F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.75135.4 Research Article Articles Risk of lowering mortality from the improvement of inflammatory markers and disease progression among moderate, severe, and critical COVID-19 patients using anticoagulant : a cross-sectional study from two second referral hospitals in Surabaya, Indonesia

[version 4; peer review: 1 approved, 1 approved with reservations]

 Romadhon Pradana Zaky Conceptualization Data Curation Formal Analysis Investigation Methodology Resources Software Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-9306-5425 1 2 Bintoro Siprianus Ugroseno Yudho Conceptualization Data Curation Formal Analysis Investigation Methodology Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0003-1360-5298 a 2 Suryantoro Satriyo Dwi Conceptualization Data Curation Formal Analysis Investigation Methodology Resources Software Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-0522-8659 b 1 2 Asmarawati Tri Pudy Data Curation Formal Analysis Investigation Methodology Resources Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-9869-5098 1 2 Nur Rosyid Alfian Data Curation Formal Analysis Investigation Methodology Resources Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing 1 2 Savitri Merlyna Data Curation Formal Analysis Investigation Methodology Resources Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing 2 Amrita Putu Niken Ayu Data Curation Formal Analysis Investigation Methodology Resources Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0003-4837-1786 2 Diansyah Muhammad Noor Data Curation Formal Analysis Investigation Methodology Project Administration Resources Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing 1 2 Prayoga Ami Ashariati Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Resources Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing 1 2 Windradi Choirina Data Curation Formal Analysis Investigation Methodology Resources Validation Writing – Original Draft Preparation Writing – Review & Editing 1 Mahdi Bagus Aulia Conceptualization Data Curation Formal Analysis Investigation Methodology Resources Validation Writing – Original Draft Preparation https://orcid.org/0000-0001-6634-6088 2 Widiyastuti Krisnina Nurul Data Curation Investigation Methodology Resources Software Validation Writing – Original Draft Preparation 2 Novendrianto Dwiki Data Curation Investigation Methodology Resources Software Visualization Writing – Review & Editing https://orcid.org/0000-0003-2631-4609 2 Agustin Esthiningrum Dewi Data Curation Investigation Project Administration Resources Validation Writing – Review & Editing https://orcid.org/0000-0001-6972-1955 1 Alkaff Firas Farisi Data Curation Investigation Methodology Project Administration Resources Software Validation Visualization Writing – Original Draft Preparation https://orcid.org/0000-0002-5628-1345 3 Prahasanti Kartika Data Curation Formal Analysis Investigation Methodology Resources Software Visualization Writing – Original Draft Preparation 4 Dewanto Didi Darmahadi Formal Analysis Investigation Methodology Project Administration Supervision Validation Writing – Original Draft Preparation 5 Universitas Airlangga Hospital, Surabaya, East Java, 60115, Indonesia Faculty of Medicine, Airlangga University, Surabaya, East Jawa, 60132, Indonesia Department of Internal Medicine, Medical Centre University, Groningen, Groningen, 9713 GZ, Netherlands Antilles Faculty of Medicine, Muhammadiyah Surabaya University, Surabaya, East Java, 60115, Indonesia Husada Utama Hospital, Surabaya, East Java, 60131, Indonesia ugroseno@fk.unair.ac.id satriyo.dwi.suryantoro@fk.unair.ac.id

No competing interests were disclosed.

 28 3 2023 2022 11 61 27 3 2023 Copyright: © 2023 Romadhon PZ et al. 2023 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: To date, coronavirus diseases 2019 (COVID-19) has no definitive treatment. Thrombosis and hypercoagulation may occur in the advanced stage. Further study on how to use anticoagulants is still required to promote the best prognosis.

Methods: A cross-sectional study of 110 moderate, 140 severe, and 81 critical patients receiving unfractioned heparin (UFH), low-molecular-weight heparin (LMWH), and fondaparinux was conducted. Data were collected from March 15 th to August 31 st 2020 at Universitas Airlangga and Husada Utama Hospital. A comparative study of white blood cell (WBC), neutrophils, lymphocytes, neutrophil-lymphocyte ratio (NLR), c-reactive protein (CRP), procalcitonin (PCT), D-dimer, all-cause mortality rate, length of stay, and days of death among three severities of COVID-19 was done. Univariate and multivariate analysis were used to determine the correlation between inflammatory state after anti-coagulant with patients’ mortality.

Results: Two deaths occurred in moderate cases, 36 deaths in severe cases, and 70 deaths in critical cases on ventilators. On day 13, moderate and severe groups showed decreased WBC, neutrophils, NLR, CRP, and D-dimer (p < 0.05). NLR, CRP, and D-dimer (p<0.05) in critically ill and ventilated patients decreased. Day-13 evaluation revealed 32.73% decrease of inflammatory markers in moderate group; 32.86% in severe patients; and 16.05% in critically ill, ventilated patients. A significant correlation between day 13 inflammatory status with mortality was seen in moderate and critical cases with a ventilator (r=0.337; p< 0.05 and r=0.25; p 0.05). Inflammatory profile on day 6 (adjusted odds ratio [aOR] = 2.36; p < 0.05) and day 13 ([aOR] = 4.15; p < 0.05) was associated with patients’ mortality.

Conclusions: Anticoagulants in COVID-19 patients lower inflammation markers. Evaluating inflammatory status is essential to predict the mortality. Inflammatory markers on day 13, based on the severity of COVID-19 and comorbidities, were associated with mortality in moderate and critical cases.

 COVID-19 Anti-coagulant mortality length of stay severity health The author(s) declared that no grants were involved in supporting this work. Amendments from Version 3

We revised the number of ethical letters because it turned out that we wrote it incorrectly in the previous version.

