INTRODUCTION
The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first reported in the Wuhan province in China in December 2019, caused at least 3 million deaths globally in 2020 by the WHO’s preliminary estimates.1 With vaccination gaining pace across the globe, there has been a decline in mortality due to SARS-CoV-2 disease (COVID-19); however, COVID-19 is still causing substantial mortality.2 In addition, the likelihood of the emergence of new variants, leading to a surge in cases and deaths, remains a definite possibility. In-hospital case fatality due to COVID-19 has been variously reported as 2–3%.3
The mortality observed in the Indian and South Asian subcontinent has reportedly been lower than in the West, and multiple reasons have been ascribed for this differential mortality as described in the literature.4 Understanding of the predictors of mortality due to COVID-19 helps in prioritizing patient care, especially in low-resource settings where hospital beds are limited. It also helps in prioritizing preventive care and vaccination. An understanding of the predisposing conditions and disease-specific clinical, laboratory, and radiological parameters can assist in developing a COVID-19–specific composite score to predict unfavorable clinical outcomes.5
Various studies have reported higher age, male sex, low oxygen saturation (SpO2) and dyspnea at admission, and preexisting morbidities as predictors of in-hospital mortality due to COVID-19.6–8 However, data on the effect of various repurposed and other therapies are contradictory. Few studies reported beneficial effect of drugs like hydroxychloroquine in preventing death;9 most randomized controlled trials did not corroborate this.10,11 There are limited data from India on either the predictors of COVID-19 deaths or the effect of various drugs and treatment modalities in preventing death, and the available data are from a single health facility, restricting their generalizability.12,13 This multicentric hospital-based study was undertaken with the objective of documenting treatment practices and identifying predictors of mortality among symptomatic COVID-19 patients who were admitted to different hospitals.
MATERIALS AND METHODS
This retrospective, hospital-based, unmatched case-control study was assessed among deaths that occurred at nine tertiary care teaching hospitals in India between March 2020 through December 2020 to March 2021. Study hospitals were conveniently chosen to represent different regions of the country. Their capacity varied from 60 to 2,700 beds, and all were managing COVID-19 as well as non–COVID-19 cases.
Case and control selection.
Cases were defined as patients with SARS CoV-2 infection confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)/rapid antigen test (RAT) during the hospital stay or within 28 days before hospital admission and death in the hospital during the study period. Controls were patients with infection with SARS CoV-2 diagnosed by RT-PCR/RAT who were discharged from the hospital after recovery and were recruited from the same hospital. Although we initially planned to have two controls per case, this was later changed to one control per case when the sample size planned for the study (660 cases) was exceeded. This sample size was adequate for estimation of an odds ratio of 1.5 for a population exposure level of 15% with 1% alpha error and 85% power. Cases were consecutively selected from hospital records by date of admission. Cases and controls were selected at a ratio of 1:1. Controls were not matched to cases except by day of admission in the same hospital and were selected randomly from hospital records.
Data extraction.
All information regarding cases and controls was extracted retrospectively from the medical records of patients. A standardized data extraction sheet was developed, and data were extracted by trained physicians. In addition to age and sex, information on preexisting morbidities (i.e., hypertension, diabetes mellitus, ischemic heart disease, stroke, any malignancy, chronic respiratory disease, tuberculosis, and immune disorder, as well as tobacco smoking) was extracted. Missing information on preexisting morbidities was treated as absence of morbidity, as it was assumed that it is a routine practice to record those morbidities in hospital case sheets. Information on the clinical status of a patient at the time of admission, that is, presenting symptoms (cough, fever, difficulty in breathing) and signs (respiratory rate, systolic and diastolic blood pressure, altered mental status, SpO2, and pulse rate), was extracted. The quick Sequential Organ Failure Assessment (qSOFA) score was calculated by assigning a score of 1 each for altered mental status, respiratory rate ≥ 22, and systolic blood pressure ≥ 100 mm Hg. Information on administration of antivirals, steroids, hydroxychloroquine, convalescent plasma, or immunoglobulins was collected; however, data on type of antiviral or duration of drug treatment were not extracted. In addition, information on type of respiratory support (high-flow nasal cannulation, noninvasive and invasive mechanical ventilation), complications during the hospital stay (sepsis, heart failure, respiratory failure, coagulopathy, acute cardiac and kidney injury, stroke, arrythmia, and myocarditis), and admission to the intensive care unit (ICU) was extracted.
