Risk factors for the development of severe COVID-19 disease and death have been widely reported across several studies. Knowledge about the determinants of severe disease and mortality in the Indian context can guide early clinical management.
We conducted a hospital-based case control study across nine sites in India to identify the determinants of severe and critical COVID-19 disease.
We identified age above 60 years, duration before admission >5 days, chronic kidney disease, leucocytosis, prothrombin time > 14 sec, serum ferritin >250 ng/mL, d-dimer >0.5 ng/mL, pro-calcitonin >0.15 μg/L, fibrin degradation products >5 μg/mL, C-reactive protein >5 mg/L, lactate dehydrogenase >150 U/L, interleukin-6 >25 pg/mL, NLR ≥3, and deranged liver function, renal function and serum electrolytes as significant factors associated with severe COVID-19 disease.
We have identified a set of parameters that can help in characterising severe COVID-19 cases in India. These parameters are part of routinely available investigations within Indian hospital settings, both public and private. Study findings have the potential to inform clinical management protocols and identify patients at high risk of severe outcomes at an early stage.
Citation: Zodpey SP, Negandhi H, Kamal VK, Bhatnagar T, Ganeshkumar P, Athavale A, et al. (2021) Determinants of severity among hospitalised COVID-19 patients: Hospital-based case-control study, India, 2020. PLoS ONE 16(12): e0261529. https://doi.org/10.1371/journal.pone.0261529
Editor: Raffaele Serra, University Magna Graecia of Catanzaro, ITALY
Received: April 28, 2021; Accepted: December 3, 2021; Published: December 29, 2021
Copyright: © 2021 Zodpey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: The study was funded by the Indian Council for Medical Research under RFC No. ECD/NTF/8/2020-21/COVID dated 10.07.2020. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." We note that one or more of the authors are employed by a commercial company: Independent consultant Dr. DCS Reddy is an independent consultant and had neither commercial nor competing interest. He contributed to designing, technical review of the study methods and review of the manuscript. We have mentioned this in the revised manuscript.
Competing interests: The authors have declared that no competing interests exist.
COVID-19 pandemic has caused over 2.4 million deaths and over 111 million cases worldwide by 24th February 2021 . Due to widespread transmission, several countries were burdened with high case load and deaths. Critical care resources have been stretched across some countries [2, 3]. The fatalities reported by countries and regions also varied widely.
While the available data on absolute number of deaths is fairly reliable, the calculation of mortality rates and comparing them across countries is difficult because countries widely differ in their screening and testing criteria. The analysis of 72,314 cases using data from the Chinese Centre for Disease Control and Prevention , indicated most cases to be mild (81%; i.e., non-pneumonia and mild pneumonia), whereas 14% were severe (i.e., dyspnea, respiratory frequency ≥30/min, blood oxygen saturation ≤93%, the partial pressure of arterial oxygen to fraction of inspired oxygen ratio <300, and/or lung infiltrates >50% within 24 to 48 hours), and 5% were critical (i.e., respiratory failure, septic shock, and/or multiple organ dysfunction or failure). India rapidly scaled up hospital and critical care resources and a proactive public health response targeting surveillance, wearing masks, limiting movement in the early phase of the epidemic along with an intensive information dissemination campaign. The mortality attributed to COVID-19 in India was relatively low compared to the rest of the world. India had reported 1,19,71,624 cases and 1,61,552 deaths till 28 March 2021 with lowest case fatality ratio of 1.5% globally .
The determinants of severity can guide clinical management; proactively screening for their presence could prioritize COVID-19 patients for intensive care treatment and thereby allocate scarce medical resources appropriately. Risk factors for the development of severe disease and death have been widely reported across several studies , and vulnerable groups include older adults, cardiovascular disease, diabetes, chronic respiratory disease, hypertension, and cancer. Obesity and smoking were also associated with increased risks in some studies . Lymphopenia is a predictor of disease progression . Cytokine storm is also associated with disease severity .
Knowledge of characteristics of people at high risk of experiencing a poor outcome from the infection could help in care provision . We conducted this study to identify the determinants of severe COVID-19 disease in India using a case-control study design.
