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  Table of Contents 
Year : 2023  |  Volume : 24  |  Issue : 1  |  Page : 58-64

Mortality predictors during the third wave of COVID-19 pandemic: A multicentric retrospective analysis from tertiary care centers of Western India

1 Department of Anaesthesiology, Dr. S.N. Medical College, Jodhpur, Rajasthan, India
2 Department of Anaesthesiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
3 Department of Anaesthesiology, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
4 Department of Anaesthesiology, J. L. N. Medical College, Ajmer, Rajasthan, India
5 Department of Anaesthesiology, S. P. Medical College, Bikaner, Rajasthan, India

Date of Submission02-Dec-2022
Date of Decision10-Mar-2023
Date of Acceptance17-Mar-2023
Date of Web Publication24-May-2023

Correspondence Address:
Dr. Rishabh Jaju
Department of Anaesthesiology, All India Institute of Medical Sciences, Deoghar, Jharkhand
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/TheIAForum.TheIAForum_112_22

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Background: The COVID-19 has a varied mode of presentation in different regions of the world. This multicentric study was planned to evaluate the survival outcomes in intensive care unit-admitted patients admitted during the third wave of the COVID-19 pandemic on the basis of clinicodemographic profile and vaccination status.
Methodology: Data from 299 patients admitted to three tertiary care centers in Western India were collected and analyzed. Based on survival outcomes, all patients were divided into two groups: survivors and nonsurvivors. Univariate analysis of the demographic profile, comorbidities, vaccination status, and disease severity was performed, whereas multivariate analysis was performed to predict independent factors associated with mortality.
Results: Among total 299 studied patients, 208 (69.5%) patients survived and 91 (30.4%) did not. The number of elderly patients and patients with comorbidities such as diabetes, tuberculosis, chronic obstructive pulmonary disease, cardiovascular and respiratory diseases, and malignancy were more prevalent among nonsurvivors. Patients who did not receive a single dose of vaccine were higher in the nonsurvivor group (P = 0.037); however, no significant difference in survival outcome was found if patients had received the first or both doses of vaccine. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score at 24 h after admission and Sequential Organ Failure Assessment (SOFA) score at admission were significantly higher in nonsurvivors compared to survivors (P < 0.0001). On multivariate analysis, APACHE II and SOFA scores were found to be independent predictors of outcome.
Conclusions: Older age, presence of comorbidities, nonvaccination and higher disease severity scores affected mortality during the third wave of COVID-19.

Keywords: Acute Physiology and Chronic Health Evaluation II, comorbidity, COVID-19, intensive care unit, Sequential Organ Failure Assessment, third wave, vaccination

How to cite this article:
Paliwal N, Bihani P, Rao S, Jaju R, Mohammed S, Khare A, Dhawan S, Rajpurohit V, Tak ML, Singariya G. Mortality predictors during the third wave of COVID-19 pandemic: A multicentric retrospective analysis from tertiary care centers of Western India. Indian Anaesth Forum 2023;24:58-64

How to cite this URL:
Paliwal N, Bihani P, Rao S, Jaju R, Mohammed S, Khare A, Dhawan S, Rajpurohit V, Tak ML, Singariya G. Mortality predictors during the third wave of COVID-19 pandemic: A multicentric retrospective analysis from tertiary care centers of Western India. Indian Anaesth Forum [serial online] 2023 [cited 2023 Jun 1];24:58-64. Available from: http://www.theiaforum.org/text.asp?2023/24/1/58/377548

  Introduction Top

The COVID-19 pandemic has affected the lives of millions of people worldwide in the past 2 years.[1] The first and subsequent waves drew the attention of many medical researchers and heightened the scientific understanding of the disease, leading to the development of more effective pharmacological measures, respiratory support measures, preventive measures including vaccines, social distancing, COVID-19 appropriate behaviors, use of personal protective equipment, and identification of mortality risk factors worldwide. Health-care workers have devoted their valuable time and energy to exploring the causes, aggravating factors, and effectiveness of vaccination on morbidity and mortality due to COVID-19.[2] Disease mortality and morbidity vary in different geographic regions, which may be due to the varying clinicodemographic profiles and vaccination status of the population.