Data analysis.
Data were entered in an Excel sheet and imported in Stata version 15 (StataCorp, College Station, TX) for further analysis. No imputation was done for missing data. Various study characteristics were presented either as proportion or means and compared between cases and controls using the χ2 test or unpaired t-test wherever appropriate. Univariable and multivariable logistic regression was done to assess the association between various predictor variables and deaths due to COVID-19. To assess the association between demographic variables and clinical presentation, the multivariable logistic regression was adjusted for variables that were found to be significant (P < 0.05) in the bivariate analysis. Adjustments were done in the multivariable logistic regression for age, sex, preexisting morbidities, presenting symptoms at the time of admission, qSOFA score and SpO2 at the time of admission, admission in the ICU, invasive mechanical ventilation, and associated complications in the hospital. Adjustment for clustering by site was done in all analyses. P < 0.05 was considered significant. Ethical approval for the analysis was obtained from the Ethics Committee of the All India Institute of Medical Sciences, New Delhi, and all participating institutes.
RESULTS
A total of 2,431 patients were included in the study; 1,137 were cases, and 1,294 were controls. The least number of cases contributed by a site was 27, and the maximum was 334. The participating hospitals showed considerable variations in patient severity profile (comorbidity and qSOFA score) and treatment practices, especially the use of antivirals and hydroxychloroquine (Table 1).
Profile of participating hospitals based on the study sample
| Hospital | Study period | Total cases | Total controls | % Of controls with | Treatment practices among controls (%) | Outcomes in the hospital among controls (%) | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Co- morbidity | qSOFA score > 1 | Antibiotic use | Anti-viral use | Steroid use | Hydroxy- chloroquine use | Invasive mechanical ventilation | Intensive care unit admission | ||||
| 1 | Mar 20–Dec 20 | 52 | 86 | 45.4 | 2.1 | 47.7 | 3.5 | 19.8 | 3.5 | 0 | 0 |
| 2 | July 20–Sep 20 | 27 | 27 | 29.6 | 0 | 37.0 | 44.4 | 22.2 | 18.5 | 0 | 0 |
| 3 | Apr 20–Mar 21 | 130 | 138 | 22.5 | 0 | 45.7 | 6.5 | 10.9 | 0 | 2.9 | 10.9 |
| 4 | June 20–Jan 21 | 32 | 58 | 44.8 | 0 | 82.8 | 69.0 | 44.8 | 15.5 | 0 | 0 |
| 5 | May 20–July 20 | 219 | 173 | 37.6 | 2.3 | 99.4 | 5.2 | 48.0 | 93.6 | 0.6 | 2.3 |
| 6 | June 20–Dec 20 | 62 | 120 | 49.2 | 5.8 | 77.5 | 3.3 | 32.5 | 59.2 | 0.8 | 13.3 |
| 7 | Apr 20–Dec 20 | 334 | 324 | 30.9 | 2.2 | 36.7 | 2.5 | 12.4 | 0.9 | 11.8 | 7.1 |
| 8 | May 20–Dec 20 | 117 | 208 | 42.3 | 2.7 | 24.0 | 6.7 | 21.6 | 0 | 0 | 3.8 |
| 9 | Apr 20–Dec 20 | 164 | 160 | 62.5 | 6.4 | 94.4 | 5.6 | 47.5 | 67.5 | 3.8 | 40.0 |
| Total | – | 1,137 | 1,294 | 39.9 | 2.9 | 57.7 | 8.4 | 26.8 | 27.9 | 3.9 | 10.0 |
qSOFA = quick Sequential Organ Failure Assessment.
The mean age of patients was 52.8 years (SD: 16.5 years), and 32.1% were females. In addition, 6.1% of patients had a history of ischemic heart disease, one-third had a history of hypertension, approximately one-third had a history of diabetes mellitus, 10.0% had a history of chronic respiratory disease, and 12.2% were current smokers (Table 2). Only half were diagnosed as COVID-19 cases before admission to the hospital; the rest were diagnosed after admission to the hospital. Breathlessness was the most common symptom at the time of admission (53.1%). An antibiotic was prescribed for 73.7% of patients, whereas antiviral medications were prescribed for only 16.2% of patients. Hydroxychloroquine was prescribed for 24.8% of patients, and steroids were prescribed for 47.2% of patients. In addition, 38.6% of patients were put on mechanical ventilation, and 41.9% were admitted to the ICU (Table 2).
