreover, this immune-compromised state can expose patients to superimposed infections such as hospital-acquired or superimposed pneumonia and sepsis.⁴⁰ Diabetic patients also have elevated levels of pro-inflammatory markers.⁴⁰

The hazard of death was two-folds higher among invasively ventilated (intubated) patients compared to their counterparts (AHR=2.02; 95% CI: 1.25, 3.26). This was in line with previous study findings.¹⁹^,²⁰^,³²^,³⁹^,⁴¹ This can be multifactorial. First, in most scenarios, intubation is considered if NIV is unsuccessful, which could increase the chance of refractory hypoxia.⁴² Second, intubated patients were highly prone to ventilator-associated complications, including pneumothorax, ventilator-associated pneumonia, increased risk of sepsis, and sedation inadequacy or excess.⁴³ Third, unlike those non-invasively ventilated, intubated patients can not complain of any discomfort or inconvenience such as pain and are solely dependent on the skill and commitment of health personnel. Previous studies also agreed that more than half of patients who fulfilled intubation criteria survived with just NIV and avoided invasive ventilation, whereas clinical outcomes and mortality among invasively ventilated patients were devastating.²⁰^,⁴¹

Additionally, a diagnosis of shock while on artificial ventilation also showed a statistically significant association with mortality of critically ill COVID-19 patients. Mechanically ventilated patients who developed shock had a twofold (AHR=1.99; 95% CI: 1.12, 3.52) increased risk of death due to critical COVID-19 illnesses compared to their counterparts. We could not find any research that reported the effect of shock on the mortality of COVID-19 patients who needed MV support. The scientific rationale could be that shock can result in hypoperfusion and hypoxemia. To add fire to the chaff, the pressure support (specially PEEP) can compromise tissue perfusion by indirectly counteracting venous return and cardiac output,⁴⁴^–⁴⁶ making shock management arduous. As a result, hypoperfusion of vital organs could lead to multi-organ failure and hypo-function, thus, ending up in death.

On the other hand, the risk of death among patients who developed delirium in the ICU compared to their counterparts was 60% higher (AHR=1.60; 95% CI: 1.05, 2.44). This might be due to the effect of delirium on patient-ventilator interaction. Patients with delirium usually have asynchrony, especially during NIV support. Furthermore, delirious patients would fight with the ventilator machine, which could increase peak airway pressure. Sequentially, this will lead to an increased risk of pulmonary barotrauma (pneumothorax). On the other hand, delirious patients require an extra dose of sedation to maintain synchrony, exposing them to unintended drug adverse effects and an unrousable state. Consequently, all these factors can deteriorate the patient's condition and result in death.

The study considered censored observations. Besides, in addition to baseline information, follow-up data were used to estimate patient characteristics, which enabled a more accurate investigation of the effects of these variables. Conversely, this study also has its limitations. First, there was considerable delay in ICU admission and mechanical ventilation support from symptom onset. Hence, there might be an overestimation of the mortality rate. Second, we did not evaluate the effect of some important variables such as procalcitonin, troponin, serum ferritin, d-dimer, lactate dehydrogenase, and interleukin-6 that showed statistically significant association in the previous studies conducted abroad due to lack of access for these laboratory tests.

Conclusions

The study finding showed a high incidence of mortality among critically ill COVID-19 patients who were supported with mechanical ventilators. Older age (>60 years), invasive means of mechanical ventilation, having DM, and ICU complications such as shock and delirium were independent predictors of death among mechanically ventilated COVID-19 patients. Clear directions are needed in the recommendation of non-invasive versus invasive ventilation, especially among elderly patients. The controversy of when to intubate (early versus late intubation dilemma) needs to be clarified as well. Early detection and prompt management of shock is paramount, especially considering that most intubated patients develop shock immediately after intubation and are due to sepsis.

Data availability

Underlying data

Figshare: DATASET for Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia. https://doi.org/10.6084/m9.figshare.21126178.⁴⁷

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Reporting guidelines

Figshare: STROBE checklist for ‘Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia’. https://doi.org/10.6084/m9.figshare.20870908.²⁶

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgments

The authors would like to thank data collectors, supervisors, hospital staff, and administrators for their unreserved efforts and commitment. The authors also appreciated Saint Paul’s Hospital Millennium Medical College for chasing this chance and covering the data collection costs.