 Introduction

Coronavirus disease 2019 (COVID-19) infection has caused widespread novel COVID-19 pneumonia, causing respiratory problems. 1 The World Health Organization (WHO) declared this as a global pandemic affecting many aspects of life. 2 A severe degree of COVID-19 pneumonia is defined as a condition in patients complaining of difficulty in breathing, plus one of: respiratory rate >30 times per minute; severe respiratory distress; or oxygen saturation (SpO 2) <93% in room air or a PaO 2/FiO 2 ratio (PF Ratio) <300. In children, it is defined as having a cough or difficulty breathing, plus at least one of: central cyanosis or SpO 2 <90%; severe respiratory distress (such as snoring, heavy chest wall traction); signs of severe pneumonia, namely inability to suckle/drink; lethargy or decreased consciousness, or seizures. Other signs of pneumonia are chest retractions, rapid breathing >60×/minute in children aged <2 months, >50×/minute in children aged 2-11 months, >40×/minute in children aged 1-5 years, or >30×/minute in children aged >5 years. 2

Severe SARS-CoV2 infection promotes a syndrome related to prothrombotic conditions, in which blood clots easily. This condition is characterized by several specific abnormal laboratory values, such as mild thrombocytopenia, increased fibrin, degradation of fibrin products, fibrinogen, and D-dimers. Increased D-dimers are strongly correlated with worsened clinical conditions and increased risk of death in COVID-19. 3 , 5 Based on reports from several studies, there is a growing incidence of several thromboembolic states in patients with COVID-19 admitted to the intensive care unit (ICU), one of which is pulmonary embolism. COVID-19 patients suspected of experiencing a thromboembolic event should be given anticoagulant therapy when radiological imaging is difficult to perform. 3 – 5

The pathophysiology of hypercoagulation in COVID-19 patients is still currently being explored. A case series presenting three cases of COVID-19 with antiphospholipid syndrome has recently been reported by The New England Journal of Medicine. 5 SARS-CoV-2 infection is associated with antiphospholipid antibodies, which predispose to hypercoagulation. The study of Campbell et al. reported severe COVID-19 patients with increased levels of lactate dehyrdrogenase (LDH), D-Dimer, bilirubin, decreased platelets, mild anemia, heart and kidney injury, and diffused thrombotic micro-angiopathy. 5 – 7

Patients with severe COVID-19 experience complications of coagulopathy in the form of disseminated intravascular coagulation (DIC) which might result in death. Severe COVID-19 patients experience respiratory problems and increased virulence, according to the criteria of the Third International Consensus Definitions for Sepsis (Sepsis-3). Severe COVID-19 patients are also at risk of venous thromboembolism (VTE) due to prolonged bed rest. The International Society of Thrombosis and Haemostasis (ISTH) provides a new category to identify the early phase of sepsis-associated DIC which is also called sepsis-induced coagulopathy (SIC). COVID-19 patients who fit the diagnostic criteria for SIC can be also given anticoagulant therapy. 5 – 10

Anticoagulant therapy in COVID-19 patients is administered to those who show signs of thrombosis, such as elevated inflammatory factors and D-dimers within 7-14 days, with threefold D-Dimer value. The option would be to use low molecular weight heparin (LMWH) at a dose of 100 IU/ kg twice a day, for 3-5 days. The European Society of Cardiology also includes anticoagulants in the COVID-19 therapy algorithm. For patients admitted to the ICU, the parenteral heparin drip protocol must be strictly controlled and the time value of the active prothromblastin time is 60-85 seconds. For non-ICU patients, the subcutaneous dose of enoxaprin is started at 1mg/kg twice daily. After all the completed researches, further study on how to use anticoagulants is still being analysed to promote the best prognosis. 3 , 11 , 12

Methods Ethics approval

Approval was issued by the research ethics board Universitas Airlangga Hospital (No: 166/KEP/2020) on 2 nd October 2020. The consent to participate was not applicable since our data were obtained from medical records.

 Study design and population

We performed a retrospective cohort study with consecutive sampling among COVID-19 adult patients admitted to two referral hospitals for COVID-19, Universitas Airlangga Hospital (UAH) and Husada Utama Hospital, Surabaya, East Java, Indonesia, from March 15 th 2020 to August 31 st 2020. The patients included were ones in moderate, severe, or critical condition. We assessed patients referring to WHO guidelines and Indonesian Ministry of Health guidelines, and tested for SARS-CoV-2 Polymerase Chain Reaction (PCR) through oropharyngeal and nasopharyngeal swabs. Sequential chest x-ray and laboratory inflammatory marker evaluation were performed, then we did three timeframes of assessment. The first evaluation was done at the admission, followed by a second evaluation on the sixth day, and lastly evaluated before discharge or death (mean time 13 days).

 Data collection

In October 2020, we collected the data from medical records of those admitted to UAH and Husada Utama hospital between March 15 th, 2020 to August 31 st, 2020. We had 331 patients with moderate to critically ill COVID-19. Incomplete medical records were excluded. From this selection, we organized patient records into 110 moderate cases, 140 severe cases, and 81 critically ill cases with ventilator (see Figure 1).

Selection of patients (CRP: c-reactive protein).

