Heart rate variability as a prognostic marker in critically ill patients

Introduction

The fluctuation in the time between contiguous heartbeats is referred to as heart rate variability. For the maintaining of systemic homeostasis and an adequate response to external stimuli, the sympathetic and parasympathetic subgroups of the autonomic nervous system (ANS) must be in balance.¹ The autonomic nervous system (ANS) is a part of the peripheral nervous system (PNS) that is responsible for coordinating involuntary physiological functions that include respiration, heart rate, blood pressure, digestion, etc. These functions are further controlled by the sympathetic and parasympathetic systems. Dysfunction of ANS (one or more subdivisions) along with a pre-existing disease has been seen to be been associated with a poor outcome of the disease. Cardiac autonomic dysfunction can result from any disease that affects any of the various components of the ANS.²

Disorders such as sepsis, coronary artery disease, strokes, chronic liver disease, chronic kidney disease, malignancy, hypertension, diabetes mellitus, etc, with autonomic dysfunction, often require management in intensive care units (ICUs). In the ICU, prompt and proper assessment of patients’ prognosis, outcome, and mortality risk is of the most importance. It has been speculated that early recognition of autonomic dysfunction can help predict prognosis and direct appropriate and timely treatment. The available scoring systems such as APACHE 4, though competent, lack feasibility of application in the ICU setting due to lack of required information for scoring.³ Therefore, over the years, many methods have been used in assessing autonomic function such as the baroreflex sensitivity, cardiac-chemoreflex sensitivity, and the heart rate variability (HRV).⁴

Heart rate variability calculation is one of popular approaches to estimate autonomic nervous system (ANS) dysregulation. It is the variability between heart beats or more scientifically, variability between successive R waves on electrocardiogram (ECG) or the R-R interval (RR). It is a non-invasive method increasingly used in medicine.³ The clinical potential of HRV recognized by Hon and Lee in 1965, when they discovered that the acute changes in HRV was linked to foetal distress and foetal hypoxia could be predicted from it before any appreciable changes occurred in the heart rate itself.⁵ This finding was paramount in the development of the standard of care in monitoring fetal distress and therefore, significantly reduced incidences of morbidity and mortality.⁶ Further, in the survivors of myocardial infarction, an association of reduced HRV with increased risk of mortality and subsequent events of arrhythmic events was noted.⁷

In recent times, it has been found that reduced variability of the heart rate could prove to be an indicator of risks of adverse events and mortality in patients with several diseases that affects the autonomic nervous system more so in the critically ill.^8,9 A healthy individual with normal functioning autonomic nervous system usually has a high variability in the heart rate whereas a lower variability often indicates dysfunction and inadequate adaptability of the autonomic nervous system.¹⁰ Patients who are critically ill may experience an altered sympathetic-parasympathetic balance and a key task of the ANS is to regulate the cardio-respiratory interaction to ensure an optimal oxygen supply.⁴ Loss of this balance results in variability of the heart rate. It has been suggested that in the ICU and emergency settings, the heart rate variability analysis can help predict at risk patients and those who can be benefitted from early admissions and management.^11,12

Like many other physiological phenomena, HRV demonstrates intricate relationships between organs, tissues, and cells. The phenomena of HRV is based on the concept that the state of organs, tissues, and cells and the strength of interaction among them are affected by illness and treatment which in turn affects the HRV in an individual.¹³ Heart rate variability can arise from both cardiac and noncardiac events, frequently reflecting overall/global rather than local processes.

Many methods for HRV analysis have been developed, some more accurate than others. All the methods essentially are derived from three general domains, namely, the time domain, frequency domain, the regularity domain. Continuous ECG recordings that are digitally processed at a frequency of at least 125 Hertz (Hz) are the source of HRV data. Several more adult studies have demonstrated that decreases in HRV are correlated with clinical outcomes and precede clinical decline.¹¹ As a result, HRV can be useful in the ICU as a gauge of clinical prospects. It is not yet possible to translate these findings into useful information that emergency room doctors can use right now. Use of heart rate variability is an uncommon clinical practice due to its limited applicability in acute care. Determining the function of heart rate variability as a predictive factor in critically ill patients admitted to the ICU was the goal of this study. The additional goals were to determine the mortality rate, the length of ICU/hospital stays, and how those factors are related to HRV.