References

1. Novel Coronavirus Pneumonia Emergency Response Epidemiology: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi= Zhonghua liuxingbingxue zazhi. 2020; 41(2): 145.
2. Berlin DA, Gulick RM, Martinez FJ: Severe covid-19. N. Engl. J. Med. 2020; 383(25): 2451–2460. Publisher Full Text
3. Winck J, Scala R: Non-invasive respiratory support paths in hospitalized patients with COVID-19: Proposal of an algorithm. Pulmonology. 2021; 27: 305–312. PubMed Abstract | Publisher Full Text
4. Wiersinga WJ, Rhodes A, Cheng AC, et al.: Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020; 324(8): 782–793. Publisher Full Text
5. Costa IBSS, Bittar CS, Rizk SI, et al.: The heart and COVID-19: what cardiologists need to know. Arq. Bras. Cardiol. 2020.
6. Fraissé M, Logre E, Pajot O, et al.: Thrombotic and hemorrhagic events in critically ill COVID-19 patients: a French monocenter retrospective study. Crit. Care. 2020; 24(1): 1–4.
7. Joseph A, Zafrani L, Mabrouki A, et al.: Acute kidney injury in patients with SARS-CoV-2 infection. Ann. Intensive Care. 2020; 10(1): 1–8.
8. Wu Z, McGoogan JM: Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020; 323(13): 1239–1242.
9. Audit: ICNARC report on COVID-19 in critical care.April. 2020; 10: 2020.
10. Grasselli G, Zangrillo A, Zanella A, et al.: Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA. 2020; 323(16): 1574–1581. Publisher Full Text
11. Brochard L: Mechanical ventilation: invasive versus noninvasive. Eur. Respir. J. 2003; 22(47 suppl): 31s–37s. Publisher Full Text
12. Wilson ME, Dobler CC, Morrow AS, et al.: Association of home noninvasive positive pressure ventilation with clinical outcomes in chronic obstructive pulmonary disease: a systematic review and meta-analysis. JAMA. 2020; 323(5): 455–465. PubMed Abstract | Publisher Full Text
13. de Terwangne C , Sorgente A, Tortora R, et al.: Mortality Rate and Predictors among Patients with COVID-19 Related Acute Respiratory Failure Requiring Mechanical Ventilation: A Retrospective Single Centre Study. J. Crit. Care Med. 2021; 7(1): 21–27. PubMed Abstract | Publisher Full Text
14. Phua J, Weng L, Ling L, et al.: Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir. Med. 2020; 8(5): 506–517. PubMed Abstract | Publisher Full Text
15. McGain F, Humphries RS, Lee JH, et al.: Aerosol generation related to respiratory interventions and the effectiveness of a personal ventilation hood. Crit. Care Resusc. 2020; 22(3): 212–220. PubMed Abstract
16. Wang Y, Lu X, Li Y, et al.: Clinical course and outcomes of 344 intensive care patients with COVID-19. Am. J. Respir. Crit. Care Med. 2020; 201(11): 1430–1434. PubMed Abstract | Publisher Full Text
17. Quah P, Li A, Phua J: Mortality rates of patients with COVID-19 in the intensive care unit: a systematic review of the emerging literature. Crit. Care. 2020; 24: 1–4.
18. Zhou F, Yu T, Du R, et al.: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229): 1054–1062. PubMed Abstract | Publisher Full Text
19. Rahim F, Amin S, Noor M, et al.: Mortality of Patients With Severe COVID-19 in the Intensive Care Unit: An Observational Study From a Major COVID-19 Receiving Hospital. Cureus. 2020; 12(10).
20. Daniel P, Mecklenburg M, Massiah C, et al.: Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support. Am. J. Emerg. Med. 2021; 43: 103–108. PubMed Abstract | Publisher Full Text
21. Hua J, Qian C, Luo Z, et al.: Invasive mechanical ventilation in COVID-19 patient management: the experience with 469 patients in Wuhan. Crit. Care. 2020; 24(1): 1–3.
22. Cascella M, Rajnik M, Aleem A, et al.: Features, evaluation, and treatment of coronavirus (COVID-19). StatPearls; 2021.
23. Gattinoni L, Chiumello D, Caironi P, et al.: COVID-19 pneumonia: different respiratory treatments for different phenotypes? Springer; 2020.
24. Chew SY, Lee YS, Ghimiray D, et al.: Characteristics and Outcomes of COVID-19 Patients with Respiratory Failure Admitted to a “Pandemic Ready” Intensive Care Unit-Lessons from Singapore. Ann. Acad. Med. Singap. 2020; 49(7): 434–448. PubMed Abstract | Publisher Full Text
25. Tessema GA, Kinfu Y, Dachew BA, et al.: The COVID-19 pandemic and healthcare systems in Africa: a scoping review of preparedness, impact and response. BMJ Glob. Health. 2021; 6(12): e007179. PubMed Abstract | Publisher Full Text
26. Sibhat M: STROBE checklist for ‘Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia’: figshare.2022. Publisher Full Text
27. World Health Organization: Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: interim guidance, 13 March 2020. World Health Organization; 2020.
28. National Institutes of Health: COVID-19 Treatment Guidelines. Oxygenation and ventilation. NIH; 2020.
29. Laake JH, Buanes EA, Småstuen MC, et al.: Characteristics, management and survival of ICU patients with coronavirus disease-19 in Norway, March-June 2020. A prospective observational study. Acta Anaesthesiol. Scand. 2021; 65(5): 618–628. PubMed Abstract | Publisher Full Text
30. Ayed M, Borahmah AA, Yazdani A, et al.: Assessment of clinical characteristics and mortality-associated factors in COVID-19 Critical cases in Kuwait. Med. Princ. Pract. 2020.
31. Alharthy A, Aletreby W, Faqihi F, et al.: Clinical characteristics and predictors of 28-day mortality in 352 critically ill patients with COVID-19: a retrospective study. J. Epidemiol. Glob. Health. 2021; 11(1): 98–104. PubMed Abstract | Publisher Full Text
32. Giannoglou D, Meimeti E, Provatopoulou X, et al.: Predictors of mortality in hospitalized COVID-19 patients in Athens, Greece. medRxiv.2020.
33. Oliveira E, Parikh A, Lopez-Ruiz A, et al.: ICU outcomes and survival in patients with severe COVID-19 in the largest health care system in central Florida. PLoS One. 2021; 16(3): e0249038. PubMed Abstract | Publisher Full Text
34. Kebede E, Yoseph E, Asaye H, et al.: Examining Healthcare Workers' Perspectives Concerning Medical Equipment Availability in Three Ethiopian Hospitals: A Qualitative Pilot Study. Cureus. 2021; 13(3): e14134-e. eng. PubMed Abstract | Publisher Full Text
35. Bepouka BI, Mandina M, Makulo JR, et al.: Predictors of mortality in COVID-19 patients at Kinshasa University Hospital, Democratic Republic of the Congo, from March to June 2020. Pan Afr. Med. J. 2020: 37.
36. Tehrani S, Killander A, Åstrand P, et al.: Risk factors for death in adult COVID-19 patients: frailty predicts fatal outcome in older patients. Int. J. Infect. Dis. 2021; 102: 415–421. PubMed Abstract | Publisher Full Text
37. Fried LP, Xue Q-L, Cappola AR, et al.: Nonlinear Multisystem Physiological Dysregulation Associated With Frailty in Older Women: Implications for Etiology and Treatment. J. Gerontol.: Series A. 2009; 64A(10): 1049–1057. PubMed Abstract | Publisher Full Text
38. Smits SL, De Lang A, Van Den Brand JM, et al.: Exacerbated innate host response to SARS-CoV in aged non-human primates. PLoS Pathog. 2010; 6(2): e1000756. PubMed Abstract | Publisher Full Text
39. Santos MM, Lucena EE, Lima KC, et al.: Survival and predictors of deaths of patients hospitalised due to COVID-19 from a retrospective and multicentre cohort study in Brazil. Epidemiol. Infect. 2020: 148.
40. Guo W, Li M, Dong Y, et al.: Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab. Res. Rev. 2020; 36(7): e3319.
41. Sivaloganathan AA, Nasim-Mohi M, Brown MM, et al.: Noninvasive ventilation for COVID-19-associated acute hypoxaemic respiratory failure: experience from a single centre. Br. J. Anaesth. 2020; 125(4): e368–e371. PubMed Abstract | Publisher Full Text
42. Tu Y, Yang P, Zhou Y, et al.: Risk factors for mortality of critically ill patients with COVID-19 receiving invasive ventilation. Int. J. Med. Sci. 2021; 18(5): 1198–1206. PubMed Abstract | Publisher Full Text
43. Haribhai S, Mahboobi SK: Ventilator Complications. StatPearls. Treasure Island FL: © 2022: StatPearls Publishing LLC; 2022.
44. Zhou L, Cai G, Xu Z, et al.: High positive end expiratory pressure levels affect hemodynamics in elderly patients with hypertension admitted to the intensive care unit: a prospective cohort study. BMC Pulm. Med. 2019; 19(1): 1–9.
45. Cournand A, Motley HL, Werko L, et al.: Physiological studies of the effects of intermittent positive pressure breathing on cardiac output in man. American Journal of Physiology-Legacy Content. 1947; 152(1): 162–174. PubMed Abstract | Publisher Full Text
46. Pinsky MR: The hemodynamic consequences of mechanical ventilation: an evolving story. Intensive Care Med. 1997; 23(5): 493–503. PubMed Abstract | Publisher Full Text
47. Sibhat M: DATASET for Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia. figshare. [Dataset]. 2022. Publisher Full Text

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 16 Nov 2022

Author details Author details

¹ School of Nursing, Saint Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
² College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia

Altayework Mekonnen
Roles: Formal Analysis, Funding Acquisition, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Tesfaye Techane
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (2)

version 2

Revised

Published: 29 Sep 2023, 11:1329

https://doi.org/10.12688/f1000research.124586.2

version 1

Published: 16 Nov 2022, 11:1329

https://doi.org/10.12688/f1000research.124586.1

© 2023 Nega G et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

	Views	Downloads
F1000Research	-	-
PubMed Central Data from PMC are received and updated monthly.	-	-

Citations

SEE MORE DETAILS

CITE

how to cite this article

Nega G, Sibhat M, Mekonnen A and Techane T. Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis [version 2; peer review: 1 approved]. F1000Research 2023, 11:1329 (https://doi.org/10.12688/f1000research.124586.2)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Version 2

VERSION 2

PUBLISHED 29 Sep 2023

Revised

Views

Reviewer Report 13 Oct 2023

Fadi Aljamaan, Critical Care Department, King Saud University, Riyadh, Riyadh Province, Saudi Arabia

Approved

https://doi.org/10.5256/f1000research.156672.r210988

CITE

Report a concern

Respond or Comment

Version 1

VERSION 1

PUBLISHED 16 Nov 2022

Views

Reviewer Report 14 Aug 2023

Fadi Aljamaan, Critical Care Department, King Saud University, Riyadh, Riyadh Province, Saudi Arabia

