Article Text

Original research
Factors associated with mortality among moderate and severe patients with COVID-19 in India: a secondary analysis of a randomised controlled trial
  1. Joy John Mammen1,
  2. Snehil Kumar1,
  3. Lovely Thomas2,
  4. Gunjan Kumar3,
  5. Anand Zachariah4,
  6. Lakshmanan Jeyaseelan5,
  7. John Victor Peter2,
  8. Anup Agarwal3,
  9. Aparna Mukherjee3,
  10. Pranab Chatterjee6,
  11. Tarun Bhatnagar7,
  12. Jess Elizabeth Rasalam1,
  13. Binila Chacko2,
  14. Thenmozhi Mani5,
  15. Melvin Joy5,
  16. Priscilla Rupali8,
  17. Malathi Murugesan9,
  18. Dolly Daniel1,
  19. B Latha10,
  20. Sunita Bundas11,
  21. Vivek Kumar12,
  22. Ravi Dosi13,
  23. Janakkumar R Khambholja14,
  24. Rosemarie de Souza15,
  25. B Thrilok Chander16,
  26. Shalini Bahadur17,
  27. Simmi Dube18,
  28. Amit Suri19,
  29. Aikaj Jindal20,
  30. Om Shrivastav21,
  31. Vijay Barge22,
  32. Archana Bajpayee23,
  33. Pankaj Malhotra24,
  34. Neha Singh25,
  35. Muralidhar Tambe26,
  36. Nimisha Sharma27,
  37. Shreepad Bhat28,
  38. Ram S Kaulgud29,
  39. Anil Gurtoo30,
  40. D Himanshu Reddy31,
  41. Kamlesh Upadhyay32,
  42. Ashish Jain33,
  43. Tinkal C Patel34,
  44. Irfan Nagori35,
  45. Pramod R Jha36,
  46. K V Sreedhar Babu37,
  47. C Aparna38,
  48. Sunil Jodharam Panjwani39,
  49. M Natarajan40,
  50. Milind Baldi41,
  51. Vrushali Khirid Khadke42,
  52. Seema Dua43,
  53. Ravindraa Singh44,
  54. Ashish Sharma45,
  55. Jayashree Sharma46,
  56. Yojana A Gokhale47,
  57. Pragya D Yadav48,
  58. Gajanan Sapkal49,
  59. Himanshu Kaushal50,
  60. V Saravana Kumar51
  1. 1Transfusion Medicine and Immunohaematology, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  2. 2Medical Intensive Care Unit, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  3. 3Clinical Trials and Health Systems Research Unit, ICMR, New Delhi, Delhi, India
  4. 4Medicine, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  5. 5Biostatistics, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  6. 6Translational Global Health Policy and Research Cell, ICMR, New Delhi, Delhi, India
  7. 7ICMR School of Public Health, National Institute of Epidemiology, Chennai, Tamil Nadu, India
  8. 8Infectious Diseases, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  9. 9Hospital Infection Control Committee, Christian Medical College and Hospital Vellore, Vellore, Tamil Nadu, India
  10. 10Transfusion Medicine, Madras Medical College, Chennai, Tamil Nadu, India
  11. 11Transfusion Medicine, SMS Medical College and Hospital, Jaipur, Rajasthan, India
  12. 12Critical Care, Sir HN Reliance Foundation Hospital and Research Centre, Mumbai, Maharashtra, India
  13. 13Respiratory Medicine, Sri Aurobindo Institute of Medical Sciences, Indore, Madhya Pradesh, India
  14. 14Internal Medicine, Smt NHL Municipal Medical College, Ahmedabad, Gujarat, India
  15. 15Department of Medicine, BYL Nair Charitable Hospital, Mumbai, India
  16. 16Internal Medicine, Gandhi Medical College and Hospital, Secunderabad-Padmarao Nagar, Telangana, India
  17. 17Pathology, Government Institute of Medical Sciences, Noida, Uttar Pradesh, India