Study design and setting
We did a hospital-based case-control study among laboratory-confirmed COVID-19 patients of age≥18 years, newly admitted to nine designated COVID-19 hospitals (both public and private), from six cities across India during September—November 2020. The study centres included BJ Medical College, Ahmedabad; Care Institute of Medical Sciences (CIMS Hospital), Ahmedabad; Gandhi Medical College, Bhopal; Chirayu Medical College & Hospital, Bhopal; Symbiosis Hospital and Research Centre, Pune; Rajiv Gandhi Medical College and CSMH, Kalwa, Thane; Madras Medical College, Chennai; Chengalpattu Medical College, Chengalpattu; and Datta Meghe Medical College, Wanadongari, Nagpur.
Cases (severe disease, at admission) and controls (mild disease at admission) were defined as per the Government of India’s COVID-19 case management guidelines (version 5; issued on 03/07/2020) (S1 Table) . The working definition of severe COVID-19 disease included death and/or development of severe disease requiring ICU admission and/or ventilator support.
Assuming an exposure rate of risk factors as 9% among controls (prevalence of hypertension in India) , anticipated Odds Ratio (OR) of 2.3 , at 5% level of significance and 90% power, we estimated a sample size of 244 cases and controls, each.
Selection of study participants
Cases and controls were identified from the admission records of study hospitals and those found to fulfil the eligibility criteria were selected consecutively until the desired sample size was achieved.
We did face-to face interviews with the patients using a structured questionnaire to collect data on socio-demographic details, concurrent disease conditions and clinical symptomatology. For concurrent disease conditions questions were included about duration, severity and medication. If the patient was unable to respond, the close family members of the patient were interviewed. Data pertaining to clinical and laboratory variables were extracted from the hospital records using a data abstraction form. All information pertained to the duration between development of symptoms and the time of admission of the patients in the study hospitals.
Categorical and continuous variables were represented as frequency (percentage) and median (Interquartile range (IQR)), respectively. Between cases and controls, categorical variables were compared using Chi-square/Fisher’s exact test, whichever applicable. Non-normally distributed continuous variables (examined using Shapiro-Wilk test) were compared using the Wilcoxon rank-sum test. The quantification of association was represented as crude and adjusted odds ratios with 95% confidence intervals (CI) using simple and multiple logistic regression analysis, respectively. Factors with p-value <0.25 in simple logistic regression analysis and/or clinical relevance, with the exclusion of those operating through a common clinical pathway or indicating similar pathology, were selected for the inclusion in the final model based on multiple logistic regression analysis, after checking for collinearity using variance inflation factor (VIF). Each factor was adjusted for relevant and measured confounders identified using directed acyclic graphs and -2 log likelihood ratio test. Data analysis was done using Stata V.15.1 software.
We included 244 patients with severe COVID-19 disease (Cases) and 245 with mild to moderate COVID-19 disease (Controls). Compared to the controls, a significantly higher proportion of cases were more than 60 years old, had lower monthly household income, less educated, and possessed a below poverty line (BPL) card. (Table 1).
The most common symptoms at admission were fever, shortness of breath, cough and myalgia. A significantly higher proportion of cases reported cough and presented with hypertension, diabetes mellitus and chronic kidney disease. Cases also had a significantly higher proportion of multiple comorbidities compared to the controls. (Table 2).
A significantly higher proportion of cases compared to controls had abnormal laboratory parameters at the time of admission, except for blood group, creatinine kinase and vitamin D. (Table 3).
On univariate analysis, age of 60 years and above, duration before admission more than five days, diabetes mellitus, hypertension, chronic kidney disease, leucocytosis, elevated levels of erythrocyte sedimentation rate, prothrombin time, serum ferritin, d-dimer, pro-calcitonin, fibrin degradation products, c-reactive protein, lactate dehydrogenase, interleukin-6, neutrophil lymphocyte ratio (NLR) and deranged liver function tests, renal function tests and serum electrolytes were associated with severe COVID-19 disease. After adjusting for known confounders, factors associated with severe COVID-19 were age above 60 years, duration before admission >5 days, pre-existing diabetes, chronic kidney disease, leucocytosis, prothrombin time > 14 sec, serum ferritin >250 ng/mL, d-dimer >0.5 ng/mL, pro-calcitonin >0.15 μg/L, fibrin degradation products >5 μg/mL, C-reactive protein >5 mg/L, lactate dehydrogenase >150 U/L, interleukin-6 >25 pg/mL, NLR ≥3, and deranged liver function, renal function and serum electrolytes. (Table 4).
We identified older age, co-morbidities (diabetes, chronic kidney disease) and laboratory parameters (leucocyte count, prothrombin time, serum ferritin, d-dimer, pro-calcitonin, fibrin degradation products, lactate dehydrogenase, neutrophil lymphocyte ratio, C-reactive protein, interleukin-6, liver function, renal function and serum electrolytes) as determinants of severe disease at the time of admission among COVID-19 patients.