Demographic and clinical characteristics of patients who died as a result of COVID-19 and their unmatched controls in nine hospitals in India
| Characteristics | Total (N = 2,431) | Cases (N = 1,137) | Controls (N = 1,294) | P value |
|---|---|---|---|---|
| Mean (SD) age (years) | 52.8 (16.5) | 59.3 (15.1) | 47.1 (15.6) | < 0.001 |
| Age group (years) | ||||
| 18–30 | 303 (12.5) | 58 (5.1) | 245 (18.9) | < 0.001 |
| 31–45 | 533 (21.9) | 155 (13.6) | 378 (29.2) | – |
| 46–59 | 651(26.8) | 303 (26.7) | 348 (26.9) | – |
| 60–74 | 712 (29.3) | 442 (38.9) | 270 (20.9) | – |
| ≥ 75 | 232 (9.5) | 179 (15.7) | 53 (4.1) | – |
| Sex | < 0.001 | |||
| Male | 1,650 (67.9) | 812 (71.4) | 838 (64.8) | – |
| Female | 781 (32.1) | 325 (28.6) | 456 (35.2) | – |
| Preexisting morbidities or risk factors | ||||
| Coronary artery disease | 149 (6.1) | 98 (8.4) | 53 (4.1) | < 0.001 |
| Hypertension | 821 (33.8) | 524 (46.1) | 297 (23.0) | < 0.001 |
| Stroke | 39 (1.6) | 28 (2.5) | 11 (0.9) | 0.002 |
| Diabetes mellitus | 854 (35.1) | 541 (47.6) | 313 (24.2) | < 0.001 |
| Any malignancy | 76 (3.1) | 46 (4.1) | 30 (2.3) | 0.015 |
| Chronic respiratory disease | 242 (10.0) | 195 (17.1) | 47 (3.6) | < 0.001 |
| Pulmonary tuberculosis | 74 (3.0) | 45 (4.0) | 29 (2.2) | 0.014 |
| Immune disorder | 35 (1.4) | 30 (2.6) | 5 (0.4) | < 0.001 |
| Smoking | 296 (12.2) | 167 (14.7) | 129 (10.0) | < 0.001 |
| Admitted as COVID-19 case | 1,164 (47.9) | 368 (32.4) | 796 (61.5) | < 0.001 |
| Presenting symptoms at admission | ||||
| Fever | 1,137 (46.8) | 610 (53.7) | 527 (40.7) | < 0.001 |
| Cough | 1,094 (45.0) | 574 (50.2) | 525 (40.5) | < 0.001 |
| Breathlessness | 1,290 (53.1) | 880 (77.5) | 410 (31.7) | < 0.001 |
| Altered mental status | 187 (7.7) | 167 (14.7) | 20 (1.5) | < 0.001 |
| Clinical status at admission | ||||
| Mean respiratory rate (SD)* | 25.7 (14.9) | 26.7 (11.0) | 24.8 (17.6) | 0.003 |
| Mean Spo2 (SD)† | 91.7 (11.3) | 86.7 (13.9) | 96.2 (4.9) | < 0.001 |
| Mean SBP (SD)‡ | 128.2 (23.2) | 129.8 (26.8) | 126.8 (19.3) | 0.002 |
| qSOFA score§ | ||||
| 0 | 807 (36.8) | 203 (19.5) | 604 (52.3) | – |
| 1 | 1,146 (52.2) | 629 (60.5) | 517 (44.8) | 0.001 |
| 2 | 231 (10.5) | 197 (18.9) | 34 (2.9) | – |
| 3 | 11 (0.5) | 11 (1.1) | 0 | – |
| Medications during hospital stay | ||||
| Antivirals | 394 (16.2) | 286 (25.2) | 109 (8.4) | < 0.001 |
| Steroids | 1,147 (47.2) | 800 (70.4) | 347 (26.8) | < 0.001 |
| Hydroxychloroquine | 603 (24.8) | 242 (21.3) | 361 (27.9) | < 0.001 |
| Intravenous immunoglobulin | 74 (3.0) | 65 (5.7) | 9 (0.7) | < 0.001 |
| Convalescent plasma | 99 (4.1) | 94 (8.3) | 5 (0.4) | < 0.001 |
| Respiratory or other clinical support | ||||
| High-flow nasal cannulation | 583 (24.0) | 476 (41.9) | 107 (8.3) | < 0.001 |
| Noninvasive ventilation | 558 (23.0) | 541 (47.6) | 17 (1.3) | < 0.001 |