  18. 18Internal Medicine, Gandhi Medical College Bhopal, Bhopal, Madhya Pradesh, India
  19. 19Pulmonary Medicine, Atal Bihari Vajpayee Institute of Medical Sciences and Ram Manohar Lohia Hospital, New Delhi, Delhi, India
  20. 20Transfusion Medicine, Satguru Partap Singh Hospitals, Ludhiana, Punjab, India
  21. 21Infectious Diseases, Kasturba Hospital for Infectious Diseases, Mumbai, Maharashtra, India
  22. 22Medicine, RCSM Government Medical College, Kolhapur, Maharashtra, India
  23. 23Transfusion Medicine, AIIMS Jodhpur, Jodhpur, Rajasthan, India
  24. 24Department of Internal Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, Punjab, India
  25. 25Transfusion Medicine, AIIMS Patna, Patna, Bihar, India
  26. 26Department of Community Medicine, B J Government Medical College, Pune, Maharashtra, India
  27. 27Transfusion Medicine, ESIC Medical College and Hospital Faridabad, Faridabad, Haryana, India
  28. 28Internal Medicine, Smt Kashibai Navale Medical College and General Hospital, Pune, Maharashtra, India
  29. 29Internal Medicine, Karnataka Institute of Medical Sciences, Hubli, Karnataka, India
  30. 30Internal Medicine, Lady Hardinge Medical College, New Delhi, Delhi, India
  31. 31Internal Medicine, King George Medical College, Lucknow, Uttar Pradesh, India
  32. 32Internal Medicine, Byramjee Jeejeebhoy Medical College, Ahmedabad, Gujarat, India
  33. 33Respiratory Medicine, Mahatma Gandhi Medical College and Hospital, Jaipur, Rajasthan, India
  34. 34Internal Medicine, Government Medical College, Surat, Gujarat, India
  35. 35Medicine, GMERS Medical College Gotri Vadodara, Vadodara, Gujarat, India
  36. 36Internal Medicine, Sumandeep Vidyapeeth University, Vadodara, Gujarat, India
  37. 37Transfusion Medicine, Sri Venkateswara Institute of Medical Sciences, Tirupati, Andhra Pradesh, India
  38. 38Pathology, Kurnool Medical College, Kurnool, Andhra Pradesh, India
  39. 39Internal Medicine, Government Medical College, Bhavnagar, Gujarat, India
  40. 40Internal Medicine, Madurai Medical College, Madurai, Tamil Nadu, India
  41. 41Internal Medicine, Mahatma Gandhi Memorial Medical College, Indore, Madhya Pradesh, UK
  42. 42Interventional Pulmonology, Poona Hospital and Research Centre, Pune, Maharashtra, India
  43. 43Transfusion Medicine, Super Speciality Paediatric Hospital and Teaching Hospital, Noida, Uttar Pradesh, India
  44. 44Transfusion Medicine, Aditya Birla Memorial Hospital, Pune, Maharashtra, India
  45. 45Medicine, R D Gardi Medical College, Ujjain, Madhya Pradesh, India
  46. 46Transfusion Medicine, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, India
  47. 47Internal Medicine, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, Maharashtra, India
  48. 48Maximum Containment Laboratory, ICMR, National Institute of Virology, Pune, Maharashtra, India
  49. 49Diagnostic Virology Group, ICMR, National Institute of Virology, Pune, Maharashtra, India
  50. 50Human Influenza Group, ICMR, National Institute of Virology, Pune, Maharashtra, India
  51. 51Epidemiology and Biostatistics Division, National Institute of Epidemiology, Chennai, Tamil Nadu, India
  1. Correspondence to Dr Joy John Mammen; joymammen{at}cmcvellore.ac.in