Approved with Reservations

https://doi.org/10.5256/f1000research.136793.r191979

Please remove the methodology and statistical details from the abstract.
Please use medical term that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation)

Please remove the methodology and statistical details from the abstract.
Please use medical term that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation)
There is no need to describe intubation procedure and mechanical ventilation in the introduction, that’s waste of attention of the audience.
“We have taken efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.
Would you please clarify how did you treat those who were weaned from MV and discharged to ward but died before discharge from hospital?
Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.
Please label P value as P value not significance.
What is the value of Table 3 compared to table 5 , you can merge them together please.
What is the importance of Table 4 in relation to your study of outcomes in mechanically ventilated patients with COVID-19??
Is table 5 bivariate or multivariate?
Please submit data about the patient who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Critical care

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 06 Oct 2023

Migbar Sibhat, College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia

06 Oct 2023

Author Response

Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit ... Continue reading Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit a revised version of our manuscript for consideration for publication. We appreciate the thoughtful and constructive comments of the reviewer and have revised our manuscript accordingly. Detailed responses to each of the comments or questions raised by the respected reviewer are presented below. We have indicated the text changes that have been made and the line number on the tracked changes manuscript at which our changes can be found.

Reviewer #1 Reflections:
1. Comment#1: “Please remove the methodology and statistical details from the abstract.”
Author’s response: We tried to shorten the methods section of the abstract part as much as possible. However, as per the journal’s guidelines, the abstract section should include a succinct description of the methods. Hence, we prefer to shorten it.

2. Comment#2: “Please use the medical terms that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation).”
Author’s response: Thank you for the suggestion. Mechanical ventilation can be applied in two different ways: invasively vs. non-invasively. Our study includes patients who received mechanical ventilation support with intubation (either via endotracheal intubation or tracheostomy) or without intubation (via CPAP or BiPAP). Patients who received respiratory support by intubation were termed as invasively ventilated whereas patients who received ventilatory support by providing positive pressure without intubation were referred to as non-invasively ventilated. This definition is internationally acceptable and is already presented in the introduction section of the main manuscript (Lines 24-28). Hence, “on mechanical ventilation” represents both “invasive” & non-invasive” ventilation, and thus, it could not be used as an exact replacement for “invasively ventilated”. Therefore, we prefer to keep it as it is.

3. Comment#3: “There is no need to describe the intubation procedure and mechanical ventilation in the introduction, that’s a waste of attention of the audience.”
Author’s response: Well! It could be simple to remove those statements. However, since the study population comprised of patients who received respiratory support by the mechanical ventilator (both invasively/intubated and non-invasively/without intubation), it’s essential to create a clear notion of mechanical ventilation, why, and how it’s applied so that readers could easily understand and interpret the study findings. Together with stating the problem, one of the purposes of the introduction section is to provide a general overview and familiarize them with the topic under investigation. Without knowing what mechanical ventilation is, the audience could not understand the study findings.

4. Comment#4: “We have made efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” Would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.”
Author’s response: This statement was mainly to minimize biases that could arise during data analysis. However, regarding the data collection, different techniques including pretest, data collector training, supervision, daily and on-spot cross-checking were applied. Besides, checklists with incomplete records were excluded to maintain data quality. It was already described in detail in the main manuscript file (please refer to Lines 100-108).

5. Comment#5: “Would you please clarify how you treat those who were weaned from MV and discharged to the ward but died before discharge from the hospital?”
Author’s response: Initially, at the proposal stage, we planned to include them in the study and count them as cases (only if readmitted to ICU since mechanical ventilation could not be provided in the wards in our set-up). Nevertheless, during data collection, we did not have such records and we removed this statement from the manuscript file. Be reminded that patients who died in the hospital (even in the ICU) without receiving mechanical ventilator support were not counted as events. Because all samples in the study should be exposed to mechanical ventilator support.

6. Comment#6: “Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.”
Author’s response: Well appreciated! We made the required changes in the main text. Findings for continuous variables were provided as supplementary files separately and cited accordingly (see Lines 145-150).

7. Comment#7: “Please label P value as P value not significance.”
Author’s response: Thanks again. We amended the issue as directed (Table 5).

8. Comment#8: “What is the value of Table 3 compared to Table 5, you can merge them please.”
Author’s Response: Table 3 is just a description table. It shows the distribution /crosstab output/ of management-related variables only. Conversely, Table 5 presents the regression analysis findings, and it contains variables included in the Cox model (which fulfilled the variable selection criteria). Table 5 consists of sociodemographic variables, comorbidities, mechanical ventilator parameters, and laboratory findings. Contrarily, Table 3 presents mechanical ventilator parameters, laboratory findings, and clinical characteristics. Therefore, the two tables are distinct, and thus, cannot be merged.

9. Comment#9: “What is the importance of Table 4 concerning your study of outcomes in mechanically ventilated patients with COVID-19?”
Author’s response: One of the study objectives was to determine the incidence of mortality among mechanically ventilated critically ill COVID-19 patients. Incidence was expressed in cumulative incidence at a certain time interval and overall incidence density rate. Hence, Table 4 presents incidence rates of mortality at specific time intervals, which was one of the study objectives. Thus, it’s essential to address one of the study objectives.

10. Comment#10: “Is table 5 bivariate or multivariate?”
Author’s Response: Table 5 displays variables that were included in the Cox regression model. In short, Table 5 is a multivariable output. It mainly presents multivariable outputs in which all variables in the final model were presented. Presenting only variables associated with the multivariable analysis could be inappropriate since variables that form the final model should be presented. Otherwise, one could not understand how many variables were incorporated for adjustment. We presented the crude estimates to make the table more informative and self-explanatory, such that readers will not question why and which variables were included in the final model for confounder adjustment. It provides better detail. To conclude, the table footnote shows crude (bivariable) and adjusted (multivariable) outputs. It should not be a question at all since every researcher understands that crude (CHR) implies before adjustment (bivariable) and adjusted (AHR) implies multivariable output.

11. Comment#11: “Please submit data about the patients who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself”
Author’s response: That’s great. All the patients included in the study were either severely or critically ill and needed mechanical ventilator support. From the beginning, patients with mild to moderate illnesses without respiratory distress and who did not require ventilatory support could not be incorporated in this study. Therefore, COVID-19 severity could not be considered as a covariate. To be included in the study, all patients should have severe respiratory distress requiring artificial ventilation by mechanical ventilation with or without intubation. Conversely, existing literature documented a huge variation in patients’ mortality rate with intubation and without intubation. During the worst times of the pandemic, clinicians were in the intubate vs. not-intubate dilemma despite the intubation criteria met (invasive vs. non-invasive ventilation dilemma). Besides, several literature reported that the mode and method of mechanical ventilation are essential for the success of ventilatory support. Since mechanical ventilation support was one exposure variable in our study, we assessed the method of MV as an exposure variable. Consequently, it’s mandatory to assess the effect of the method of MV itself on patient mortality.

We apologize if any of our responses seem offensive or if we used strong tones. If any exist, we want to declare that none of them were intentional! We just have full of appreciation and gratitude for the comments received so far! Thank you in advance.
Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit a revised version of our manuscript for consideration for publication. We appreciate the thoughtful and constructive comments of the reviewer and have revised our manuscript accordingly. Detailed responses to each of the comments or questions raised by the respected reviewer are presented below. We have indicated the text changes that have been made and the line number on the tracked changes manuscript at which our changes can be found.

Reviewer #1 Reflections:
1. Comment#1: “Please remove the methodology and statistical details from the abstract.”
Author’s response: We tried to shorten the methods section of the abstract part as much as possible. However, as per the journal’s guidelines, the abstract section should include a succinct description of the methods. Hence, we prefer to shorten it.