Patients were categorized as a moderate case if they had either or both of 1) signs of pneumonia; 2) O 2 sat ≥93% free air. Meanwhile, patients were classified into severe cases if there were clinical signs of pneumonia, with one of the following: 1) respiratory rate (RR) >30 times per minutes; 2) severe respiratory distress; 3) oxygen saturation < 93% free air. They were grouped into critically ill if they suffered from acute respiratory distress syndrome (ARDS), sepsis, and septic shock. 2

The criteria of inflammatory markers were: 1) white blood cell (WBC) >6.16x10 3 cells/μL; 13 2) neutrophil-lymphocyte ratio (NLR) >6.5; 14 3) absolute lymphocyte count ALC < 1.0x10 3 cells/μL; 15 4) c-reactive protein (CRP) >41.8 mg/L; 16 and 5) Procalcitonin > 0,07 ng/mL. 16

Statistical analysis

We examined the data using SPSS version 24.0 (Chicago, IL, USA). Descriptive analysis incorporated categorical variables reported as number (percentage) and continuous variables as mean (standard deviation). We displayed categorical variables as number (%) and continuous variables as mean (standard deviation) or median (range), depending on whether the data are normally distributed. Means of Chi square or McNemar was used to assess statistical significance for dichotomous variables, while paired t-test or Wilcoxon test were used to examine continuous variables, depending on the data distribution. We compared the mean/median difference of the first to the second evaluation and the second to the third evaluations of the laboratory results, length of stay, days of death, and mortality rate according to severity of COVID-19 using ANOVA or Kruskal Wallis. Bivariate analysis between inflammatory state and mortality was conducted using Spearman. We also determined the relationship between inflammatory state after anti-coagulant (UFH vs non-UFH) with patients’ mortality, along with some variables, which were age, gender, disease severity, and comorbidity.

 Results Baseline data

Of 331 patients enrolled, 200 were male and 131 were female. Patients were grouped based on the category of severity of disease and resulted in 110 moderate patients, 140 severe patients, and 81 critically ill patients with ventilator support. The average age of patients with severe COVID-19 was 57.45 ± 13.5 years. In the anticoagulant group, the most frequent comorbid factors found were diabetes mellitus (DM) (52.86%), hypertension (HT) (46.91%), and geriatric age (47.14%) while others had history of heart disease (5.45%), stroke (3.7%), and chronic kidney disease (1.82%). We evaluated inflammatory markers as baseline study data. There were significant differences in laboratory markers between each severity group (p value <0.05). The highest white blood count was 8.53 ± 5.41 × 10 3 /L, neutrophils 83.75 ± 9.51%, lymphocyte 17.1 ± 9.9%, lymphocyte absolute 1.1 ± 0.57 × 10 3 /L, neutrophil lymphocyte ratio (NLR) 8.38 ± 12.5, the C-reactive protein (CRP) 40.1 ± 42.43 mg/L and d-dimer 1.2 ± 8.53 mcg/L. The outcome analysis showed significant differences in the mean length of stay (p value <0.05) (see Table 1).

Characteristics of subjects (DM: diabetes mellitus; HT: hypertension).
Severity of COVID-19
Moderate Severe Critically ill with ventilator support Total
110 140 81 331
Sex
Male (n,%) 64 58,18 82 58,57 54 66,67 200
Female (n,%) 46 41,82 58 41,43 27 33,33 131
Age (mean/SD) 53,62 13,67 57,45 13,5 56,11 13,63 167,18
Comorbid
Geriatric age (age>60 years old) (n,%) 40 36,36 66 47,14 29 35,80 135
DM (n,%) 33 30,00 74 52,86 23 28,40 130
HT (n,%) 40 36,36 54 38,57 38 46,91 132
Heart Disease (n,%) 6 5,45 7 5,00 4 4,94 17
Chronic Kidney Disease (n,%) 2 1,82 2 1,43 1 1,23 5
Stroke (n,%) 1 0,91 4 2,86 3 3,70 8
Anticoagulant
UFH (n,%) 50 45,5 82 58,6 56 69,1 188
Enoxaparin (n,%) 35 31,8 58 41,4 25 30,9 118
Rivaroxaban (n,%) 15 13,6 0 0 0 0 15
Fondaparinux (n,%) 10 9,1 0 0 0 0 10
Laboratory P value
Leucocyte (10 3/uL; median, SD) 6,49 3,55 7,166 5,89 8,53 5,41 <0.05 *
Neutrophil (%; median, SD) 72,4 15,7 78 21,41 83,75 9,51 <0.05 *
Lymphocyte (%, median, SD) 17,1 9,9 12,15 10,42 9,5 7,01 <0.05 *
Lymphocyte absolute (10 3/uL, median, SD) 1,1 0,57 0,915 1,31 0,84 0,51 <0.05 *
Neutrophil-Lymphocyte Ratio (NLR) (median, SD) 4,18 5,3 6 5,61 8,38 12,5 <0.05 *
C-Reactive Protein (mg/L; median, SD) 18,7 39,43 40,1 42,43 34,1 49,28 <0.05 *
D-Dimer (mcg/l; median, SD) 0,62 3,2 0,9 7,48 1,2 8,53 <0.05 *
Outcome
Length of Stay (mean, SD) 18,46 9,54 19,54 10,63 14,26 11,5 <0.05 *
Discharge (n,%) 108 98,18 104 74,29 11 13,58
Death (n,%) 2 1,82 36 25,71 70 86,42

Kruskal Wallis.

 Subgroup analysis

We evaluated and compared inflammatory markers up to three times. To analyze the relationship between the administration of anticoagulants in each severity of COVID-19 cases, we calculated the decrease or increase in these inflammatory markers.