| Invasive ventilation | 939 (38.6) | 889 (78.2) | 50 (3.9) | < 0.001 |
| ECMO | 5 (0.2) | 4 (0.4) | 1 (0.1) | 0.136 |
| Dialysis | 102 (4.2) | 84 (7.4) | 18 (1.4) | < 0.001 |
| Admitted to intensive care unit | 1,018 (41.9) | 888 (78.2) | 130 (10.1) | < 0.001 |
| Associated complications | ||||
| Sepsis | 409 (16.8) | 400 (35.2) | 9 (0.7) | < 0.001 |
| Coagulopathy | 145 (6.0) | 99 (8.7) | 46 (3.6) | < 0.001 |
| Acute renal injury | 348 (14.3) | 337 (29.6) | 11 (0.9) | < 0.001 |
| Stroke | 40 (1.6) | 40 (3.5) | 0 | < 0.001 |
| Arrythmia | 72 (3.0) | 72 (6.3) | 0 | < 0.001 |
| Myocarditis | 23 (1.0) | 23 (2.0) | 0 | < 0.001 |
ECMO = zxtracorporeal membrane oxygenation; qSOFA = quick Sequential Organ Failure Assessment; SBP = systolic blood pressure; Spo2 = oxygen saturation.
n Case: 1,059; control: 1,179.
n Case: 1,089; control: 1,217.
n Case: 1,080, control: 1,220.
n Case: 1,040; control: 1,155.
On bivariate analysis, cases were more likely than controls to be older (59.3 years versus 47.1 years) and male (71.4% versus 64.8%) and to have comorbidities (ischemic heart disease: 8.4% versus 4.1%; hypertension: 46.1% versus 23.0%; stroke: 2.5% versus 0.9%; diabetes mellitus: 47.6% versus 24.2%; chronic respiratory disease: 17.1% versus 3.6%; and an immune disorder: 2.6% versus 0.4%). Cases were less likely than controls to have been admitted with confirmed COVID-19 (32.4% versus 61.5%) and more likely to have a fever (53.7% versus 40.7%), and breathlessness (77.5% versus 31.7%). Cases also had a lower mean SpO2 than controls (86.7% versus 96.2%) at the time of admission. Cases were more likely than controls to receive antivirals (25.2% versus 8.4%) and steroids (70.4% versus 26.8%) but less likely to receive hydroxychloroquine (21.3% versus 27.9%), more likely to be put on invasive (78.2% versus 3.9%) or noninvasive (47.6% versus 1.3%) ventilation, and more likely to be admitted to the ICU (78.2% versus 10.1%). During treatment, cases were more likely than controls to develop sepsis (35.2% versus 0.7%), coagulopathy (8.7% versus 3.6%), acute renal injury (29.6% versus 0.9%), stroke (3.5% versus 0), and myocarditis (2.0% versus 0) (Table 2).
On multivariable analysis, the following variables were associated with mortality due to COVID-19: increasing age (adjusted odds ratio [aOR]: 46–59 years: 3.4 [95% CI: 1.5–7.7]), 60–74 years: (4.1 [95% CI: 1.7–9.5]), ≥ 75 years: (11.0 [95% CI: 4.0–30.6]); preexisting diabetes mellitus (aOR: 1.9 [95% CI: 1.2–2.9]); malignancy (aOR: 3.1 [95% CI: 1.3–7.8]); pulmonary tuberculosis (aOR: 3.3 [95% CI: 1.2–8.8]); breathlessness at the time of admission (aOR: 2.2 [95% CI: 1.4–3.5]); high qSOFA score at the time of admission (aOR: 5.6 [95% CI: 2.7–11.4]); and SpO2 < 94% at the time of admission (aOR: 2.5 [95% CI: 1.6–3.9]).