2. Comment#2: “Please use the medical terms that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation).”
Author’s response: Thank you for the suggestion. Mechanical ventilation can be applied in two different ways: invasively vs. non-invasively. Our study includes patients who received mechanical ventilation support with intubation (either via endotracheal intubation or tracheostomy) or without intubation (via CPAP or BiPAP). Patients who received respiratory support by intubation were termed as invasively ventilated whereas patients who received ventilatory support by providing positive pressure without intubation were referred to as non-invasively ventilated. This definition is internationally acceptable and is already presented in the introduction section of the main manuscript (Lines 24-28). Hence, “on mechanical ventilation” represents both “invasive” & non-invasive” ventilation, and thus, it could not be used as an exact replacement for “invasively ventilated”. Therefore, we prefer to keep it as it is.

3. Comment#3: “There is no need to describe the intubation procedure and mechanical ventilation in the introduction, that’s a waste of attention of the audience.”
Author’s response: Well! It could be simple to remove those statements. However, since the study population comprised of patients who received respiratory support by the mechanical ventilator (both invasively/intubated and non-invasively/without intubation), it’s essential to create a clear notion of mechanical ventilation, why, and how it’s applied so that readers could easily understand and interpret the study findings. Together with stating the problem, one of the purposes of the introduction section is to provide a general overview and familiarize them with the topic under investigation. Without knowing what mechanical ventilation is, the audience could not understand the study findings.

4. Comment#4: “We have made efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” Would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.”
Author’s response: This statement was mainly to minimize biases that could arise during data analysis. However, regarding the data collection, different techniques including pretest, data collector training, supervision, daily and on-spot cross-checking were applied. Besides, checklists with incomplete records were excluded to maintain data quality. It was already described in detail in the main manuscript file (please refer to Lines 100-108).

5. Comment#5: “Would you please clarify how you treat those who were weaned from MV and discharged to the ward but died before discharge from the hospital?”
Author’s response: Initially, at the proposal stage, we planned to include them in the study and count them as cases (only if readmitted to ICU since mechanical ventilation could not be provided in the wards in our set-up). Nevertheless, during data collection, we did not have such records and we removed this statement from the manuscript file. Be reminded that patients who died in the hospital (even in the ICU) without receiving mechanical ventilator support were not counted as events. Because all samples in the study should be exposed to mechanical ventilator support.

6. Comment#6: “Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.”
Author’s response: Well appreciated! We made the required changes in the main text. Findings for continuous variables were provided as supplementary files separately and cited accordingly (see Lines 145-150).

7. Comment#7: “Please label P value as P value not significance.”
Author’s response: Thanks again. We amended the issue as directed (Table 5).

8. Comment#8: “What is the value of Table 3 compared to Table 5, you can merge them please.”
Author’s Response: Table 3 is just a description table. It shows the distribution /crosstab output/ of management-related variables only. Conversely, Table 5 presents the regression analysis findings, and it contains variables included in the Cox model (which fulfilled the variable selection criteria). Table 5 consists of sociodemographic variables, comorbidities, mechanical ventilator parameters, and laboratory findings. Contrarily, Table 3 presents mechanical ventilator parameters, laboratory findings, and clinical characteristics. Therefore, the two tables are distinct, and thus, cannot be merged.

9. Comment#9: “What is the importance of Table 4 concerning your study of outcomes in mechanically ventilated patients with COVID-19?”
Author’s response: One of the study objectives was to determine the incidence of mortality among mechanically ventilated critically ill COVID-19 patients. Incidence was expressed in cumulative incidence at a certain time interval and overall incidence density rate. Hence, Table 4 presents incidence rates of mortality at specific time intervals, which was one of the study objectives. Thus, it’s essential to address one of the study objectives.

10. Comment#10: “Is table 5 bivariate or multivariate?”
Author’s Response: Table 5 displays variables that were included in the Cox regression model. In short, Table 5 is a multivariable output. It mainly presents multivariable outputs in which all variables in the final model were presented. Presenting only variables associated with the multivariable analysis could be inappropriate since variables that form the final model should be presented. Otherwise, one could not understand how many variables were incorporated for adjustment. We presented the crude estimates to make the table more informative and self-explanatory, such that readers will not question why and which variables were included in the final model for confounder adjustment. It provides better detail. To conclude, the table footnote shows crude (bivariable) and adjusted (multivariable) outputs. It should not be a question at all since every researcher understands that crude (CHR) implies before adjustment (bivariable) and adjusted (AHR) implies multivariable output.

11. Comment#11: “Please submit data about the patients who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself”
Author’s response: That’s great. All the patients included in the study were either severely or critically ill and needed mechanical ventilator support. From the beginning, patients with mild to moderate illnesses without respiratory distress and who did not require ventilatory support could not be incorporated in this study. Therefore, COVID-19 severity could not be considered as a covariate. To be included in the study, all patients should have severe respiratory distress requiring artificial ventilation by mechanical ventilation with or without intubation. Conversely, existing literature documented a huge variation in patients’ mortality rate with intubation and without intubation. During the worst times of the pandemic, clinicians were in the intubate vs. not-intubate dilemma despite the intubation criteria met (invasive vs. non-invasive ventilation dilemma). Besides, several literature reported that the mode and method of mechanical ventilation are essential for the success of ventilatory support. Since mechanical ventilation support was one exposure variable in our study, we assessed the method of MV as an exposure variable. Consequently, it’s mandatory to assess the effect of the method of MV itself on patient mortality.

We apologize if any of our responses seem offensive or if we used strong tones. If any exist, we want to declare that none of them were intentional! We just have full of appreciation and gratitude for the comments received so far! Thank you in advance.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 06 Oct 2023

Migbar Sibhat, College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia

06 Oct 2023

Author Response

Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit ... Continue reading Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit a revised version of our manuscript for consideration for publication. We appreciate the thoughtful and constructive comments of the reviewer and have revised our manuscript accordingly. Detailed responses to each of the comments or questions raised by the respected reviewer are presented below. We have indicated the text changes that have been made and the line number on the tracked changes manuscript at which our changes can be found.

Reviewer #1 Reflections:
1. Comment#1: “Please remove the methodology and statistical details from the abstract.”
Author’s response: We tried to shorten the methods section of the abstract part as much as possible. However, as per the journal’s guidelines, the abstract section should include a succinct description of the methods. Hence, we prefer to shorten it.

2. Comment#2: “Please use the medical terms that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation).”
Author’s response: Thank you for the suggestion. Mechanical ventilation can be applied in two different ways: invasively vs. non-invasively. Our study includes patients who received mechanical ventilation support with intubation (either via endotracheal intubation or tracheostomy) or without intubation (via CPAP or BiPAP). Patients who received respiratory support by intubation were termed as invasively ventilated whereas patients who received ventilatory support by providing positive pressure without intubation were referred to as non-invasively ventilated. This definition is internationally acceptable and is already presented in the introduction section of the main manuscript (Lines 24-28). Hence, “on mechanical ventilation” represents both “invasive” & non-invasive” ventilation, and thus, it could not be used as an exact replacement for “invasively ventilated”. Therefore, we prefer to keep it as it is.

3. Comment#3: “There is no need to describe the intubation procedure and mechanical ventilation in the introduction, that’s a waste of attention of the audience.”
Author’s response: Well! It could be simple to remove those statements. However, since the study population comprised of patients who received respiratory support by the mechanical ventilator (both invasively/intubated and non-invasively/without intubation), it’s essential to create a clear notion of mechanical ventilation, why, and how it’s applied so that readers could easily understand and interpret the study findings. Together with stating the problem, one of the purposes of the introduction section is to provide a general overview and familiarize them with the topic under investigation. Without knowing what mechanical ventilation is, the audience could not understand the study findings.