India reported 7000–9000 COVID-19 cases daily during November 2021; thereafter, there was an accelerated increase in the number of positive cases. In January 2022, more than three lakh cases were reported daily.[3] Preliminary data so far have shown that the Omicron variant of COVID-19 is more contagious but less virulent than its previous lineages. Previous studies have reported that patients with multiple comorbidities have higher intensive care unit (ICU) admission rates and mortality.[4],[5] In a multicenter cohort study conducted in 73 ICUs in Europe on critically ill COVID-19 patients with pneumonia, no significant difference in ICU mortality between the first wave and the second and third waves (31.7% vs. 28.8% respectively) was recorded and mortality predictors included age, presence of cardiovascular disease, Sequential Organ Failure Assessment (SOFA) score, and immunosuppression.[6]

Experience from the last waves of the pandemic has provided scientific evidence to identify the risk factors associated with survival outcomes and insight into treatment strategies. However, published literature to predict COVID-19-related mortality in Indian subgroup of population is limited. Hence, we conducted this multicentric study to determine the risk factors predicting mortality in COVID-19 patients. The primary objective of this study was to estimate and compare the impact of comorbidities on survival outcomes in the ICU-admitted COVID-19 patients during the third wave of the pandemic. The demographic characteristics, vaccination status, and disease severity assessed by the SOFA score of patients at the time of admission and Acute Physiology and Chronic Health Evaluation II (APACHE II) at 24 h of ICU admission were also compared between survivors and nonsurvivors.

  Methodology Top

The data for this retrospective, multicenter, observational study were collected from three ICUs of tertiary care centers in Western India after obtaining the Institutional Ethics Committee approval from all the participating centers (SNMC/IEC/2022/1676-1677, 12691-716 Acad-III/MCA/2022, F.29(Acad) SPMC/2022/800). Data of all patients aged 18 years or above with confirmed nasopharyngeal swab reverse transcription polymerase chain reaction (RT-PCR) COVID-19-positive reports and admitted in ICUs between December 01, 2021, and February 28, 2022, were collected from the control room (established for COVID-19 reporting at the medical college level). Patients with missing or incomplete data on the outcome variables were excluded from the study. As the data were de-identified by removing the patient's name and record number, informed consent of participants/their relatives was waived.

Demographic profiles, comorbidities, vaccination status, and disease severity scores were collected from the patients' ICU treatment records. After data anonymization, all forms were sent to the second researcher and the study coordinator.

Patients were divided into two groups: survivors and non-survivors. The following data were recorded-general demographic characteristics such as age, sex, weight and body mass index, presence of comorbidities such as diabetes mellitus (DM), hypertension (HTN), chronic kidney disease (CKD), tuberculosis (TB), malignancy, vaccination status (whether patients had received only the 1st dose or both the dosages of Covishield/Covaxin or did not receive even a single dose of vaccine). Measures of the disease severity-SOFA score at admission and APACHE II scores at 24 h of ICU admission were also recorded and analysed.

Statistical analysis

All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA, version 23.0, for Windows). Categorical variables were presented as frequencies and percentages, and therefore the difference between survivor and nonsurvivor groups was compared using the Chi-square test or Fisher's exact test when appropriate, while continuous variables were expressed as median (interquartile range) or mean ± standard deviation and were compared using Student's t-test or Mann–Whitney U-test. The difference was considered significant if P < 0.05 for a two-tailed test. Multivariate Cox regression analysis was done for the variables with statistical significance in the univariable analysis. The results of the multivariable analysis were reported as odds ratios (OR) with the 95% confidence intervals (CI). Receiver operating characteristics (ROC) analysis was performed to determine the accuracy of the scoring systems (APACHE II and SOFA) for the prediction of mortality. The area under the curve (AUC) was determined with 95% CIs. The optimum cutoff value of the scoring system was determined based on the maximum Youden's index.