Based on the first and second laboratory evaluations, there were differences in decreasing leukocyte count and D-dimer in severe cases (p 0.03; p 0.026), decreasing neutrophils neutrophil-lymphocyte ratio and CRP in moderate cases, (p 0.004; p 0.028; and p<0.05), and increasing lymphocyte in critically ill cases (p<0.05). In this initial evaluation, there were no differences in inflammatory status between each degree of COVID-19 cases.

In contrast to the initial evaluation, the comparison of the inflammatory status between the second and third examinations found that there were differences in each severity group (moderate (p<0.05); severe (p<0.05) and critically ill (p 0.001)). Inflammation marker examination found decreasing leukocytes in moderate and severe cases (p<0.05; p 0.001), decreasing neutrophils in moderate, severe and critically ill cases (p<0.05; p<0.05; p 0.007), increasing lymphocyte count in moderate cases (p<0.05); increasing absolute lymphocyte in moderate and severe cases (p<0.05; p<0.05), decreasing NLR in moderate, severe and critically ill cases (p<0.05; p<0.05; p<0.05), decreasing CRP in moderate and severe cases (p<0.05; p< 0.05), and decreasing d-dimer in all case groups (p<0.05; p<0.05; p<0.05).

With these significant results, we evaluated the inflammatory parameters from the initial to the last examination. In moderate group, there was an improvement in inflammation parameters in all variables (p<0.05). In severe cases, significant improvements in inflammatory parameters were recorded, including decreasing leukocytes, decreasing neutrophils, decreasing lymphocytes, increasing absolute lymphocytes, decreasing NLR, decreasing CRP and d-dimer (p 0.01; p<0.05; p<0.05; p<0.05; p 0.0; p<0.05; p<0.05 p<0.05).

In the critically ill with ventilator support group, significant results were also obtained on each inflammatory marker variable except for decreasing leukocytes (p 0.6). From the clinical aspect, analysis of the use of anticoagulants on day of death found significant differences among groups of severity (p<0.05). Significant correlation of inflammatory status on the thirteenth day with mortality based on patient comorbidities was seen in moderate and critical cases on a ventilator (r=0.337; p<0.05 and r=0.25; p 0.05) (see Table 2 and Figure 2).

Evaluation of inflammatory marker and disease progression (SD: standard deviation).
First to second evaluation Moderate Severe Critically Ill with ventilator
n=110 P value n=140 P value n=81 P value
Inflammatory status 1st 40 36.36 0,003 * 73 52.14 0,142 41 50.62 0.32
2nd 21 19.09 61 43.57 48 59.26
Laboratory evaluation
Decreasing leucocyte (10 3/uL; median, SD) (n,%) 7.3 5.74 51 46.364 0.71 8.46 5.7 58 41.43 0,03 * 10.31 7.06 31 38.27 0.173
Decreasing neutrophil (%; median, SD) (n,%) 67.85 26.25 69 62.727 0.004 * 77.25 28.22 69 49.29 0.486 83 37.75 43 53.09 0.091
Increasing lymphocyte (%, median, SD) (n%) 16.85 12.05 55 50 0.866 9.65 13.35 58 41.43 0.07 5 9.48 20 24.69 < 0.05 *
Increasing lymphocyte absolute (10 3/uL, median, SD) (n,%) 1.2 0.83 64 58.182 0.16 0.925 1.11 73 52.14 0.544 0.706 0.865 35 43.21 0.056
Decreasing neutrophil-lymphocyte ratio (NLR) (median, SD) (n,%) 3.12 7.98 68 61.818 0.028 * 5.67 9.43 65 46.43 0.74 9 11.77 40 49.38 0.897
Decreasing C-reactive protein (mg/L; median, SD) (n,%) 17.26 31.429 63 57.273 <0.05 * 23.64 70.22 94 67.14 <0.05 13.8 40.35 29 35.80 0.324
Decreasing D-dimer (mcg/l;median, SD) (n,%) 0.51 3.19 58 52.727 0.598 1.362 2.32 79 56.43 0.026 1.08 11.85 31 38.27 0.427
Second to third evaluation Moderate Severe Critically Ill with ventilator
n=110 P value n=140 P value n=81 P value
Inflammatory status 2nd 21 19.091 <0.05 * 61 43.57 <0.05 * 48 59.26 0.001 *
3rd 4 3.6364 27 19.29 28 34.57
Laboratory evaluation
Decreasing leucocyte (10 3/uL; median, SD) (n,%) 3.73 4.76 67 60.909 <0.05 * 5.57 11.9 78 55.71 0.001 * 8.7 14.73 36 44.44 0.349
Decreasing neutrophil (%; median, SD) (n,%) 31,1 35,19 73 66,364 <0,05 * 49.05 39.51 89 63.57 <0.05 * 69.6 42.61 41 50.62 0.00 *7
Increasing lymphocyte (%, median, SD) (n%) 10 14.58 31 28.182 <0.05 * 3.7 10.77 42 30.00 <0.05 * 2.4 8.9 24 29.63 0.078
Increasing lymphocyte absolute (10 3/uL, median, SD) (n,%) 0.757 1,067 34 30,909 <0,05 * 0.564 2.14 47 33.57 <0.05 * 0,373 4.29 28 34.57 0.218
Decreasing neutrophil-lymphocyte ratio (NLR) (median, SD) (n,%) 2.23 4,37 77 70 <0,05 * 5.02 7,93 88 62.86 <0.05 * 3.3 14.82 39 48.15 <0.05 *
Decreasing C-reactive protein (mg/L; median, SD) (n,%) 2,71 7,83 64 58,182 <0,05 * 6.37 15.9 75 53.57 <0.05 * 7.8 19.9 43 53.09 0.221
Decreasing D-dimer (mcg/l;median, SD) (n,%) 0.39 0.822 62 56.364 <0,05 * 1.078 3.68 70 50.00 <0,05 * 0.847 2.1 35 43.21 < 0.05 *
First to third evaluation Moderate P value Severe P value Critically ill with ventilator P value
Decreasing leucocyte (%) 69.09 <0.05 * 57.86 0.01 * 51.85 0.60
Decreasing neutrophil (%) 77.27 <0.05 * 70.71 <0.05 * 65.43 <0.05 *
Increasing lymphocyte (%) 25.45 <0.05 * 30.71 <0.05 * 19.75 <0.05 *
Increasing lymphocyte absolute (%) 34.55 <0.05 * 39.29 0.00 * 35.80 0.013
Decreasing neutrophil-lymphocyte ratio (%) 78.18 <0.05 * 70.71 <0.05 * 62.96 0.135
Decreasing C-Reactive protein (%) 69.09 <0.05 * 73.57 <0.05 * 72.84 <0.05
Decreasing D-dimer (%) 73.64 <0.05 * 71.43 <0.05 * 76.54 <0.05 *
Decreasing inflammation status (%) 32.73 32.86 16.05
Days of death (mean) 30.50 11.70 12.95 <0.05 *
Correlation of inflammation status with mortality (r) 0.337 <0.05 ** 0.136 0.109 0.25 0.05 **