4. Comment#4: “We have made efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” Would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.”
Author’s response: This statement was mainly to minimize biases that could arise during data analysis. However, regarding the data collection, different techniques including pretest, data collector training, supervision, daily and on-spot cross-checking were applied. Besides, checklists with incomplete records were excluded to maintain data quality. It was already described in detail in the main manuscript file (please refer to Lines 100-108).

5. Comment#5: “Would you please clarify how you treat those who were weaned from MV and discharged to the ward but died before discharge from the hospital?”
Author’s response: Initially, at the proposal stage, we planned to include them in the study and count them as cases (only if readmitted to ICU since mechanical ventilation could not be provided in the wards in our set-up). Nevertheless, during data collection, we did not have such records and we removed this statement from the manuscript file. Be reminded that patients who died in the hospital (even in the ICU) without receiving mechanical ventilator support were not counted as events. Because all samples in the study should be exposed to mechanical ventilator support.

6. Comment#6: “Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.”
Author’s response: Well appreciated! We made the required changes in the main text. Findings for continuous variables were provided as supplementary files separately and cited accordingly (see Lines 145-150).

7. Comment#7: “Please label P value as P value not significance.”
Author’s response: Thanks again. We amended the issue as directed (Table 5).

8. Comment#8: “What is the value of Table 3 compared to Table 5, you can merge them please.”
Author’s Response: Table 3 is just a description table. It shows the distribution /crosstab output/ of management-related variables only. Conversely, Table 5 presents the regression analysis findings, and it contains variables included in the Cox model (which fulfilled the variable selection criteria). Table 5 consists of sociodemographic variables, comorbidities, mechanical ventilator parameters, and laboratory findings. Contrarily, Table 3 presents mechanical ventilator parameters, laboratory findings, and clinical characteristics. Therefore, the two tables are distinct, and thus, cannot be merged.

9. Comment#9: “What is the importance of Table 4 concerning your study of outcomes in mechanically ventilated patients with COVID-19?”
Author’s response: One of the study objectives was to determine the incidence of mortality among mechanically ventilated critically ill COVID-19 patients. Incidence was expressed in cumulative incidence at a certain time interval and overall incidence density rate. Hence, Table 4 presents incidence rates of mortality at specific time intervals, which was one of the study objectives. Thus, it’s essential to address one of the study objectives.

10. Comment#10: “Is table 5 bivariate or multivariate?”
Author’s Response: Table 5 displays variables that were included in the Cox regression model. In short, Table 5 is a multivariable output. It mainly presents multivariable outputs in which all variables in the final model were presented. Presenting only variables associated with the multivariable analysis could be inappropriate since variables that form the final model should be presented. Otherwise, one could not understand how many variables were incorporated for adjustment. We presented the crude estimates to make the table more informative and self-explanatory, such that readers will not question why and which variables were included in the final model for confounder adjustment. It provides better detail. To conclude, the table footnote shows crude (bivariable) and adjusted (multivariable) outputs. It should not be a question at all since every researcher understands that crude (CHR) implies before adjustment (bivariable) and adjusted (AHR) implies multivariable output.

11. Comment#11: “Please submit data about the patients who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself”
Author’s response: That’s great. All the patients included in the study were either severely or critically ill and needed mechanical ventilator support. From the beginning, patients with mild to moderate illnesses without respiratory distress and who did not require ventilatory support could not be incorporated in this study. Therefore, COVID-19 severity could not be considered as a covariate. To be included in the study, all patients should have severe respiratory distress requiring artificial ventilation by mechanical ventilation with or without intubation. Conversely, existing literature documented a huge variation in patients’ mortality rate with intubation and without intubation. During the worst times of the pandemic, clinicians were in the intubate vs. not-intubate dilemma despite the intubation criteria met (invasive vs. non-invasive ventilation dilemma). Besides, several literature reported that the mode and method of mechanical ventilation are essential for the success of ventilatory support. Since mechanical ventilation support was one exposure variable in our study, we assessed the method of MV as an exposure variable. Consequently, it’s mandatory to assess the effect of the method of MV itself on patient mortality.

We apologize if any of our responses seem offensive or if we used strong tones. If any exist, we want to declare that none of them were intentional! We just have full of appreciation and gratitude for the comments received so far! Thank you in advance.
Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit a revised version of our manuscript for consideration for publication. We appreciate the thoughtful and constructive comments of the reviewer and have revised our manuscript accordingly. Detailed responses to each of the comments or questions raised by the respected reviewer are presented below. We have indicated the text changes that have been made and the line number on the tracked changes manuscript at which our changes can be found.

Reviewer #1 Reflections:
1. Comment#1: “Please remove the methodology and statistical details from the abstract.”
Author’s response: We tried to shorten the methods section of the abstract part as much as possible. However, as per the journal’s guidelines, the abstract section should include a succinct description of the methods. Hence, we prefer to shorten it.

2. Comment#2: “Please use the medical terms that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation).”
Author’s response: Thank you for the suggestion. Mechanical ventilation can be applied in two different ways: invasively vs. non-invasively. Our study includes patients who received mechanical ventilation support with intubation (either via endotracheal intubation or tracheostomy) or without intubation (via CPAP or BiPAP). Patients who received respiratory support by intubation were termed as invasively ventilated whereas patients who received ventilatory support by providing positive pressure without intubation were referred to as non-invasively ventilated. This definition is internationally acceptable and is already presented in the introduction section of the main manuscript (Lines 24-28). Hence, “on mechanical ventilation” represents both “invasive” & non-invasive” ventilation, and thus, it could not be used as an exact replacement for “invasively ventilated”. Therefore, we prefer to keep it as it is.

3. Comment#3: “There is no need to describe the intubation procedure and mechanical ventilation in the introduction, that’s a waste of attention of the audience.”
Author’s response: Well! It could be simple to remove those statements. However, since the study population comprised of patients who received respiratory support by the mechanical ventilator (both invasively/intubated and non-invasively/without intubation), it’s essential to create a clear notion of mechanical ventilation, why, and how it’s applied so that readers could easily understand and interpret the study findings. Together with stating the problem, one of the purposes of the introduction section is to provide a general overview and familiarize them with the topic under investigation. Without knowing what mechanical ventilation is, the audience could not understand the study findings.

4. Comment#4: “We have made efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” Would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.”
Author’s response: This statement was mainly to minimize biases that could arise during data analysis. However, regarding the data collection, different techniques including pretest, data collector training, supervision, daily and on-spot cross-checking were applied. Besides, checklists with incomplete records were excluded to maintain data quality. It was already described in detail in the main manuscript file (please refer to Lines 100-108).

5. Comment#5: “Would you please clarify how you treat those who were weaned from MV and discharged to the ward but died before discharge from the hospital?”
Author’s response: Initially, at the proposal stage, we planned to include them in the study and count them as cases (only if readmitted to ICU since mechanical ventilation could not be provided in the wards in our set-up). Nevertheless, during data collection, we did not have such records and we removed this statement from the manuscript file. Be reminded that patients who died in the hospital (even in the ICU) without receiving mechanical ventilator support were not counted as events. Because all samples in the study should be exposed to mechanical ventilator support.

6. Comment#6: “Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.”
Author’s response: Well appreciated! We made the required changes in the main text. Findings for continuous variables were provided as supplementary files separately and cited accordingly (see Lines 145-150).

7. Comment#7: “Please label P value as P value not significance.”
Author’s response: Thanks again. We amended the issue as directed (Table 5).