There was no difference in mortality by sex after adjustment for other variables. Administration of convalescent plasma or hydroxychloroquine during hospitalization did not affect the odds of mortality (Table 3). People who were put on mechanical ventilation (aOR: 32.6 [95% CI: 19.6–54.2]) or admitted to the ICU (aOR: 8.4 [95% CI: 5.5–12.9]) had very strong odds of mortality due to COVID-19.
Association between demographics, clinical characteristics, and mortality among COVID-19 patients in nine hospitals in India
| Characteristic | Unadjusted OR (95% CI of OR) | P value | Adjusted OR* (95% CI of OR) | P value |
|---|---|---|---|---|
| Age group (years) | ||||
| ≤ 30 | Ref | – | (Ref) | – |
| 31–45 | 1.7 (1.2–2.4) | 0.002 | 1.7 (0.8–3.9) | 0.197 |
| 46–59 | 3.6 (2.7–5.1) | < 0.001 | 3.8 (1.5–7.7) | 0.004 |
| 60–74 | 6.9 (5.0–9.6) | < 0.001 | 4.1 (1.7–9.5) | 0.001 |
| ≥ 75 | 14.3 (9.4–21.7) | < 0.001 | 11.0 (4.0–30.6) | < 0.001 |
| Sex | ||||
| Female | Ref | – | Ref | – |
| Male | 1.4 (1.1–1.6) | < 0.001 | 1.3 (0.9–2.0) | 0.183 |
| Preexisting morbidity | ||||
| Coronary artery disease | 2.2 (1.5–3.1) | < 0.001 | 0.5 (0.1–1.2) | 0.118 |
| Hypertension | 2.9 (2.4–3.4) | < 0.001 | 1.1 (0.7–1.7) | 0.716 |
| Stroke | 2.9 (1.5–5.9) | 0.003 | 0.7 (0.2–3.7) | 0.709 |
| Diabetes mellitus | 2.8 (2.4–3.4) | < 0.001 | 1.9 (1.2–2.9) | 0.004 |
| Any malignancy | 1.8 (1.1–2.8) | 0.016 | 3.1 (1.3–7.8) | 0.013 |
| Chronic respiratory disease | 5.5 (3.9–7.6) | < 0.001 | 0.8 (0.4–1.6) | 0.523 |
| Pulmonary tuberculosis | 1.8 (1.1–2.9) | 0.015 | 3.3 (1.2–8.7) | 0.018 |
| Immune disorder | 6.9 (2.7–18.1) | < 0.001 | 1.9 (0.3–11.9) | 0.478 |
| Admitted as COVID-19 case | 0.3 (0.2–0.4) | < 0.001 | 0.7 (0.5–1.0) | 0.068 |
| Presenting symptoms at admission | ||||
| Fever | 1.7 (1.4–2.0) | < 0.001 | 1.4 (0.9–2.1) | 0.093 |
| Cough | 1.5 (1.3–1.7) | < 0.001 | 0.8 (0.5–1.2) | 0.274 |
| Breathlessness | 7.4 (6.2–9.0) | < 0.001 | 2.2 (1.4–3.5) | < 0.001 |
| Clinical status at admission | ||||
| SpO2 (< 94%) | 9.4 (7.6–11.5) | < 0.001 | 2.5 (1.6–3.9) | < 0.001 |
| qSOFA | ||||
| Low (0–1) | Ref | – | Ref | – |
| High (2–3) | 8.2 (5.7–12.0) | < 0.001 | 5.6 (2.7–11.4) | < 0.001 |
| Medications during hospital stay | ||||
| Antivirals | 3.7 (2.9–4.7) | < 0.001 | 1.6 (0.9–2.8) | 0.111 |
| Steroid therapy | 6.4 (5.4–7.8) | < 0.001 | 1.3 (0.9–2.1) | 0.179 |
| Hydroxychloroquine | 0.7 (0.6–0.8) | < 0.001 | 0.6 (0.4–1.1) | 0.051 |
| Intravenous immunoglobulin | 8.6 (4.3–17.5) | < 0.001 | 1.6 (0.4–6.4) | 0.480 |
| Convalescent plasma | 23.2 (9.4–57.3) | < 0.001 | 3.7 (0.9–15.8) | 0.074 |
OR = odds ratio; qSOFA = quick Sequential Organ Failure Assessment; Ref = reference category; SpO2 = oxygen saturation.