Abstract

Objective Large data on the clinical characteristics and outcome of COVID-19 in the Indian population are scarce. We analysed the factors associated with mortality in a cohort of moderately and severely ill patients with COVID-19 enrolled in a randomised trial on convalescent plasma.

Design Secondary analysis of data from a Phase II, Open Label, Randomized Controlled Trial to Assess the Safety and Efficacy of Convalescent Plasma to Limit COVID-19 Associated Complications in Moderate Disease.

Setting 39 public and private hospitals across India during the study period from 22 April to 14 July 2020.

Participants Of the 464 patients recruited, two were lost to follow-up, nine withdrew consent and two patients did not receive the intervention after randomisation. The cohort of 451 participants with known outcome at 28 days was analysed.

Primary outcome measure Factors associated with all-cause mortality at 28 days after enrolment.

Results The mean (SD) age was 51±12.4 years; 76.7% were males. Admission Sequential Organ Failure Assessment score was 2.4±1.1. Non-invasive ventilation, invasive ventilation and vasopressor therapy were required in 98.9%, 8.4% and 4.0%, respectively. The 28-day mortality was 14.4%. Median time from symptom onset to hospital admission was similar in survivors (4 days; IQR 3–7) and non-survivors (4 days; IQR 3–6). Patients with two or more comorbidities had 2.25 (95% CI 1.18 to 4.29, p=0.014) times risk of death. When compared with survivors, admission interleukin-6 levels were higher (p<0.001) in non-survivors and increased further on day 3. On multivariable Fine and Gray model, severity of illness (subdistribution HR 1.22, 95% CI 1.11 to 1.35, p<0.001), PaO2/FiO2 ratio <100 (3.47, 1.64–7.37, p=0.001), neutrophil lymphocyte ratio >10 (9.97, 3.65–27.13, p<0.001), D-dimer >1.0 mg/L (2.50, 1.14–5.48, p=0.022), ferritin ≥500 ng/mL (2.67, 1.44–4.96, p=0.002) and lactate dehydrogenase ≥450 IU/L (2.96, 1.60–5.45, p=0.001) were significantly associated with death.

Conclusion In this cohort of moderately and severely ill patients with COVID-19, severity of illness, underlying comorbidities and elevated levels of inflammatory markers were significantly associated with death.

Trial registration number CTRI/2020/04/024775.

  • COVID-19
  • public health
  • adult intensive & critical care

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Statistics from Altmetric.com

Strengths and limitations of this study

  • There is no study from India with representation from multiple states that has detailed the clinical profile and evaluated for factors associated with death. This study may help with strategic planning at a national level.

  • The primary outcome of the Phase II, Open Label, Randomized Controlled Trial to Assess the Safety and Efficacy of Convalescent Plasma to Limit COVID-19 Associated Complications in Moderate Disease, disease progression or all-cause mortality at day 28 did not differ across the trial arms; therefore, the present analysis need not be adjusted for convalescent plasma intervention.

  • There may be variability of treatment provided in the multiple centres; however, care was taken that patients received best standard of care for COVID-19 dictated by the best available evidence at the time and guidelines for the management of COVID-19 issued by health authorities of the Indian government.

  • The laboratory and biomarker assays for ferritin, lactate dehydrogenase, C-reactive protein and D-dimer were conducted using tests from different manufacturers.

  • Participants of this study may not comprise a true observational cohort as this was a post hoc analysis of randomised controlled trial data. Our study did not analyse the effect of SARS-CoV-2 variants causing a high mortality in younger population during the second wave of COVID-19 infection, and therefore extrapolation to the general population must be carefully qualified.

Introduction

The first human case of COVID-19 caused by the novel coronavirus (named SARS-CoV-2) was reported in Wuhan City, China, in December 2019. On 30 January 2020, the WHO declared that the outbreak of COVID-19 constituted a Public Health Emergency of International Concern.1 Based on the high level of global spread and the severity of COVID-19, on 11 March 2020, the Director-General of the WHO declared the COVID-19 outbreak a pandemic.2 The sudden outbreak followed by rapid spread in a globalised world resulted in a huge burden on the healthcare system, besides affecting the socioeconomic well-being among all nations.