p value from Wilcoxon test.

p value from Spearman correlation.

 Differentiation of inflammatory parameters following anticoagulants in COVID-19.

Univariate and multivariate analysis using regression logistic revealed that no decline of inflammatory profile both on day 6 (adjusted odds ratio [aOR] = 2.36; 95% CI: 1.46-3.83, p<0.05) and day 13 (adjusted odds ratio [aOR]=4.15; 95% CI: 2.33-7.42, p<0.05) was related to the mortality event (see Table 3).

Odds for inflammation profile on day 1, day 6, and day 13 of anti-coagulant treatment (OR: odds ratio).
Status inflammation Outcome death
Unadjusted OR 95% CI P value Adjusted OR 95% CI P value
Inflammation 1 st day 1.23 0.98-1.55 0.091 1.29 0.76-2.18 0.34
Inflammation 6 th day 1.59 1.24-2.06 0.001 * 2.36 1.46-3.83 <0.05 **
Inflammation 13 th day 1.67 1.48-1.89 <0.05 * 4.15 2.33-7.42 <0.05 **

p<0.05 from univariate analysis.

p<0.05 from multivariate regression logistic analysis with adjusted from potential confounder variable.

Between UFH versus non UFH showed that in the overall case UFH significantly reduced mortality (OR=1.27; 95% CI=1.06-1.53; p=0.015), but not for moderate case and severe and critical case UFH vs non UFH showed no difference in the reduction of inflammation in overall cases, moderate cases, and severe cases (p>0.05) (see Table 4).

Association between UFH versus non-UFH anticoagulants on mortality, decreased inflammation on day 6, and day 13.
Mortality Inflammation status 6 th day Inflammation status 13 th day
Category anticogulant UFH vs Non UFH OR 95% CI P value OR 95% CI P value OR 95% CI P value
Overall case 1.27 1.06-1.53 0.015 ** 1.02 0.84-1.24 0.85 0.95 0.75-1.20 0.67
Moderate case 1.1 0.27-4.47 0.89 1.45 0.76-2.75 0.22 1.85 0.34-10.22 0.4
Severe and crtitical case 1.14 0.93-1.39 0.22 1.06 0.86-1.30 0.56 1.01 0.79-1.29 0.91

p value Chi Square Test; p<0.05.

 Discussion

COVID-19 has been linked to coagulation disorders which cause various complications. An increase in coagulation parameters, such as D-dimers, is an independent risk factor for death. Patients with D-dimers of more than 1000 ng/mL have a 20 times greater risk of death due to infection. 17 Although the pathogenesis of coagulopathy in COVID-19 cannot be fully explained, the mechanism may resemble septic coagulopathy in bacterial infections. Overabundant pro-inflammatory cytokines increases the level of damage-associated molecular patterns (DAMP). Activation of coagulation factors due to cell and endothelial damage is the most common mechanism of infection. Both the pathogen and DAMP from damaged tissue activate monocytes. The activated monocytes release pro-inflammatory cytokines (IL-1, IL-6, IL-10, TNF-α) and chemokines that animate neutrophils, lymphocytes, platelets, and vascular endothelial cells. The coagulation cascade is commenced by tissue factor and phosphatidylserine on the cells’ surface. Healthy endothelial cells retain anti-thrombogenetic properties by expressing glycocalyx and binding with anti-thrombin proteins. Damaged endothelial cells change their nature to become more procoagulant due to glycocalyx disorders and loss of anticoagulant proteins. 7 , 12 , 18 , 19 Markers of hypercoagulation and high levels of inflammatory mediators are consistent with poor outcome in patients having acute respiratory distress syndrome (ARDS) and sepsis. These observations have led to several studies focused on inflammation and coagulation pathways in acute lung injury, ARDS or sepsis. 17 , 20 , 21

Our study included 110 moderate cases, 140 severe cases, and 81 critically ill cases with a ventilator. Patients with pneumonia category and those who have comorbid hospitalized with a history of heart problems, use a central venous line, respiratory failure and have a risk of thrombosis according to the guidelines of the European Society of Cardiology and ISTH are recommended to use a prevention and therapeutic dose of anticoagulant.