8. Comment#8: “What is the value of Table 3 compared to Table 5, you can merge them please.”
Author’s Response: Table 3 is just a description table. It shows the distribution /crosstab output/ of management-related variables only. Conversely, Table 5 presents the regression analysis findings, and it contains variables included in the Cox model (which fulfilled the variable selection criteria). Table 5 consists of sociodemographic variables, comorbidities, mechanical ventilator parameters, and laboratory findings. Contrarily, Table 3 presents mechanical ventilator parameters, laboratory findings, and clinical characteristics. Therefore, the two tables are distinct, and thus, cannot be merged.

9. Comment#9: “What is the importance of Table 4 concerning your study of outcomes in mechanically ventilated patients with COVID-19?”
Author’s response: One of the study objectives was to determine the incidence of mortality among mechanically ventilated critically ill COVID-19 patients. Incidence was expressed in cumulative incidence at a certain time interval and overall incidence density rate. Hence, Table 4 presents incidence rates of mortality at specific time intervals, which was one of the study objectives. Thus, it’s essential to address one of the study objectives.

10. Comment#10: “Is table 5 bivariate or multivariate?”
Author’s Response: Table 5 displays variables that were included in the Cox regression model. In short, Table 5 is a multivariable output. It mainly presents multivariable outputs in which all variables in the final model were presented. Presenting only variables associated with the multivariable analysis could be inappropriate since variables that form the final model should be presented. Otherwise, one could not understand how many variables were incorporated for adjustment. We presented the crude estimates to make the table more informative and self-explanatory, such that readers will not question why and which variables were included in the final model for confounder adjustment. It provides better detail. To conclude, the table footnote shows crude (bivariable) and adjusted (multivariable) outputs. It should not be a question at all since every researcher understands that crude (CHR) implies before adjustment (bivariable) and adjusted (AHR) implies multivariable output.

11. Comment#11: “Please submit data about the patients who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself”
Author’s response: That’s great. All the patients included in the study were either severely or critically ill and needed mechanical ventilator support. From the beginning, patients with mild to moderate illnesses without respiratory distress and who did not require ventilatory support could not be incorporated in this study. Therefore, COVID-19 severity could not be considered as a covariate. To be included in the study, all patients should have severe respiratory distress requiring artificial ventilation by mechanical ventilation with or without intubation. Conversely, existing literature documented a huge variation in patients’ mortality rate with intubation and without intubation. During the worst times of the pandemic, clinicians were in the intubate vs. not-intubate dilemma despite the intubation criteria met (invasive vs. non-invasive ventilation dilemma). Besides, several literature reported that the mode and method of mechanical ventilation are essential for the success of ventilatory support. Since mechanical ventilation support was one exposure variable in our study, we assessed the method of MV as an exposure variable. Consequently, it’s mandatory to assess the effect of the method of MV itself on patient mortality.

We apologize if any of our responses seem offensive or if we used strong tones. If any exist, we want to declare that none of them were intentional! We just have full of appreciation and gratitude for the comments received so far! Thank you in advance.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 16 Nov 2022

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1
Version 2 (revision) 29 Sep 23	read
Version 1 16 Nov 22	read

Fadi Aljamaan, King Saud University, Riyadh, Saudi Arabia

Comments on this article

All Comments(0)

Add a comment

Browse by related subjects

Back to all reports

Reviewer Report

6 Views

13 Oct 2023 | for Version 2

Fadi Aljamaan, Critical Care Department, King Saud University, Riyadh, Riyadh Province, Saudi Arabia

6 Views Cite this report Responses(0)

Approved

Dear authors

You did extensive excellent work, please pay attention to few minor comments.

Table 2 add percentages for all variables (check age and sex)

Table 5 is actually bivariate, where is the multivariate, also I suggest to omit some non clinically important variables like PEEP, PIP and albumin as it only cause noise.

For Comorbidities in Table 5 what do you mean , also you analyze for HTN, DM, COPD, CKD ?

Competing Interests

No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

14 Views

14 Aug 2023 | for Version 1

Fadi Aljamaan, Critical Care Department, King Saud University, Riyadh, Riyadh Province, Saudi Arabia

14 Views Cite this report Responses(1)

Approved With Reservations

Please remove the methodology and statistical details from the abstract.
Please use medical term that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation)
There is no need to describe intubation procedure and mechanical ventilation in the introduction, that’s waste of attention of the audience.
“We have taken efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.
Would you please clarify how did you treat those who were weaned from MV and discharged to ward but died before discharge from hospital?
Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.
Please label P value as P value not significance.
What is the value of Table 3 compared to table 5 , you can merge them together please.
What is the importance of Table 4 in relation to your study of outcomes in mechanically ventilated patients with COVID-19??
Is table 5 bivariate or multivariate?
Please submit data about the patient who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Critical care

Respond to this report

Responses (1)

Author Response

06 Oct 2023

Migbar Sibhat, College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia

Manuscript title: Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis
Thank you for the opportunity to submit a revised version of our manuscript for consideration for publication. We appreciate the thoughtful and constructive comments of the reviewer and have revised our manuscript accordingly. Detailed responses to each of the comments or questions raised by the respected reviewer are presented below. We have indicated the text changes that have been made and the line number on the tracked changes manuscript at which our changes can be found.

Reviewer #1 Reflections:
1. Comment#1: “Please remove the methodology and statistical details from the abstract.”
Author’s response: We tried to shorten the methods section of the abstract part as much as possible. However, as per the journal’s guidelines, the abstract section should include a succinct description of the methods. Hence, we prefer to shorten it.

2. Comment#2: “Please use the medical terms that are more professional and international, for example (invasively ventilated) should use (on mechanical ventilation).”
Author’s response: Thank you for the suggestion. Mechanical ventilation can be applied in two different ways: invasively vs. non-invasively. Our study includes patients who received mechanical ventilation support with intubation (either via endotracheal intubation or tracheostomy) or without intubation (via CPAP or BiPAP). Patients who received respiratory support by intubation were termed as invasively ventilated whereas patients who received ventilatory support by providing positive pressure without intubation were referred to as non-invasively ventilated. This definition is internationally acceptable and is already presented in the introduction section of the main manuscript (Lines 24-28). Hence, “on mechanical ventilation” represents both “invasive” & non-invasive” ventilation, and thus, it could not be used as an exact replacement for “invasively ventilated”. Therefore, we prefer to keep it as it is.

3. Comment#3: “There is no need to describe the intubation procedure and mechanical ventilation in the introduction, that’s a waste of attention of the audience.”
Author’s response: Well! It could be simple to remove those statements. However, since the study population comprised of patients who received respiratory support by the mechanical ventilator (both invasively/intubated and non-invasively/without intubation), it’s essential to create a clear notion of mechanical ventilation, why, and how it’s applied so that readers could easily understand and interpret the study findings. Together with stating the problem, one of the purposes of the introduction section is to provide a general overview and familiarize them with the topic under investigation. Without knowing what mechanical ventilation is, the audience could not understand the study findings.

4. Comment#4: “We have made efforts to minimize potential sources of bias by setting and following thoroughly predetermined research plans, performing analysis by different team members independently, and receiving critics and reviews from external peers” Would you please clarify this sentence, as I don’t understand where were the potential sources of bias exactly especially in data collection.”
Author’s response: This statement was mainly to minimize biases that could arise during data analysis. However, regarding the data collection, different techniques including pretest, data collector training, supervision, daily and on-spot cross-checking were applied. Besides, checklists with incomplete records were excluded to maintain data quality. It was already described in detail in the main manuscript file (please refer to Lines 100-108).