In India, the disease was first detected on 30 January 2020 in the state of Kerala, in a student who returned from Wuhan.3 4 After a brief, initial respite, the virus spread at a rapid pace in India, resulting in more than 10 million confirmed cases as of December 2020 with more than 145 000 deaths.5

Most patients diagnosed with COVID-19 experience mild to moderate respiratory illness, fever, dry cough and fatigue and recover without requiring special treatment.6 Oxygen desaturation is the hallmark of progression. Patients with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease and cancer are more likely to develop serious illness. These patients may develop viral pneumonia, with resultant dyspnoea and hypoxaemia which may progress to respiratory or multisystem failure and even death.7 There is paucity of large-scale data on the clinical characteristics, outcomes of COVID-19 in the Indian population and evaluation of risk factors with an unfavourable outcome at a national level. Identification of such potential risk factors is important to anticipate medical treatment and to reduce the mortality burden for severe COVID-19 illness by proactive interventions.

The Indian Council of Medical Research (ICMR) conducted a randomised trial (a Phase II, Open Label, Randomized Controlled Trial to Assess the Safety and Efficacy of Convalescent Plasma to Limit COVID-19 Associated Complications in Moderate Disease (PLACID Trial)) to determine the effectiveness and safety of convalescent plasma in moderately and severely ill patients with COVID-19 to limit progression of disease.8 Patients received standard of care for COVID-19 in keeping with the institutional protocols, based on the best available evidence at the time and guidelines for the management of COVID-19 issued by the national health authorities. Participants in the intervention arm received two doses of 200 mL of convalescent plasma, transfused 24 hours apart, in addition to standard of care. The control arm did not receive any additional therapy. The study concluded that the use of convalescent plasma was not associated with a reduction in 28-day mortality.8

The aim of this analysis was to identify the risk factors associated with mortality by mining the data collected from the cohort enrolled in the PLACID Trial.8

Methods

Participants

The study enrolled patients from 39 different hospitals, of which 29 were teaching in public hospitals and 10 were in private facilities across 14 states and union territories. Patients over the age of 18 years who were confirmed to have COVID-19 based on a positive SARS-CoV-2 RT-PCR test and presenting with moderate and severe illness with either a partial pressure of oxygen in arterial blood/fraction of inspired oxygen (PaO2/FiO2) ratio between 200 and 300 or respiratory rate >24/min and decreased oxygen saturation on room air (SpO2 <93%) were included during the study period from 22 April to 14 July 2020. As per the guidelines issued by the Ministry of Health, Government of India at the time of conduct of the study, the subset of patients with the above criteria but with a respiratory rate between 24 and 30/min were classified as moderate disease. Those with respiratory rate >30 breaths/min were classified as severe disease.9 Patients were followed up for 28 days and assessed for their health status and all-cause mortality. Written consent was obtained from the patients or their families before enrolling in the study.

Data

Data were obtained from the ICMR PLACID Trial database collected in structured paper case record forms and entered in Research Electronic Data Capture system (version 8.5, Vanderbilt University, Tennessee). The trial protocol was registered with the Clinical Trial Registry of India. After trial completion, based on cooperative agreement between the centres, and Institutional Review Board permission, the data were shared and analysed further to explore for other meaningful results. No separate ethical clearance was taken for this study.

Demographic, clinical, laboratory tests and outcome data were collected prospectively. Clinical symptoms, need for organ support (respiratory, renal, haemodynamic) and laboratory tests (complete blood count, coagulation profile, serum biochemical profile, renal and liver function tests) were monitored serially on day of enrolment (day 0) and on days 1, 3, 5, 7, 14 and 28. Inflammatory biomarkers (lactate dehydrogenase (LDH), serum ferritin and C-reactive protein (CRP)) were tested at admission and on days 3 and 7 whereas interleukin-6 (IL-6) was done at admission and on day 3.