The routine use of anticoagulant regimens in COVID-19 patients requiring hospitalization has been recommended, after several studies found an association between viral inflammation and coagulation disorders. 22 Several guidelines for the management of COVID-19 have included anticoagulant regimens regarding COVID-19-related coagulation disorders. Recommendations issued by the Anticoagulation Forum (ACF) and the American College of Chest Physicians (ACCP) selected Low Molecular Weight Heparin (LMWH) over Unfractioned Heparin (UFH) to minimize laboratory evaluation. ACCP also recommends fondaparinux over UFH in patients with a high risk of bleeding, kidney problems, and any plans for procedures in the near future. UFH is more recommended by the ACF for patients with renal impairment with creatinine clearance <15-30 mL/min. The American Society of Hematology (ASH) states that LMWH or UFH is the therapy of choice over oral anticoagulants due to the potential for drug interactions and short half-lives. 23 , 24 In our study, the population involved was a group of patients requiring hospitalization due to SARS-CoV2 infection ranging from moderate to critically ill. At all three severity groups, patients receiving anticoagulant therapy had increased D-dimer values. This increase in D-dimers expresses clusters of fibrin lysis and thrombus in the pulmonary vessels. Previously, Guan et al. (2020) found that 46% of 1099 COVID-19 patients had an increase in D-dimer and only 5% experienced a decrease in platelet count. 19 , 25

The relationship between coagulation function and indicators of inflammation and infection has been previously analyzed by Long et al. (2020). D-dimers were positively correlated with CRP (r=0.36, p=0.0007) and procalcitonine (r=0.45, p<0.001). 26 Increased CRP was also found in patients with coagulation disorders by Friedrich et al. (2020) to a mean level of 131 ± 106 mg/l. 27 In our study, there was an increase in CRP by a mean of 40.1 ± 42.43/l in the severe group population receiving anticoagulants that met the criteria for anticoagulant according to The International Society of Thrombosis Haemostasis (ISTH) or due to an increase in D-dimer. A meta-analysis regarding dosing of anticoagulant therapy on the COVID-19 mortality rate found that there was a slight decrease in the mortality rate in COVID-19 patients in need of a ventilator. 28 Several studies have shown different results on the mortality outcome of COVID-19 patients receiving anticoagulant therapy. In theory, the coagulation cascade is active when inflammation is present due to SARS-CoV2 infection. Therefore, it may be possible to obtain beneficial effects from the use of anticoagulants as anti-inflammatory agents.

According to the theory of hypercoagulation disorders, there is a two-way relationship between the immune system and thrombin formation, where inhibition of thrombin formation may reduce the inflammatory response. 29 The positive effects of using anticoagulants on mortality were reported by Nadkarni et al.; compared to non-users, recipients of anticoagulants for both therapy and prophylaxis had reduced mortality (adjusted hazard ratio [aHR] = 0.53; 95% CI: 0.45-0.62, and aHR = 0.50; 95% CI.: 0.45-0.57, respectively), and intubation rates (aHR 0.69; 95% CI: 0.51-0.94, and aHR 0.72; 95% CI: 0.58-0.89, respectively). 30 Our study revealed that no reduction in inflammatory markers was significantly correlated with the mortality.

Several other studies have classified the use of anticoagulants according to their intended use for therapeutical and prophylaxis purpose. Klok et al. (2020) found that 15% of the population needed ICU while Helms et al. (2020) revealed 25 of their patients had pulmonary embolism despite receiving anticoagulant therapy. 22 Therefore, anticoagulant prophylaxis is rational. In addition, the prophylactic use of apixaban (odds ratio [OR] 0.46, p=0.001) and enoxaparin (OR=0.49, p=0.001) exhibited a significant decrease in mortality. There was also an association between therapeutic apixaban and decreased mortality rates (OR 0.57, p=0.006). Pawlowski et al. (2020) compared the effects of enoxaparin and heparin, showing that patients receiving heparin had a higher risk of death and higher ICU admission than ones in enoxaparin group (risk ratio: 6.76; 95% C.I: [3.39, 12.7]; adjusted p-value <0.0001); (risk ratio of ICU admission: 1.51; 95% C.I.: [1.12, 2.03]; adjusted p-value 0.01). Additionally, ICU and hospital length of stay were shorter in the enoxaparin population (mean ICU duration: 0.9 days [standard deviation: 2.5], mean hospital duration: 5.4 days [standard deviation: 4.3]). 31

In this study, we found favorable changes in inflammatory markers such as white blood cells, neutrophils, lymphocytes, CRP, and D-dimers after the use of anticoagulants. The analysis of the results of this study has been divided based on the subgroup of the severity of COVID-19 disease. From the clinical aspect, analysis of the use of anticoagulants on day of death found significant differences among groups of severity (p<0.05). There was also a significant correlation between inflammatory status on the thirteenth day and mortality based on patient comorbidities obtained from moderate and critical cases on a ventilator (r=0.337; p< 0.05 and r=0.25; p 0.05). Univariate and multivariate analysis exhibited that no reduction in inflammatory profile on day 6 (adjusted odds ratio [aOR]=2.36; 95% CI: 1.46-3.83, p value < 0.05) and day 13 (adjusted odds ratio [aOR]=4.15; 95% CI: 2.33-7.42, p value < 0.05) were linked to the patients’ mortality. Meanwhile, the comparison of UFH versus non-UFH showed that in the overall case, UFH significantly reduced mortality, although there was no difference in the decrease in inflammation on days 6 and 13. This study is able to explain that the values of inflammatory and hypercoagulable markers go hand in hand with the severity of the patient. Although not all inflammatory markers improve after anticoagulant treatment, inflammatory variables mostly manifested good results. This study we update to distinguish one regimen of anticoagulant from another based on UFH versus non UFH. Conclusively, it is necessary to carry out further subgroup analysis of the types of anticoagulants and the comparison of therapeutical effects and prophylactic use of anticoagulant towards the mortality outcome and length of stay based on larger data.