Adjusted for age, sex, comorbidities, presenting symptoms, SpO2, and qSOFA score at admission, admission as a Covid patient, intensive care unit admission, invasive mechanical ventilation, and associated complications during hospital stay and hospital where cases were recruited.
DISCUSSION
In this hospital-based case-control study of predictors of mortality due to COVID-19, we found that older age, preexisting diabetes mellitus, pulmonary tuberculosis or malignancy, difficulty in breathing at admission, and low SpO2 and high qSOFA score at time of admission were associated with higher odds of death. Patients with COVID-19 who were 75 years or older were 11 times more likely to die than those younger than 30 years. To the best of our knowledge, this is one of the first multicentric studies to estimate predictors of COVID-19 death in India with a robust sample size.
We found that increasing age was associated with increased odds of mortality due to COVID-19. This is consistent with evidence from elsewhere. A multicentric cohort study among adults 18 years or older in 10 countries of Africa found that every increasing year increased the odds of death due to COVID-19 by 3%.14 A similar association was reported in a meta-analysis as well in studies conducted in other countries.6,7,9,14–18 We did not find any association between the sex of the patient and the odds of mortality due to COVID-19. Available evidence from elsewhere in this regard is conflicting. Although two meta-analyses and some other studies found that males were more likely to die as a result of COVID-19 in-hospital,6,7,17,19 other studies did not find any association.9,14,20 Another study found a significant association between male sex and in-hospital mortality due to COVID-19 only in those older than 65 years.18
We found that poor clinical status at the time of hospital admission was associated with increased odds of mortality. The qSOFA score, which combines low systolic blood pressure, altered mental status, and raised respiratory rate, was a strong predictor of mortality due to COVID-19 in our study. We found that those with a qSOFA score of 2 and above were five times more likely to die as a result of COVID-19, which was consistent with other studies.14,20 An SpO2 value ≤ 94% at the time of admission was also associated with higher odds of mortality; similar results were found in other studies as well.9,17,21 We found a strong association between difficulty breathing at the time of admission and mortality, although the presence of a cough and fever was not significantly associated. Most of the studies conducted across different settings found a positive association between breathlessness at the time of admission and mortality8,16,17,21; however, evidence regarding cough is conflicting. While many studies have found cough to be a protective factor in COVID-19 mortality,8,17 we could not find any such association. Most of the studies did not find any association between fever and mortality due COVID-19.7,8,16
Among the preexisting morbidities that we examined, diabetes mellitus, malignancy, and pulmonary tuberculosis were found to be independent predictors of mortality due to COVID-19. Studies have found an association between multi-morbidity and deaths due to COVID-19.22 Although patients with diabetes mellitus were almost twice as likely as those without diabetes mellitus to die as a result of COVID-19, those with any malignancy were three times more likely to die. Most of the available evidence suggests that diabetes mellitus is one of the most important predictors of in-hospital COVID-19 mortality,6,8,12,14,23 whereas evidence regarding malignancy and other morbidities is conflicting. Although some studies found an association between malignancy and COVID-19 mortality,15,17,23 others did not find any association.9,14 One study found an association only among males.18 We did not find any association between COVID-19 mortality and ischemic heart disease, hypertension, chronic respiratory disease, or stroke.
We found that use of hydroxychloroquine was not associated with reduced odds of mortality due to COVID-19. Although some observational studies have reported protection with use of hydroxychloroquine,9 most trials did not find any effect of hydroxychloroquine on reducing 28-day mortality.10,11,24 We did not find any association between steroid use and mortality due to COVID-19. A large trial found that dexamethasone reduced 28-day mortality from COVID-19 among those receiving oxygen support or on mechanical ventilation, but not among those not receiving any respiratory support.25 However, even after we restricted our analysis to only those patients receiving respiratory support, steroid therapy was not associated with reduced odds of mortality. We were unable to collect information on type or duration of steroid therapy; however, methylprednisolone was the most common steroid used. We were also unable to collect information on duration of use of other medications. However, we found that convalescent plasma therapy did not provide any survival benefit. Most trials did not find any association between therapy with convalescent plasma and protection from mortality due to COVID-19.26,27
A similar study conducted in Tamil Nadu (South India) found a higher mortality rate among patients aged 40–70 years, with the highest rate among diabetic patients with elevated urea levels.28 The aORs of significant factors in the multivariable logistic regression were SpO2 < 95%: 2; age ≥ 50 years: 2.52; pulse rate ≥ 100/minute: 2.02; and coexisting diabetes mellitus: 1.73, with hypertension and gender not retaining their significance. These results are similar to those of the current study in relation to the variables SpO2 (95/94%), age, and preexisting diabetes mellitus.