The outcome of interest was all-cause day 28 mortality. In addition, we looked for association between laboratory parameters and mortality.

Statistical methods

Mean and SD or median and IQR were used for continuous variables as appropriate, and for categorical variables, number and proportions were used. To find the association between mortality and study variables, χ2 test and Fisher’s exact test were used. To find the mean difference across the groups, independent t-test was used. Similarly, Mann-Whitney U test was used to compare median difference. The primary endpoint was all-cause mortality (event of interest) at day 28 from the time of enrolment, discharged alive (competing event) or hospital admission after day 28 (censored), whichever was earlier. Discharged alive was treated as a competing event because the event of ‘discharged alive’ precludes the event of all-cause mortality. The variables that were statistically significant or clinically important were considered in the multivariable Fine and Gray regression model. However, if a variable was expected to have collinear concern or had sparse data, it was not included in the analysis. Two multivariable models were developed. The first model included clinical and laboratory parameters tested on days 0, 1, 3, 5, 7, 14 and 28 while the second included inflammatory biomarkers tested on days 0, 3 and 7, after adjusting for age and comorbidities. Variables that were considered included parameters that were strongly associated with mortality at univariate analysis or those known from previous literature to be strongly associated with outcome. For certain laboratory markers such as D-dimer, ferritin and LDH, clinically relevant thresholds were used for the analysis rather than using these data as continuous variables. The clinically relevant thresholds for these variables were set as >1.0 mg/L for D-dimer, ≥500 mg/mL for ferritin and ≥450 IU/L for LDH. The threshold for ferritin of 500 μg/L was based on the cut-off value for the diagnosis of haemophagocytic lymphohistiocytosis as well as some preliminary evidence in COVID-19 that a threshold of >500 µg/L was associated with invasive ventilator dependence.10 Similarly, traditionally a threshold of <0.5 mg/L is used to exclude pulmonary thromboembolism; in this context two thresholds were used, 0.5–1.0 mg/L and >1.0 mg/L.11 The model assumption was verified using log-log S (t) plots and global test. A p value <0.05 levels was considered as statistically significant. All statistical analyses were performed using STATA V.16.0 (StataCorp. 2019. College Station, Texas).

Patient and public involvement

Patients and the public were not involved in the design, conduct, reporting or dissemination plans of our research. The study results will be disseminated to the study participants via their treating doctors.

Results

The PLACID Trial recruited 464 eligible patients for the study. The primary outcome at 28 days was not available for two patients who were lost to follow-up after discharge; nine patients withdrew consent after randomisation and two patients did not receive the intervention after randomisation as a matched donor was not available. The cohort with a known outcome at 28 days thus comprised 451 patients (online supplemental file 1).

The primary outcome of the PLACID Trial, disease progression or all-cause mortality at day 28 did not differ across the trial arms, therefore the analysis did not adjust for convalescent plasma intervention. The distribution of patients in the intervention and control arms was 50.3% (n=227) and 49.7% (n=224), respectively. The mean (SD) age of the cohort was 51±12.4 years; 76.7% were males. Table 1 shows the distribution of demographic variables and clinical parameters in the study population.

Table 1

Distribution of demographic variables and clinical parameters in enrolled patients and comparison of survivors and non-survivors in the cohort

The most common presenting symptoms were shortness of breath (91.6%), fatigue (78.7%), cough (68.5%) and fever (35%). Comorbidities were present in 59.9% of patients; 31.7% had any one comorbidity and 28.2% had two or more comorbidities. The most frequent comorbidities were diabetes (43.5%), hypertension (37.5%), obesity (6.9%) and chronic obstructive pulmonary disease (COPD) (3.3%). There was a history of smoking in 8.2%. The time from onset of symptoms to admission was 4 days (IQR 3–7 days). Majority of the patients required non-invasive (98.9%) ventilatory support. The median duration of respiratory support was 6 days (IQR 4–10 days). In this cohort, 4% of patients required vasopressor support. None of the patients required extracorporeal membrane oxygenation or dialysis support.