 Conclusion

Administration of anticoagulants to COVID-19 patients with moderate to critical presentation promoted significant outcomes of inflammatory markers which ultimately showed a statistical difference in mortality. Most of the inflammatory markers in patients improved after anticoagulant administration. Therefore, our findings confirm that the administration of anticoagulants can be optimized since they are able to work as anti-inflammatories.

 Data availability Underlying data

figshare: Data of Inflammatory Parameters after Anti-coagulant among Moderate, Severe, and Critically Ill COVID-19 Patients. https://doi.org/10.6084/m9.figshare.16910905.v2

This project contains the following files:

Data_antikoagulan_all_join_3.xlsx (raw data file)

Legend (Anti-coagulant_311021).docx (data key)

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

 Authors' contributions

Pradana Zaky Romadhon: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Siprianus Ugroseno Yudho Bintoro: Conceptualization, Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Satriyo Dwi Suryantoro: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Tri Pudy Asmarawati: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Alfian Nur Rosyid: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Merlyna Savitri: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Putu Niken Ayu Amrita: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Muhammad Noor Diansyah: Conceptualization, Formal Analysis, Investigation, Methodology, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Ami Ashriati Prayoga: Conceptualization, Formal Analysis, Investigation, Methodology, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Choirina Windradi: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Bagus Aulia Mahdi: Conceptualization, Data Curation Formal Analysis, Investigation, Methodology, Project Administration,Resources, Software, Validation, Writing – Original Draft Preparation

Krisnina Nurul Widiyastuti: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Dwiki Novendrianto:: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Esthiningrum Dewi Agustin:: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Firas Farisi Alkaff: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Kartika Prahasanti: Data Curation, Investigation, Methodology, Resources, Software, Visualization; Writing – Original Draft Preparation

Didi Darmahadi Dewanto: Formal Analysis, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Original Draft Preparation

 Acknowledgements

We would like to thanks Prof. Dr. Nasronudin dr, Sp. PD KPTI for supporting this research.

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Clinical and Applied Thrombosis/Hemostasis. 2020;26:107602962096285. 33074732 10.1177/1076029620962853 Abou-Ismail MY Diamond A Kapoor S : The hypercoagulable state in COVID-19: Incidence, pathophysiology, and management. Thrombosis Research. 2020;194:101–115. 32788101 10.1016/j.thromres.2020.06.029 Iba T Levy JH Levi M : Coagulopathy in COVID-19. J Thromb Haemost. Journal of Thrombosis and Haemostasis. 2020;18:2103–2109. 32558075 10.1111/jth.14975 Saleh NY Aboelghar HM Salem SS : The severity and atypical presentations of COVID-19 infection in pediatrics. BMC Pediatrics. 2021;21(1):144. 33765980 10.1186/s12887-021-02614-2 Vidali S Morosetti D Cossu E : D-dimer as an indicator of prognosis in SARS-CoV-2 infection: a systematic review. ERJ Open Research. 2020;6:00260–02020. 10.1183/23120541.00260-2020 Favaloro EJ Thachil J : Reporting of D-dimer data in COVID-19: Some confusion and potential for misinformation. Clinical Chemistry and Laboratory Medicine. 2020;58:1191–1199. 32432563 10.1515/cclm-2020-0573 Wadaa-Allah A Emhamed MS Sadeq MA : Efficacy of the current investigational drugs for the treatment of COVID-19: a scoping review. Annals of Medicine. 2021;53(1):318–334. 33706639 10.1080/07853890.2021.1875500 Bikdeli B Madhavan MV Jimenez D : COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. Journal of the American College of Cardiology. 2020;75(23):2950–2973. 32311448 10.1016/j.jacc.2020.04.031 Feng X Zhu B Jiang C : Correlation between White Blood Cell Count at Admission and Mortality in COVID-19 Patients. A Retrospective Study. 2020. Li X Liu C Mao Z : Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Critical Care. 2020;24(1):647. 33198786 10.1186/s13054-020-03374-8 Wagner J DuPont A Larson S : Absolute lymphocyte count is a prognostic marker in Covid-19: A retrospective cohort review. International Journal of Laboratory Hematology. 2020;42(6):761–765. 32779838 10.1111/ijlh.13288 Liu F Li L Xu M : Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. Journal of Clinical Virology. 2020;127:104370. 32344321 10.1016/j.jcv.2020.104370 Colling ME Kanthi Y : COVID-19-associated coagulopathy: An exploration of mechanisms. Vascular Medicine. 2020;25(5):471–478. 32558620 10.1177/1358863X20932640 Singhania N Bansal S Nimmatoori DP : Current Overview on Hypercoagulability in COVID-19. American Journal of Cardiovascular Drugs. 2020;20(5):393–403. 32748336 10.1007/s40256-020-00431-z Haimei MA : Pathogenesis and Treatment Strategies of COVID-19-Related Hypercoagulant and Thrombotic Complications. 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EClinicalMedicine. 2021;33:100774. 33718845 10.1016/j.eclinm.2021.100774 10.5256/f1000research.146099.r228747 Reviewer response for version 4 Laloglu Esra 1 Referee https://orcid.org/0000-0001-5189-3564 Department of Biochemistry, Faculty of Medicine, Ataturk University, Erzurum, Erzurum, Turkey

Competing interests: No competing interests were disclosed.