  Results Top

During the study period, 310 patients with RT-PCR positive-COVID-19 status were admitted to the participating ICUs. Eleven patients were excluded because their data on study outcomes were missing. Data from the remaining 299 patients' data were compiled and analyzed [Figure 1]. Out of the total admitted patients, 208 (69.5%) survived, and 91 (30.4%) died during their ICU stay.
Figure 1: STORBE flow chart of the study. STROBE: Strengthening the Reporting of Observational studies in Epidemiology

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In our cohort, the percentage of elderly patients and presence of comorbidities such as diabetes, TB, chronic obstructive pulmonary disease (COPD), ischemic heart disease, malignancy, CKD, chronic heart failure (CHF), and chronic liver disease (CLD) were significantly higher among the nonsurvivors (P = 0.038, P < 0.0001, P = 0.013, P = 0.02, P = 0.001, P = 0.03, P = 0.001, P = 0.001, and P = 0.003, respectively) [Table 1] and [Figure 2]. The most common comorbidity observed was HTN (n = 80; 26.8%), followed by diabetes (n = 74; 24.7%), obesity (n = 40; 13.4%), and COPD (n = 31; 10.4%). The percentage of nonvaccinated patients was significantly higher among the nonsurvivor (P = 0.037). However, there was no significant difference with regard to vaccination status (whether received one/both dosage of COVID-19 vaccine) among survivors and nonsurvivors [Table 1]. The APACHE II score at 24 h after admission in ICU and SOFA score on ICU admission were significantly higher in non survivors compared to survivors (P < 0.0001 and P < 0.0001, respectively) [Table 1].
Figure 2: Forest plot of the logistic regression analysis of comorbidities in survivor and nonsurvivors. CI: Confidence interval, COPD: Chronic obstructive pulmonary disease, IHD: Ischemic heart disease, CKD: Chronic kidney disease, CHF: Chronic heart failure, CLD: Chronic liver disease

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Table 1: Comparison of demographic parameters, vaccination status, and disease severity scores between survivors and nonsurvivors

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The logistic regression analysis of significant variables in univariate analysis demonstrated that the APACHE II score at 24 h of ICU admission and SOFA score at ICU admission were independent predictors of outcome [Table 2]. The ROC curve analysis showed that among the significant parameters, the APACHE II and SOFA scoring system had good outcome predictive ability (AUC was 0.96 [95% CI: 0.98–1.00] and 0.93 [95% CI: 0.91-0.96], respectively). The optimum cutoff values of APACHE II and SOFA were 22.5 (sensitivity 90% and specificity 95%) and 10.5 (sensitivity 70% and specificity 95%), respectively [Figure 3].
Figure 3: ROC curve for the predicted value of APACHE II and SOFA score. ROC: Receiver operating characteristic, APACHE II: Acute Physiology and Chronic Health Evaluation II, SOFA: Sequential Organ Failure Assessment

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Table 2: Multivariate regression analysis of significant variables for the mortality risk among intensive care unit admitted COVID-19 patients

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The ICU length of stay was significantly lower in nonsurvivors than in survivors (P = 0.009); however, the number of days on a ventilator was comparable between survivors and nonsurvivors [Table 3].
Table 3: Comparison of length of ventilator stay and length of intensive care unit stay between survivors and nonsurvivors

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  Discussion Top

In this multicenter retrospective study, 69.5% of the total RT-PCR-positive COVID-19 patients survived during their stay in the ICU. Comorbidities such as diabetes, cardiovascular and respiratory diseases, increasing age, nonvaccinated status, and higher disease severity scores (APACHE II score at 24 h after admission and SOFA score at admission) were associated with poor survival outcomes.