Strengths and limitations of the study.
This study was conducted in multiple hospital settings with a robust sample size. However, there are some limitations. As is true for any study based on retrospective extraction of patient case records, completeness was a major limitation. The study did not capture risk factors such as day of illness at admission, severity of illness (moderate/severe), or laboratory parameters, as well as details of treatment such as dose and duration. However, we used indicators for severity both at the time of admission (e.g., qSOFA score) and during the period of hospitalization (e.g., need for ventilatory support, ICU admission, and associated complications in the hospital) to adjust our analyses. We did not impute any data on signs and symptoms, but preexisting morbidities or treatment modalities during hospitalization were considered positive only when they were explicitly mentioned in the case record. The ratio of cases to controls in the study institutions was variable. The emergence of newer data during the study period led to a change in standard practice at the institutions in the study period, which may have affected the comparisons.
CONCLUSION
In this hospital-based case-control study, we found that older age, preexisting diabetes mellitus, pulmonary tuberculosis or malignancy, difficulty in breathing, low SpO2 and general condition as determined by qSOFA score at the time of admission, and admission to the ICU were significant predictors of mortality among hospitalized patients with COVID-19. Prioritizing patients at increased risk of death and rationalizing therapy will help reduce mortality due to COVID-19 during the ongoing pandemic.
ACKNOWLEDGMENTS
We acknowledge the support provided by the medical record technicians and staff working in the COVID-19 wards of participating hospitals. The American Society of Tropical Medicine and Hygiene (ASTMH) has waived the Open Access fee for this article due to the ongoing COVID-19 pandemic and has assisted with publication expenses.
REFERENCES
- 1.↑
World Health Organization , 2023. The True Death Toll of COVID-19: Estimating Global Excess Mortality. Available at: https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality.
- 2.↑
Center for System Science and Engineering, 2022. Johns Hopkins University , Coronavirus COVID-19 Dashboard. Available at: https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6. Accessed November 10, 2022.
- 3.↑
Cao Y , Hiyoshi A , Montgomery S , 2010. Covid-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data. BMJ Open 10: e043560.
- 4.↑
Jain VK , Iyengar K , Vaish A , Vaishya R , 2020. Differential mortality in COVID-19 patients from India and western countries. Diabetes Metab Syndr 14: 1037–1041.
- 5.↑
Chidambaram V et al.2020. Factors associated with disease severity and mortality among patients with COVID-19: a systematic review and meta-analysis. PLoS One 15: e0241541.
- 6.↑
Dorjee K , Kim H , Bonomo E , Dolma R , 2020. Prevalence and predictors of death and severe disease in patients hospitalized due to COVID-19: a comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients. PLoS One 15: e0243191.
- 7.↑
Tian W , Jiang W , Yao J , Nicholson CJ , Li RH , Sigurslid HH , Wooster L , Rotter JI , Guo X , Malhotra R , 2020. Predictors of mortality in hospitalized COVID‐19 patients: a systematic review and meta‐analysis. J Med Virol 92: 1875–1883.
- 8.↑
Mesas AE , Cavero-Redondo I , Alvarez-Bueno C , Cabrera MAS , deAndrade SM , Sequi-Dominguez I , 2020. Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS One 15: e0241742.
- 9.↑
Arshad S et al.2020. Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19. Int J Infect Dis 97: 396–403.
- 10.↑
RECOVERY Collaborative Group ; Horby P et al.2020. Effect of hydroxychloroquine in hospitalized patients with Covid-19. N Engl J Med 383: 2030–2040.