 23 1 2024 Copyright: © 2024 Laloglu E 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

In ıntroduction section, please write the aim of the study

In your  abstract you wrote as” Anticoagulants in COVID-19 patients lower inflammation markers.Also your title is about  improvement of inflammatory markers  but  your introduction section only associated with progression, especially  prothrombotic conditions. Can  you also talk about the relationship between inflammatory markers and progression?

In material method please write  information about biochemical and hematological analyzes ( What are the tests analyzed in serum, plasma or total blood? How were blood samples collected, were they centrifuged, which brand of devices were analyzed?write  the country of devices……etc

In result section, there is no healthy group in your study so can you write the reference value of the laboratory parameters?

In table 1, Your value is not normally distributed, sou can you add median (min, max) for the values of laboratory parameters

In table 1, please write the exact value of p value (not p<0.005), write as  p=0.011

In table 1 and table 2 and figüre 2  The p value belongs to the comparison of which two groups? Please write  under the table and figure

In table 1 There are also laboratory parameters, add them to the table title.

In result section “Significant correlation of inflammatory status on the thirteenth day with mortality based on patient comorbidities was seen in moderate and critical cases on a ventilator (r=0.337; p<0.05 and r=0.25; p 0.05)”

The r value is significant but weak, not strong,  r=0.25 (weak), r =0.337 (medium )can you write degree of the correlation

I do not understand table 2. Table 2 is the value of second evaluation. In table 1 there is laboratory value at first day, When I look at the Leucocyte  value of table 1 and then table 2  I can see an increasing not  decreasing. In table 1 , for the moderate group Leucocyte  value= 6,49 and ın table 2 Leucocyte  value=7.3. I do not understand which value decreasing or increasing. This table is very mixed. You have written increasing or decreasing laboratory values in table 2 and given the mean values, sd values for all parameters. Please write the table 2 again.

In table 2 there is no  ** p value from Spearman correlation.

In your abstract “Anticoagulants in COVID-19 patients lower inflammation markers” but ın your result section “,……. but not for moderate case and severe and critical case UFH vs non UFH showed no difference in the reduction of inflammation in overall cases, moderate cases, and severe cases. Which  sentence is true? What does the meaning of both sentences?

In discussion section “…..Our study revealed that no reduction in inflammatory markers was significantly correlated with the mortality.” Why is this sentence contrary to the literature? Can you explain in discussion section

In discussion section and coblusion section …………….”Meanwhile, the comparison of UFH versus non-UFH showed that in the overall case, UFH significantly reduced mortality, although there was no difference in the decrease in inflammation on days 6 and 13. This study is able to explain that the values of inflammatory and hypercoagulable markers go hand in hand with the severity of the patient.” Is there a contrast.

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

Partly

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

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

Partly

Are the conclusions drawn adequately supported by the results?

Partly

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

Partly

Reviewer Expertise:

Biochemistry

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.

 10.5256/f1000research.138255.r150159 Reviewer response for version 3 Zhang Zhongheng 1 Referee https://orcid.org/0000-0002-2336-5323 Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Competing interests: No competing interests were disclosed.

 20 9 2022 Copyright: © 2022 Zhang Z 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

I am satisfied with current version and previous comments are well addressed.

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

Partly

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

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

Yes

Are the conclusions drawn adequately supported by the results?

Yes

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

Partly

Reviewer Expertise:

emergency and 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.

 10.5256/f1000research.122136.r126644 Reviewer response for version 2 Zhang Zhongheng 1 Referee https://orcid.org/0000-0002-2336-5323 Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Competing interests: No competing interests were disclosed.

 9 3 2022 Copyright: © 2022 Zhang Z 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

This is an interesting clinical question relevant to the risk stratification for COVID-19. However, I can find numerous risk stratification studies by utilizing exactly the lab measurements for the disease and there is lack of novelty in the current study. I also have several detailed comments:

1. I do not think the discussion on anticoagulation strategy is relevant for the current study. The current study is a risk factor analysis and the causality of heparin intervention is not explored.

2. The data analysis results described in the abstract do not support the conclusion that "Anticoagulants in COVID-19 patients lower inflammation markers."; the causal inference has not been implemented formally.

3. Many patients would be discharged/dead before day 13, how did you handle such competing risk? In such a situation, death can be regarded as competing risk if you are focusing on inflammation outcomes.

4. "After all the completed researches, further study on how to use anticoagulants is still being analysed to promote the best prognosis."---this discussion is irrelevant to the current study.

5. I suggest that the authors should extract data on heparin use and then explore the causality between heparin and mortality, while stratified by subgroups of population.

6. "We also determined the relationship between inflammatory state after anti-coagulant with patients’ mortality, along with some variables, which were age, gender, disease severity, and comorbidity."---this statement is confusing. You need to specify to use multivariable regression model to adjust for confounding. Furthermore, the model specification described here does allow replication of the study. For example, did you consider interaction or non-linearity for the relationship between covariates and outcome? You assumed linearity but may not hold true in real world data. You need to discuss the limitation of current study in adjusting for confounding factors. However, another approach is to use ensemble modelling 1 , which can address non-linearity automatically without pre-specification. At least you need to mention this in the discussion section.

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

Partly

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

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

Yes

Are the conclusions drawn adequately supported by the results?

Yes

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

Partly

Reviewer Expertise:

emergency and 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.

References : Predictive analytics with ensemble modeling in laparoscopic surgery: A technical note. Laparoscopic, Endoscopic and Robotic Surgery .2022; 10.1016/j.lers.2021.12.003 10.1016/j.lers.2021.12.003