The all-cause mortality at 28 days was 14.4% (95% CI 11.5% to 17.9%, n=65). Median time from symptom onset to hospital admission was 4 days in survivors (IQR 3–7 days) and non-survivors (IQR 3–6 days). The frequency of shortness of breath, cough and fatigue were similar in survivors and non-survivors; however, the presence of fever at admission was significantly (p=0·042) associated with death (table 1). Other than COPD and chronic kidney disease (CKD), other comorbidities were not significantly associated with death (table 1). Admission Sequential Organ Failure Assessment (SOFA) score was higher in non-survivors. The need for invasive mechanical ventilation, duration of invasive mechanical ventilation and vasopressor therapy were associated with death (table 1).

On univariate analysis (table 2), there was an association between increasing age and mortality. Patients with two or more comorbidities had 2.25 (95% CI 1.18 to 4.29, p=0.014) times increased chance of mortality. There was a strong mortality association for platelet count <100×109/L (subdistribution HR (SHR) 6.88, 95% CI 3.61 to 13.13, p<0.001), neutrophil lymphocyte ratio (NLR) >10 (28.84, 11.92–69.76, p<0.001), LDH ≥450 IU/L (4.88, 2.72–8.75, p<0.001), D-dimer >1 mg/L (3.34, 1.55–7.19, p=0.002) and ferritin ≥500 ng/mL (4.11, 2.28–7.41, p<0·001). Admission IL-6 levels were significantly (p<0.001) higher (76.00, 18.27–171.77) in non-survivors than in survivors (18.51, 4.26–56.86). By day 3, IL-6 levels dropped to 11.6 (2.64–45.84) in survivors while it nearly doubled in non-survivors (140.35, 21.56–427.36). CRP did not show any statistical significance (1.0003, 0.999–1.001, p=0.080).

Table 2

Univariate Fine and Gray model for baseline characteristics, laboratory parameters and inflammatory biomarkers

The need for invasive ventilation and vasopressors were associated with death (table 2). Increasing SOFA score was associated with mortality (1.63, 1.54–1.74, p<0.001). The mean SOFA score at day 0 was 2.30 and 3.05 for survivors and non-survivors, respectively. The difference in the SOFA score progressively increased between the two groups over time (figure 1). Mortality also proportionately increased with lower PaO2/FiO2 values with SHR of 25.64 (14.8–44.41, p<0.001) in the severe group as compared with the mild group.

Figure 1

Serial Sequential Organ Failure Assessment (SOFA) score among survivors and non-survivors. Increasing SOFA score was associated with mortality. The mean SOFA score at day 0 was 2.30 and 3.05 for survivors and non-survivors, respectively. The difference in the SOFA score showed divergence between the two groups over time.

Two models were run for multivariable Fine and Gray regression model over a period of time (table 3). Model A included age, comorbidities, PaO2/FiO2, NLR and SOFA score. Model A revealed significant SHRs for PaO2/FiO2 ratio <100 (3.47, 1.64–7.37, p=0.001), NLR >10 (9.97, 3.65–27.13, p<0.001) and SOFA score (1.22, 1.11–1.35, p<0.001) after adjusting for age and comorbidities. Model B included age, comorbidities, D-dimer, ferritin and LDH. D-dimer >1 mg/L (2.50, 1.14–5.48, p=0.022), ferritin ≥500 ng/mL (2.67, 1.44–4.96, p=0.002) and LDH ≥450 IU/L (2.96, 1.60–5.45, p=0.001) were associated with mortality after adjusting for age and comorbidities (table 3). IL-6 was omitted from the model as it was not measured on day 7.