- 11.↑
WHO Solidarity Trial Consortium, 2022 Remdisivir and three other drugs for hospitalized patients with COVID-19: final results of the WHO solidarity randomized trial and updated meta-analyses. Lancet 399: 1941–1953.
- 12.↑
Agarwal N , Biswas B , Lohani P , 2020. Epidemiological determinants of COVID-19 infection and mortality: a study among patients presenting with severe acute respiratory illness during the pandemic in Bihar, India. Niger Postgrad Med J 27: 293–301.
- 13.↑
Gupta N , Ish P , Kumar R , Dev N , Yadav SR , Malhotra N , Agrawal S , Gaind R , Sachdeva H ; other members of the Safdarjung Hospital Covid Working Group , 2020. Evaluation of the clinical profile, laboratory parameters and outcome of two hundred COVID-19 patients from a tertiary centre in India. Monaldi Arch Chest Dis 9: 33169598.
- 14.↑
African COVID-19 Critical Care Outcomes Study (ACCCOS) Investigators , 2021. Patient care and clinical outcomes for patients with COVID-19 infection admitted to African high-care or intensive care units (ACCCOS): a multicentre, prospective, observational cohort study. Lancet 397: 1885–1894.
- 15.↑
Bellan M , Patti G , Hayden E , Azzolina D , Pirisi M , Acquaviva A , 2020. Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID‐19 patients. Sci Rep 10: 20731.
- 16.↑
Bepouka BI et al.2020. 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 37: 105.
- 17.↑
Berenguer J et al.2020. Characteristics and predictors of death among 4035 consecutively hospitalized patients with COVID-19 in Spain. Clin Microbiol Infect 26: 1525–1536.
- 18.↑
Castelnuovo AD et al.2020. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study. Nutr Metab Cardiovasc Dis 30: 1899–1913.
- 19.↑
Jaspard M et al.2021. Clinical presentation, outcomes and factors associated with mortality: a prospective study from three COVID-19 referral care centres in West Africa. Int J Infect Dis 108: 45–52.
- 20.↑
Bhargava A , Sharma M , Akagi E , Szpunar SM , Saravolatz L , 2021. Predictors for in-hospital mortality from coronavirus disease 2019 (COVID-19) infection among adults aged 18–65 years. Infect Control Hosp Epidemiol 42: 775–775.
- 21.↑
Bairwa M , Kumar R , Ajmal M , Bahurupi Y , Kant R , 2021. Predictors of critical illness and mortality based on symptoms and initial physical examination for patients with SARS-CoV-2: a retrospective cohort study. J Infect Public Health 14: 1028–1034.
- 22.↑
Imam Z , Odish F , Gill I , O’Connor D , Armstrong J , Vanood A , Ibironke O , Hanna A , Ranski A , Halalau A , 2020. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan, United States. J Intern Med 288: 469–476.
- 23.↑
Corona G , Pizzocaro A , Vena W , Rastrelli G , Semeraro F , Isidori AM , Pivonello R , Salonia A , Sforza A , Maggi M , 2021. Diabetes is most important cause for mortality in COVID-19 hospitalized patients: systematic review and meta-analysis. Rev Endocr Metab Disord 22: 275–296.
- 24.↑
WHO Solidarity Trial Consortium ; Pan H et al.2021. Repurposed antiviral drugs for Covid-19: interim WHO Solidarity Trial results. N Engl J Med 384: 497–511.
- 25.↑
RECOVERY Collaborative Group ; Horby P et al.2021. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med 384: 693–704.
- 26.↑
RECOVERY Collaborative Group , 2021. Convalescent plasma in patients admitted to hospital with Covid-19 (RECOVERY): a randomized controlled, open-label, platform trial. Lancet 397: 2049–2059.
- 27.↑
Agarwal A , Mukherjee A , Kumar G , Chatterjee P , Bhatnagar T , Malhotra P ; PLACID Trial Collaborators , 2020. Convalescent plasma in the management of moderate Covid-19 in adults in India: open label phase II multicentre randomized controlled trial (PLACID Trial). BMJ 371: m3939. Erratum in: BMJ 2020;371.
- 28.↑
Gopalan N et al., 2022. Predictors of mortality among hospitalized COVID-19 patients and risk score formulation for prioritizing tertiary care—an experience from south India. PLOS One. 17: e0263471.