Methods

Results

Figure 1 shows the patients flow diagram. The total number of ICU patients admitted during the study was N = 1640. Individuals were excluded from the study for the following reasons: patients who are currently taking medications that may interact with HRV analysis drugs like beta blocker (N = 330), patients who are on sedatives (N = 490), patients who are drowsy and disoriented at the time of admission (N = 550), patients who did not give consent (N = 6) and due to technical error of hrv analysis (N = 19) and loss to follow up due to leaving against medical advice (N = 20) excluded from the study. A total of 225 subjects were eligible for study.²¹

Figure 1. Patients flow diagram.

Table 1 shows a mean age of 54.0 years (16.3), systolic blood pressure of 124.4 mmHg (16.9), diastolic blood pressure of 83.2 mmHg (14.7), and respiratory rate of 26.6 (6.2) respectively. The most common co-morbidity affecting the study population was hypertension which was seen in 146 (64.9%) patients. The other co-morbidities were sepsis 82 (36.4), stroke 63 (28.4), diabetes 67 (29.8), chronic liver disease 20 (8.9). Male study participants had significantly higher no. of sepsis 65 (43.0%) as compared to in females 17(23.0%) and Chronic liver disease was seen in more males 18 (11.9%) as compared to females 2 (2.7%). 40% of female participants had a stroke which was significantly higher than males 21.9%.

Table 2 shows the distribution of biochemical parameters among the study population. There was no significant difference between males and females in biomedical parameters.

Table 3 shows the mean duration of hospital stay was 11.4 (10.2%) days among the study population. 42 patients died during hospital stay amounting to an 18.7 % mortality. 183 (81.3%) patients were discharged during the study period. The number of patients who required ventilatory support was 48 (±21.3), male 37 (±24.5), females 11 (±14.9). Vasopressor support 61 (±27.1), male 44 (±29.1), and females 17 (±23.0).

Table 4 shows the variables High frequency, Low frequency/High frequency ratio were significantly associated (p < 0.05) with SDANN and SDNN.

Table 5 shows the variables High frequency, Low frequency/High frequency ratio and Outcome were significantly associated (p < 0.05) with APACHE 4 score. HF parameter was abnormal in 83% of patients who died as compared to 44% of the survived group (p-value < 0.001). LF/HF parameter was abnormal in 86% of patients who died as compared to 54% of the survived group (p-value < 0.001).

Table 6 shows the variables low frequency, High frequency, Low frequency/High frequency ratio, APACHE 4 score, GCS score were significantly associated (p < 0.05) with the variable’s outcome.

Table 7 shows the variables Urea, Low density lipid, Use of mechanical Ventilator, FiO₂, PaO₂ significantly associated with survival were (p < 0.05) with the Sepsis, SDANN, and SDNN, APACHE 4, GCS score, LF, HF, and LF/HF ratio.

Table 8 shows diagnostic accuracy of heart rate variability shows at a cutoff of SDANN Index was ≤15 by ROC to predict mortality: Death with a sensitivity of 86% and specificity of 31%. At a cutoff of SDANN Index was ≤15 by ROC it predicts mortality: Death with a sensitivity of 86%, and specificity of 31%. At a cutoff of RMSSD was ≥31 by ROC it predicts mortality: Death with a sensitivity of 67%, and as and specificity of 44%. At a cutoff of LF ≤9.2 by ROC it predicts mortality: Death with a sensitivity of 47%, and Specificity of 81%. At a cutoff of HF ≤39.5 by ROC it predicts mortality: Death with a sensitivity of 47%, and a specificity of 69%. At a cutoff of LF/HF ≤0.3 by ROC it predicts mortality: Death with a sensitivity of 60%, and a specificity of 64%. At a cutoff of APACHE 4 score was ≥71 by ROC it predicts mortality: Death with a sensitivity of 98% and specificity of 88%.

Table 9 shows the coefficients representing the log-odds of the outcome variable for each predictor variable. APACHE 4 score shows the increase in score with the odds of the outcome variable while age and LF shows negative association.

Discussion

In this study, we show that heart rate variability is clinically useful for critically ill patients’ severity rating (APACHE 4) and prognosis. When compared to patients who failed to improve and had to be brought to the ICU, individuals whose critical illness stabilised tended to have larger heart rate variability and showed bigger hour-by-hour rises. Mean and hourly heart rate variability also predicted variations in survival.