Table 3

Multivariable Fine and Gray model for baseline characteristics, laboratory parameters and inflammatory biomarkers

Discussion

In this study that enrolled patients in the PLACID Trial from across India, SOFA score and clinical biomarkers like D-dimer, LDH and ferritin were identified as factors that could predict increased risk of death in moderately and severely ill patients with COVID-19. The definition of clinical grading of severity is different in India as compared with other countries.12–16 Mortality of critically ill patients with COVID-19 varies significantly among already published case series and ranges from 16% to 78%.17–23 Two studies from Wuhan, which included moderately as well as critically ill patients, showed mortality rates of 3.77% and 14.14%.24 25 This wide variability can be explained by differences in the age of the population, distribution of risk factors, health system responses, varied treatment protocols and disparate follow-up times. In a series of critically ill patients in China, the 28-day intensive care unit mortality was 61.5%.26 In a multicentric study from Italy, the mortality risk for patients without respiratory failure at admission was 1% after 15 days, while survival in patients with moderate to severe respiratory failure (PaO2/FiO2 ≤200 mm Hg) at admission was only 56% at 15 days.27 The fatality rate reported in Europe and the USA was significantly higher than in China.28 Therefore, findings obtained in a specific country might not be automatically extrapolated and national cohorts must be studied.

In our study population, mortality increased with age; this pattern was observed in other countries affected by COVID-19. Age seemed to affect the time from hospitalisation to death. Age-specific death rates were quite similar in studies from Asia, Europe and North America.29 In South Korea, Italy, France, Germany, England and Wales and Spain, the COVID-19 attributed mortality rates rose by about 12% per year whereas the USA and Wuhan, China had a lower rate of increase of about 9.5% per year of age.30 In a meta-analysis of 611 583 subjects, the overall mortality was 12.10%; the lowest mortality rate was reported from China (3.1%) and the highest in the UK (20.8%) and New York state (20.99%). Among the patients included in the meta-analysis, 23.2% were ≥80 years of age; mortality was highest in these patients. The largest increase in mortality risk was observed in patients aged 60–69 years as compared with those aged 50–59 years (OR 3.13, 95% CI 2.61 to 3.76).31

The presence of comorbidities significantly increases the death risk due to COVID-19. A higher risk of mortality was seen in our patients who had CKD and COPD. A meta-analysis, including 1389 patients with COVID-19, with 19.7% having severe disease showed a significant association of CKD with severe COVID-19 with pooled OR of 3.03.32 Similarly, the estimated mortality risk in patients with COPD was three times than those without (p<0.05).33 We found that 43.5% of our patients had diabetes which is markedly higher when compared with patients from Korea which showed that 16.97% had diabetes mellitus.34 Our analysis showed that the presence of diabetes was not significantly different between survivors and non-survivors (42.5% vs 49.2%, p=0.310), in contrast to the study from South Korea34 which showed a much higher mortality among patients with diabetes than in those without (20.0% vs 4.8%). Hypertension and obesity were not significantly different among survivors and non-survivors in our study. However, the presence of two or more comorbidities was associated with mortality in our study.

The Fine-Gray model identified prognostic markers for mortality, most notably age ≥60 years, PaO2/FiO2 ratio <100, NLR >10, platelet count <100×109/L, ferritin >500 ng/mL, LDH >450 IU/L and D-dimer >1 mg/L. Our study findings were similar when compared with studies from Wuhan.35 Older age, leucocytosis and high LDH level have been reported to be risk factors associated with in-hospital death in other studies also.36–38 IL-6 levels were significantly different in survivors and non-survivors at admission. By day 3, survivors had reducing IL-6 while it nearly doubled in non-survivors.

Mortality was higher among patients requiring invasive mechanical ventilation (SHR 19.57, 12.21–31.35, p<0.001) and those requiring vasopressors (SHR 11.36, 7.79–16.56, p<0.001). However, the median duration of invasive ventilation for survivors was 12 days (IQR 2, 14) and that for non-survivors was 1 day (IQR 1, 3). These results suggest that the sickest patients probably die very early in the course of hospitalisation, while patients with acute respiratory failure requiring ventilatory support may survive with prolonged ventilatory support. Therefore, invasive ventilation should be offered in a timely manner and effectively provided.