Diabetes has been recognized as important in the prediction of severe disease of COVID-19. Diabetes in patients with COVID-19 was associated with a two-fold increase in mortality and severity of COVID-19, compared to non-diabetics in a meta-analysis . Jain et al., studied the predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission. They concluded that elderly patients with comorbidities are more vulnerable to severe disease . A systematic review by Del Sole et al included 12 studies with 2794 patients where 596 patients with severe disease. They reported patients with severe disease were older in age and had diabetes than patients with non-severe disease .
Del Sole identified that increased procalcitonin (OR: 8.21, 95% CI 4.48–15.07), increased D-Dimer (OR: 5.67, 95% CI 1.45–22.16) and thrombocytopenia (OR: 3.61, 95% CI 2.62–4.97) predicted severe infection . A meta-analysis by Coomes and Haghbayan  reported that IL-6 levels are significantly elevated and associated with adverse clinical outcomes.
A study in a north Indian tertiary care centre used retrospective data to conclude that more than half of patients admitted to the hospital with SARS-CoV-2 infection had an abnormal liver function which was found to be associated with raised levels of inflammatory markers . These patients had significantly higher proportions of patients with abnormal liver function were elderly and males and were at higher risk of progressing to severe disease. Organ specific manifestations, which include the liver and the kidney along with their possible mechanism of injury have been available in literature . A systematic review and meta-analysis of the published studies indicate that COVID-19 incidence was higher in people receiving maintenance dialysis than in those with CKD not requiring kidney replacement therapy or those who were kidney or pancreas/kidney transplant recipients . In patients with COVID-19, acute Kidney Injury (AKI) may have an inflammatory etiology mediated by a cytokine storm . CKD and COVID-19 may have a higher incidence of death than people with CKD without COVID-19. 
Elevated levels of lactate dehydrogenase were suggested to be associated right from the early studies on COVID-19 severity. Work by Wang and Wang reported that compared to survival cases, patients who died during hospitalization had higher plasma levels of D-dimer, creatinine, creatine kinase, lactate dehydrogenase, lactate, and lower percentage of lymphocytes (LYM [%]), platelet count and albumin levels . Similarly, a multicentre retrospective cohort study from Wuhan to develop and validate a prognostic nomogram for predicting in-hospital mortality of COVID-19 included age (Hazard Ratio for per year increment: 1.05), severity at admission (Hazard Ratio for per rank increment: 2.91), dyspnea (Hazard Ratio: 2.18), cardiovascular disease (Hazard Ratio: 3.25), and levels of lactate dehydrogenase (Hazard Ratio: 4.53), total bilirubin (Hazard Ratio: 2.56), blood glucose (Hazard Ratio: 2.56), and urea (Hazard Ratio: 2.14) .
Other parameters that we found to be associated with severe Covid-19 at admission such as leucocytosis, prothrombin time, serum ferritin, fibrin degradation products, C-reactive protein, interleukin-6, and serum electrolytes operate through a clinical pathway or indicate pathology similar to others described above.
Our study had certain limitations. There is potential for selection bias in this hospital-based study. The cases were poorer and less educated than the controls, which indicates a difference in the source population to which cases and controls belonged to. The location and type of participating hospitals could have influenced the selection of study participants. Misclassification of case-control status is unlikely as we used the standardized criteria for classification of severe cases across the study sites. There is a likelihood of misclassification of laboratory parameters, albeit minimal, on account of testing by different laboratories across the study sites. However, all laboratories were assured to have quality control mechanisms in place.
We have identified a set of parameters characterizing severe Covid-19 that are part of routinely available investigations within Indian hospital settings, both public and private. Knowledge of these risk factors has the potential to triage COVID-19 patients at the time of admission in terms of severity of disease and adequate management of the same.
We acknowledge the role of the Epidemiology and Surveillance Working Group of the ICMR, constituted by the COVID-19 National Task Force of Government of India, for its review of the protocol and project implementation.