5. Comment#5: “Would you please clarify how you treat those who were weaned from MV and discharged to the ward but died before discharge from the hospital?”
Author’s response: Initially, at the proposal stage, we planned to include them in the study and count them as cases (only if readmitted to ICU since mechanical ventilation could not be provided in the wards in our set-up). Nevertheless, during data collection, we did not have such records and we removed this statement from the manuscript file. Be reminded that patients who died in the hospital (even in the ICU) without receiving mechanical ventilator support were not counted as events. Because all samples in the study should be exposed to mechanical ventilator support.

6. Comment#6: “Please refer to the appropriate table in the results section. Table 1 doesn’t have the results texted in the first paragraph of the results section.”
Author’s response: Well appreciated! We made the required changes in the main text. Findings for continuous variables were provided as supplementary files separately and cited accordingly (see Lines 145-150).

7. Comment#7: “Please label P value as P value not significance.”
Author’s response: Thanks again. We amended the issue as directed (Table 5).

8. Comment#8: “What is the value of Table 3 compared to Table 5, you can merge them please.”
Author’s Response: Table 3 is just a description table. It shows the distribution /crosstab output/ of management-related variables only. Conversely, Table 5 presents the regression analysis findings, and it contains variables included in the Cox model (which fulfilled the variable selection criteria). Table 5 consists of sociodemographic variables, comorbidities, mechanical ventilator parameters, and laboratory findings. Contrarily, Table 3 presents mechanical ventilator parameters, laboratory findings, and clinical characteristics. Therefore, the two tables are distinct, and thus, cannot be merged.

9. Comment#9: “What is the importance of Table 4 concerning your study of outcomes in mechanically ventilated patients with COVID-19?”
Author’s response: One of the study objectives was to determine the incidence of mortality among mechanically ventilated critically ill COVID-19 patients. Incidence was expressed in cumulative incidence at a certain time interval and overall incidence density rate. Hence, Table 4 presents incidence rates of mortality at specific time intervals, which was one of the study objectives. Thus, it’s essential to address one of the study objectives.

10. Comment#10: “Is table 5 bivariate or multivariate?”
Author’s Response: Table 5 displays variables that were included in the Cox regression model. In short, Table 5 is a multivariable output. It mainly presents multivariable outputs in which all variables in the final model were presented. Presenting only variables associated with the multivariable analysis could be inappropriate since variables that form the final model should be presented. Otherwise, one could not understand how many variables were incorporated for adjustment. We presented the crude estimates to make the table more informative and self-explanatory, such that readers will not question why and which variables were included in the final model for confounder adjustment. It provides better detail. To conclude, the table footnote shows crude (bivariable) and adjusted (multivariable) outputs. It should not be a question at all since every researcher understands that crude (CHR) implies before adjustment (bivariable) and adjusted (AHR) implies multivariable output.

11. Comment#11: “Please submit data about the patients who were mechanically ventilated, whether their disease was more severe and affected their mortality rather than the MV itself”
Author’s response: That’s great. All the patients included in the study were either severely or critically ill and needed mechanical ventilator support. From the beginning, patients with mild to moderate illnesses without respiratory distress and who did not require ventilatory support could not be incorporated in this study. Therefore, COVID-19 severity could not be considered as a covariate. To be included in the study, all patients should have severe respiratory distress requiring artificial ventilation by mechanical ventilation with or without intubation. Conversely, existing literature documented a huge variation in patients’ mortality rate with intubation and without intubation. During the worst times of the pandemic, clinicians were in the intubate vs. not-intubate dilemma despite the intubation criteria met (invasive vs. non-invasive ventilation dilemma). Besides, several literature reported that the mode and method of mechanical ventilation are essential for the success of ventilatory support. Since mechanical ventilation support was one exposure variable in our study, we assessed the method of MV as an exposure variable. Consequently, it’s mandatory to assess the effect of the method of MV itself on patient mortality.

We apologize if any of our responses seem offensive or if we used strong tones. If any exist, we want to declare that none of them were intentional! We just have full of appreciation and gratitude for the comments received so far! Thank you in advance.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. Novel Coronavirus Pneumonia Emergency Response Epidemiology: The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua liu xing bing xue za zhi= Zhonghua liuxingbingxue zazhi. 2020; 41(2): 145.

[2] 2. Berlin DA, Gulick RM, Martinez FJ: Severe covid-19. N. Engl. J. Med. 2020; 383(25): 2451–2460. Publisher Full Text

[3] 3. Winck J, Scala R: Non-invasive respiratory support paths in hospitalized patients with COVID-19: Proposal of an algorithm. Pulmonology. 2021; 27: 305–312. PubMed Abstract | Publisher Full Text

[4] 4. Wiersinga WJ, Rhodes A, Cheng AC, et al.: Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA. 2020; 324(8): 782–793. Publisher Full Text

[5] 5. Costa IBSS, Bittar CS, Rizk SI, et al.: The heart and COVID-19: what cardiologists need to know. Arq. Bras. Cardiol. 2020.

[6] 6. Fraissé M, Logre E, Pajot O, et al.: Thrombotic and hemorrhagic events in critically ill COVID-19 patients: a French monocenter retrospective study. Crit. Care. 2020; 24(1): 1–4.

[7] 7. Joseph A, Zafrani L, Mabrouki A, et al.: Acute kidney injury in patients with SARS-CoV-2 infection. Ann. Intensive Care. 2020; 10(1): 1–8.

[8] 8. Wu Z, McGoogan JM: Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020; 323(13): 1239–1242.

[9] 9. Audit: ICNARC report on COVID-19 in critical care.April. 2020; 10: 2020.

[10] 10. Grasselli G, Zangrillo A, Zanella A, et al.: Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy. JAMA. 2020; 323(16): 1574–1581. Publisher Full Text

[11] 11. Brochard L: Mechanical ventilation: invasive versus noninvasive. Eur. Respir. J. 2003; 22(47 suppl): 31s–37s. Publisher Full Text

[12] 12. Wilson ME, Dobler CC, Morrow AS, et al.: Association of home noninvasive positive pressure ventilation with clinical outcomes in chronic obstructive pulmonary disease: a systematic review and meta-analysis. JAMA. 2020; 323(5): 455–465. PubMed Abstract | Publisher Full Text

[13] 13. de Terwangne C , Sorgente A, Tortora R, et al.: Mortality Rate and Predictors among Patients with COVID-19 Related Acute Respiratory Failure Requiring Mechanical Ventilation: A Retrospective Single Centre Study. J. Crit. Care Med. 2021; 7(1): 21–27. PubMed Abstract | Publisher Full Text

[14] 14. Phua J, Weng L, Ling L, et al.: Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir. Med. 2020; 8(5): 506–517. PubMed Abstract | Publisher Full Text

[15] 15. McGain F, Humphries RS, Lee JH, et al.: Aerosol generation related to respiratory interventions and the effectiveness of a personal ventilation hood. Crit. Care Resusc. 2020; 22(3): 212–220. PubMed Abstract

[16] 16. Wang Y, Lu X, Li Y, et al.: Clinical course and outcomes of 344 intensive care patients with COVID-19. Am. J. Respir. Crit. Care Med. 2020; 201(11): 1430–1434. PubMed Abstract | Publisher Full Text

[17] 17. Quah P, Li A, Phua J: Mortality rates of patients with COVID-19 in the intensive care unit: a systematic review of the emerging literature. Crit. Care. 2020; 24: 1–4.

[18] 18. Zhou F, Yu T, Du R, et al.: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229): 1054–1062. PubMed Abstract | Publisher Full Text

[19] 19. Rahim F, Amin S, Noor M, et al.: Mortality of Patients With Severe COVID-19 in the Intensive Care Unit: An Observational Study From a Major COVID-19 Receiving Hospital. Cureus. 2020; 12(10).