In our study, the SOFA score was recognised as a valuable tool that could be used to prognosticate the outcome of patients with COVID-19. Competing risk regression models showed that the increase in SOFA score was related to mortality, with a clearly divergent pattern between the two groups. Thus, an increasing SOFA score over time may be a factor that can be used to identify a subset of patients who may have an unfavourable outcome. Studies have shown that the SOFA score could be used to evaluate severity and 60-day mortality of COVID-19 with the optimal cut-off score of 5.39

The limitations of this study include the variability of treatment provided in the multiple centres. The participants of this study may not comprise a true observational cohort, as this was a post hoc analysis of randomised controlled trial data and extrapolation to the general population must be carefully qualified. Our study did not analyse the effect of SARS-CoV-2 variants causing a high mortality in younger population during the second wave of COVID-19 infection and this may limit generalisability of the data to the second wave. Despite these limitations, this study provides a comprehensive overview of prognostic factors in moderately and severely ill patients with COVID-19 that included patients from across the country.

Conclusion

Older age, multiple comorbidities, low PaO2/FiO2 ratio and elevated levels of inflammatory markers are associated with worse prognosis. Serial SOFA score can be used for prognostication. Understanding the symptoms, burden of comorbidities and systematic monitoring of key laboratory parameters offer opportunities for targeted intervention in COVID-19 with the use of anti-inflammatory or immunomodulatory agents.

Data availability statement

Data are available upon reasonable request.

Ethics statements

Patient consent for publication

Ethics approval

Ethical approval was obtained from the ICMR Central Ethics Committee on Human Research (CECHR-002/2020) based in the National Center for Disease Informatics and Research, Indian Council of Medical Research, Bengaluru, Karnataka, as well as from the Institutional Review Boards (IRB)/Institutional Ethics Committees of all the participating hospitals.

Acknowledgments

We acknowledge the ICMR for providing us the data collected during PLACID Trial which were analysed in the present study.

References

Supplementary materials

  • Supplementary Data

    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • Twitter @tarunb20

  • Contributors All the authors contributed equally in the study and the specific contributions are mentioned in the manuscript. Study design: JJM, LJ, JVP. Clinical management: SK, LT, AZ, JER, BC, BL, SBu, VK, RD, JRK, RdS, BTC, SBa, SDub, ASu, AJi, OS, VB, AB, PM, NSi, MT, NSh, SBh, RSK, AG, DHR, KU, AJa, TCP, IN, PRJ, KVSB, CA, SJP, MN, MB, VKK, SDua, RS, AS, JS, YAG. Data collection: JJM, SK, LT, AZ, AA, AM, GK, PC, TB, JER, PR, MM, BC, BL, SBu, VK, RD, JRK, RdS, BTC, SBa, SDub, ASu, AJi, OS, VB, AB, PM, NSi, MT, NSh, SBh, RSK, AG, DHR, KU, AJa, TCP, IN, PRJ, KVSB, CA, SJP, MN, MB, VKK, SDua, RS, ASh, JS, YAG, PDY, GS, HK, VSK. Data analysis: JJM, SK, LJ, TM, MJ, PR, MM, DD, JVP. Data interpretation: JJM, SK, AZ, LJ, JER, TM, MJ, DD, JVP. Manuscript writing: JJM, SK, LT, AZ, LJ, JER, BC, TM, MJ, PR, MM, DD, JVP, AA, AM, GK, HK, PC, TB. Study administration: JJM, BC, JVP, AA, AM, GK, HK, PC, TB.

  • Funding The funding source for the primary study was ICMR (RFC No ECD/NTF/20/2020-21/Covid, 23 July 2020).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.