- 1. World Health Organization. WHO COVID-19 Dashboard [Internet]. [cited 2021 Feb 26]. https://covid19.who.int/
- 2. Xie J, Tong Z, Guan X, Du B, Qiu H, Slutsky AS. Critical care crisis and some recommendations during the COVID-19 epidemic in China. Intensive Care Med [Internet]. 2020 Mar 2 [cited 2020 Apr 19]; pmid:32123994
- 3. Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. JAMA [Internet]. 2020 Mar 13 [cited 2020 Apr 19]; https://jamanetwork.com/journals/jama/fullarticle/2763188 pmid:32167538
- 4. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020 Apr 7;323(13):1239–42. pmid:32091533
- 5. Ministry of Health and Family Welfare. COVID-19 updates [Internet]. 2021. https://www.mohfw.gov.in/
- 6. Jordan RE, Adab P, Cheng KK. Covid-19: risk factors for severe disease and death. BMJ [Internet]. 2020 Mar 26 [cited 2020 Apr 19];368. Available from: https://www.bmj.com/content/368/bmj.m1198 pmid:32217618
- 7. Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang Y-Q, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduction and Targeted Therapy. 2020 Mar 27;5(1):1–3.
- 8. Mehta P, McAuley DF, Brown M, Sanchez E, Tattersall RS, Manson JJ. COVID-19: consider cytokine storm syndromes and immunosuppression. The Lancet. 2020 Mar 28;395(10229):1033–4. pmid:32192578
- 9. Wynants L, Calster BV, Bonten MMJ, Collins GS, Debray TPA, Vos MD, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ [Internet]. 2020 Apr 7 [cited 2020 Apr 19];369. Available from: https://www.bmj.com/content/369/bmj.m1328 pmid:32265220
- 10. Government of India. Clinical Management Protocol: COVID-19 [Internet]. [cited 2020 Aug 3]. https://www.mohfw.gov.in/pdf/UpdatedClinicalManagementProtocolforCOVID19dated03072020.pdf
- 11. National Family Health Survey [Internet]. [cited 2020 Apr 30]. http://rchiips.org/NFHS/factsheet_NFHS-4.shtml
- 12. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis | Elsevier Enhanced Reader [Internet]. [cited 2020 Apr 30]. https://reader.elsevier.com/reader/sd/pii/S1201971220301363?token=84CB3A88DA633DDF31C6BDADB6F56B3687501FA682E6E90B1F5976E2695220D1FE10AC0A8FE4C69122040737CF674E69
- 13. Kumar A, Arora A, Sharma P, Anikhindi SA, Bansal N, Singla V, et al. Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis. Diabetes Metab Syndr. 2020 Aug;14(4):535–45. pmid:32408118
- 14. Jain V, Yuan J-M. Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int J Public Health. 2020 Jun;65(5):533–46. pmid:32451563
- 15. Del Sole F, Farcomeni A, Loffredo L, Carnevale R, Menichelli D, Vicario T, et al. Features of severe COVID-19: A systematic review and meta-analysis. Eur J Clin Invest. 2020 Oct;50(10):e13378. pmid:32860457
- 16. Coomes EA, Haghbayan H. Interleukin-6 in Covid-19: A systematic review and meta-analysis. Rev Med Virol. 2020 Nov;30(6):1–9. pmid:32845568
- 17. Saini RK, Saini N, Ram S, Soni SL, Suri V, Malhotra P, et al. COVID-19 associated variations in liver function parameters: a retrospective study. Postgraduate Medical Journal [Internet]. 2020 Nov 12 [cited 2021 Feb 12]; https://pmj.bmj.com/content/early/2020/11/12/postgradmedj-2020-138930 pmid:33184141
- 18. Gavriatopoulou M, Korompoki E, Fotiou D, Ntanasis-Stathopoulos I, Psaltopoulou T, Kastritis E, et al. Organ-specific manifestations of COVID-19 infection. Clin Exp Med. 2020 Nov 1;20(4):493–506. pmid:32720223
- 19. Chung EYM, Palmer SC, Natale P, Krishnan A, Cooper TE, Saglimbene VM, et al. Incidence and Outcomes of COVID-19 in People With CKD: A Systematic Review and Meta-analysis. Am J Kidney Dis. 2021 Aug 5;S0272-6386(21)00771-X. pmid:34364906
- 20. Gagliardi I, Patella G, Michael A, Serra R, Provenzano M, Andreucci M. COVID-19 and the Kidney: From Epidemiology to Clinical Practice. Journal of Clinical Medicine. 2020 Aug;9(8):2506. pmid:32759645
- 21. Wang Z, Wang Z. Identification of risk factors for in-hospital death of COVID—19 pneumonia—lessions from the early outbreak. BMC Infect Dis. 2021 Jan 25;21(1):113. pmid:33494706
- 22. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996 Dec;49(12):1373–9. pmid:8970487