The gold standard for clinical HRV measurement is HRV recordings, which are influenced by circadian rhythms, core body temperature, metabolism, the sleep cycle, and the renin-angiotensin system.¹⁴ varying heart rate The quantity of HRV that was detected during monitoring intervals that could last from 1 min to >24 hours is quantified by time domain indices. Included in the measures are the SDNN, SDANN, RMSSD, LF, HF, and LF/HF.

Low HRV has been previously described as a marker of greater illness and worse outcomes.¹⁵ In the prospective cohort of Castilho FM et al.¹⁵ the values reported in the 20 minutes Holter among the survivor group vs non- survivor group were: LF power 18.0 vs 2.0, HF power 9.0 vs 6.5 and LF/HF 1.29 vs 0.40. In our study as per the 24-hour Holter HRV among the survivor group vs non- survivor group were: LF was 211.3 ± 268.7 while HF was 241.5 ± 366.1 and the mean LF/HF ratio was 1.4 ± 1.3, SDANN index was recorded as 18.3 ±40.0 while the mean SDNN was evaluated as 39.6 ± 36.1. RMSSD was 37.8 ± 21.4. The mean analyzed beats were observed to be 1358 ± 714.9 while the mean analyzed units were 17.1 ± 4.6. HF parameter was abnormal in 83 % of patients who succumbed as compared to 44 % of those who survived. LF/HF parameter was abnormal in 89 % of patients who died as compared to 44 % of those who survived.

Amongst the ICU population, the mean APACHE 4 score of 225 study participants was 58.9 ± 23.0 and a significant association was observed between the outcome and the APACHE 4 score. A mean score of 92.1 ± 15.2 was observed for participants who died which were significantly higher than the other group of discharged participants (51.3 ± 16.9). In the prospective cohort of Castilho FM et al.¹⁵ the mean APACHE 4 score among survivors was 14.15 ± 5.93 while among non- survivors it was 21.94 ± 8.45.

In the cohort study by Salahuddin N et al.¹⁶ on heart rate variability during rapid response team consultations on 91 patients. The Mean age was 49.9 ± 22.3 years and 54.9% of the participants were male. The reasons for admission at the hospital included liver cirrhosis patients (10%), chronic respiratory disease patients (9%), renal disease patients (9%), malignancy cases (32%), chronic multiorgan dysfunction cases (12%) and other cases (13%). In our study Hypertensive patients (64.9%), sepsis (36.4%), diabetes patients (29.8%), acute stroke (28.0%), coronary artery disease (22.7%), chronic liver disease (8.9%), malignancy (2.7%), chronic kidney disease (2.2%).

In the prospective, observational cohort study conducted by Bishop DG et al.,¹⁷ out of the total 55 patients, 35 required invasive ventilation (55%), 19 required inotropic support (35%) and 2 needed renal replacement therapy (4%). The median duration of stay in the ICU was 2.6 days (1-22 days). In the retrospective cohort study conducted by Liu N et al.¹⁸ on 342 patients, 19% had 30 days of in-house mortality and the remaining 81% survived. In our study 225 patients, mean duration of hospital stay. 10.5 % died, 10.1% discharged. A significant association was observed between GCS score and patient outcome as the mean GCS score was significantly higher for the group who got discharged (13.7 ± 2.8) in comparison to the group who died (7.4 ± 1.4).

Studies by Maheshwari A et al.,¹⁹ Graff B et al.,²⁰ Hadase M et al.²¹ and Ong ME et al.²² also reported an association between low heart rate variability with sudden cardiac death, stroke outcome, prognosis in heart failure and risk of cardiac arrest respectively. In our study, factors associated with mortality were low HF, low LF, low LF/HF ratio, high APACHE IV Score and low GCS score.

Takase et al.²³ demonstrated that SDANN lower than 30 ms had greater sensitivity and specificity (92%) than SDANN higher than 20 ms (31% sensitivity and 100% specificity) to detect autonomic dysfunction and cardiac events in cardiac autonomic neuropathy. In our study diagnostic accuracy of HRV was analyzed and it was higher in APACHE 4 (89.8%) and LF (74.1%).

Conclusion

Out of the 225 participants, 20% died during the study period. APACHE 4, Glasgow coma scale (GCS) score, and LF were significantly and independently associated with mortality. Decrease in Low frequency parameter of 24 hours Heart Rate Variability identified mortality with accuracy of 74% with 81.2% specificity, and 46.7% sensitivity.