[20] 20. Daniel P, Mecklenburg M, Massiah C, et al.: Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support. Am. J. Emerg. Med. 2021; 43: 103–108. PubMed Abstract | Publisher Full Text

[21] 21. Hua J, Qian C, Luo Z, et al.: Invasive mechanical ventilation in COVID-19 patient management: the experience with 469 patients in Wuhan. Crit. Care. 2020; 24(1): 1–3.

[22] 22. Cascella M, Rajnik M, Aleem A, et al.: Features, evaluation, and treatment of coronavirus (COVID-19). StatPearls; 2021.

[23] 23. Gattinoni L, Chiumello D, Caironi P, et al.: COVID-19 pneumonia: different respiratory treatments for different phenotypes? Springer; 2020.

[24] 24. Chew SY, Lee YS, Ghimiray D, et al.: Characteristics and Outcomes of COVID-19 Patients with Respiratory Failure Admitted to a “Pandemic Ready” Intensive Care Unit-Lessons from Singapore. Ann. Acad. Med. Singap. 2020; 49(7): 434–448. PubMed Abstract | Publisher Full Text

[25] 25. Tessema GA, Kinfu Y, Dachew BA, et al.: The COVID-19 pandemic and healthcare systems in Africa: a scoping review of preparedness, impact and response. BMJ Glob. Health. 2021; 6(12): e007179. PubMed Abstract | Publisher Full Text

[26] 26. Sibhat M: STROBE checklist for ‘Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia’: figshare.2022. Publisher Full Text

[27] 27. World Health Organization: Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: interim guidance, 13 March 2020. World Health Organization; 2020.

[28] 28. National Institutes of Health: COVID-19 Treatment Guidelines. Oxygenation and ventilation. NIH; 2020.

[29] 29. Laake JH, Buanes EA, Småstuen MC, et al.: Characteristics, management and survival of ICU patients with coronavirus disease-19 in Norway, March-June 2020. A prospective observational study. Acta Anaesthesiol. Scand. 2021; 65(5): 618–628. PubMed Abstract | Publisher Full Text

[30] 30. Ayed M, Borahmah AA, Yazdani A, et al.: Assessment of clinical characteristics and mortality-associated factors in COVID-19 Critical cases in Kuwait. Med. Princ. Pract. 2020.

[31] 31. Alharthy A, Aletreby W, Faqihi F, et al.: Clinical characteristics and predictors of 28-day mortality in 352 critically ill patients with COVID-19: a retrospective study. J. Epidemiol. Glob. Health. 2021; 11(1): 98–104. PubMed Abstract | Publisher Full Text

[32] 32. Giannoglou D, Meimeti E, Provatopoulou X, et al.: Predictors of mortality in hospitalized COVID-19 patients in Athens, Greece. medRxiv.2020.

[33] 33. Oliveira E, Parikh A, Lopez-Ruiz A, et al.: ICU outcomes and survival in patients with severe COVID-19 in the largest health care system in central Florida. PLoS One. 2021; 16(3): e0249038. PubMed Abstract | Publisher Full Text

[34] 34. Kebede E, Yoseph E, Asaye H, et al.: Examining Healthcare Workers' Perspectives Concerning Medical Equipment Availability in Three Ethiopian Hospitals: A Qualitative Pilot Study. Cureus. 2021; 13(3): e14134-e. eng. PubMed Abstract | Publisher Full Text

[35] 35. Bepouka BI, Mandina M, Makulo JR, et al.: Predictors of mortality in COVID-19 patients at Kinshasa University Hospital, Democratic Republic of the Congo, from March to June 2020. Pan Afr. Med. J. 2020: 37.

[36] 36. Tehrani S, Killander A, Åstrand P, et al.: Risk factors for death in adult COVID-19 patients: frailty predicts fatal outcome in older patients. Int. J. Infect. Dis. 2021; 102: 415–421. PubMed Abstract | Publisher Full Text

[37] 37. Fried LP, Xue Q-L, Cappola AR, et al.: Nonlinear Multisystem Physiological Dysregulation Associated With Frailty in Older Women: Implications for Etiology and Treatment. J. Gerontol.: Series A. 2009; 64A(10): 1049–1057. PubMed Abstract | Publisher Full Text

[38] 38. Smits SL, De Lang A, Van Den Brand JM, et al.: Exacerbated innate host response to SARS-CoV in aged non-human primates. PLoS Pathog. 2010; 6(2): e1000756. PubMed Abstract | Publisher Full Text

[39] 39. Santos MM, Lucena EE, Lima KC, et al.: Survival and predictors of deaths of patients hospitalised due to COVID-19 from a retrospective and multicentre cohort study in Brazil. Epidemiol. Infect. 2020: 148.

[40] 40. Guo W, Li M, Dong Y, et al.: Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab. Res. Rev. 2020; 36(7): e3319.

[41] 41. Sivaloganathan AA, Nasim-Mohi M, Brown MM, et al.: Noninvasive ventilation for COVID-19-associated acute hypoxaemic respiratory failure: experience from a single centre. Br. J. Anaesth. 2020; 125(4): e368–e371. PubMed Abstract | Publisher Full Text

[42] 42. Tu Y, Yang P, Zhou Y, et al.: Risk factors for mortality of critically ill patients with COVID-19 receiving invasive ventilation. Int. J. Med. Sci. 2021; 18(5): 1198–1206. PubMed Abstract | Publisher Full Text

[43] 43. Haribhai S, Mahboobi SK: Ventilator Complications. StatPearls. Treasure Island FL: © 2022: StatPearls Publishing LLC; 2022.

[44] 44. Zhou L, Cai G, Xu Z, et al.: High positive end expiratory pressure levels affect hemodynamics in elderly patients with hypertension admitted to the intensive care unit: a prospective cohort study. BMC Pulm. Med. 2019; 19(1): 1–9.

[45] 45. Cournand A, Motley HL, Werko L, et al.: Physiological studies of the effects of intermittent positive pressure breathing on cardiac output in man. American Journal of Physiology-Legacy Content. 1947; 152(1): 162–174. PubMed Abstract | Publisher Full Text

[46] 46. Pinsky MR: The hemodynamic consequences of mechanical ventilation: an evolving story. Intensive Care Med. 1997; 23(5): 493–503. PubMed Abstract | Publisher Full Text

[47] 47. Sibhat M: DATASET for Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia. figshare. [Dataset]. 2022. Publisher Full Text

Survival status and predictors of mortality among mechanically ventilated COVID-19 patients in Addis Ababa COVID-19 Care Centers, Ethiopia: A survival analysis

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Population and participant recruitment methods

Figure 1. A flow diagram of sample recruitment methods among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Operationalization of terms and measurements

Data collection tools and procedure

Data processing, analysis, and presentation

Figure 2. Cox-Snell residual for the assessment of Cox proportional hazard model fitness among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Ethical consideration

Results

Description of socio-demographic characteristics of study participants

Table 1. Overall presentation of continuous variables using numeric measures among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Distribution of comorbidities and baseline investigation findings

Table 2. Distribution of age, sex and comorbidities among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Description of COVID-19 management and ventilator-related variables

Table 3. Distribution of management-related characteristics among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Comparison of survival status using Kaplan Meier curve

Figure 3. Overall Kaplan-Meier curve for mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Survival function and incidence rate of mortality

Table 4. Life table of mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Predictors of death among mechanically ventilated COVID-19 patients

Table 5. Bivariate and multivariable analysis output of Cox proportional hazard regression among mechanically ventilated COVID-19 patients at Addis Ababa COVID-19 Care Centers, Ethiopia, 2022 (N=496).

Discussion

Conclusions

Data availability

Underlying data

Reporting guidelines

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Browse by related subjects

Competing Interests Policy

Stay Updated