Data from several regions of India have reported that the overall disease burden and deaths as a proportion of active cases have significantly decreased in the third COVID-19 wave surge, which is attributed to substantial change in critical care practices.[7],[8],[9] The mass vaccination drive conducted by the Government of India, better health care organization facilities which decreased ICU load, improvements in respiratory support measures, alternate treatment approaches and less virulent strains of the circulating virus may be the potential explanations for mortality reduction.[4],[10]

The higher percentage of the older age group population in the nonsurvivor group may be due to the declining immune response, fragility, and the higher risk of acquiring other comorbidities.[11] Although some studies have reported a higher incidence of death among men, we found no impact of sex on survival outcomes.[12]

Evidence from around the globe has reported that patients with underlying comorbidities are at a high risk of developing severe symptoms and are more prone to mortality due to COVID-19. The most common comorbidity observed in our study was HTN (26.8%), followed by diabetes (24.7%), obesity (13.4%), and COPD (10.4%). Many studies published during the previous COVID-19 waves have reported significant associations of cardiovascular disease, diabetes, COPD, chronic renal failure, and mortality in ICU-admitted patients.[4],[5],[6],[13] Data analysis of COVID-19 Associated Hospitalization Surveillance Network (COVID-19-NET) reported that most prevalent comorbidity was HTN (49.7%) followed by obesity, chronic lung disease, DM, and cardiovascular diseases.[14] A meta-analysis of 28 studies with a total sample size of 6270 patients (1615 severe and 4655 nonsevere patients) reported that about 41% of patients had comorbidities and the odds of comorbidities were maximum with a history of cerebrovascular disease: OR 4.85 (95% CI: 3.11–7.57) followed by chronic vascular disease, chronic lung disease, cancer, diabetes, and HTN.[4] Another meta-analysis of 1527 patients from 6 published studies from China reported the prevalence of diabetes, cardiocerebrovascular disease, and HTN in patients admitted with COVID-19 to be 9.7%, 16.4%, and 17.1%, respectively.[15]

The odds of having diabetes, cardiovascular disease, COPD, and CKD were significant between survivors and nonsurvivors. Diabetes may lead to dysfunctional proinflammatory cytokine response which can compromise and weaken the immune system, thus worsening the outcome. Advance diabetes compromises the function of multiple organs, leading to increased mortality. Higher prevalence of mortality in patients with diabetes has also been recorded in previous viral endemics or pandemics.[6],[16] The presence of preexisting cardiac diseases has been associated with a 4.4 times increase in the chances of ICU admission and COVID-19 induced myocardial injury. Medications used to treat CHF and HTN have been reported to enhance the expression of angiotensin converting enzyme-2 receptors in adipocytes, turning them into potential viral carrier, thus facilitating the spread of COVID-19 virus to other organs.[4],[15] Yang et al. published a meta-analysis reporting respiratory system diseases (OR: 2.46, 95% CI: 1.76–3.44) imposing a higher risk of disease severity.[13] COPD leads to airflow obstruction and an increased incidence of developing acute respiratory distress syndrome (ARDS). Anxiety and fear among COPD patients with COVID-19 also contribute to negativity. Similar to diabetes, a dysregulated immune system in CKD and CLD may compromise survival.[4]

COVID-19 vaccines have remained a valuable aid in controlling the global pandemic and different vaccines have proven their efficacy in decreasing severity and mortality. About 24% of admitted patients had not received even a single dose of vaccine and the prevalence of nonvaccinated patients was higher in nonsurvivor group. However, we did not find any difference in the survival outcome among patients who had received at least one dose of vaccine.

A Pan-India survey on the efficacy of Covishield and Covaxin in health-care workers, the COVAT study, has reported humoral immune response after two doses.[17] A preliminary data on 10,262 SARS-CoV-2 vaccine breakthrough infections from 46 US states reported the median patient age to be 58 years and mortality rate of just 2%.[18] The health authority of Oregon has reported more than 1000 cases of breakthrough infections after vaccination, but a mortality rate of only 2%.[19] An analysis of Pfizer-BioNTech COVID-19 BNT162b2 vaccine among adults has reported that vaccination against COVID-19 saved more lives than expected in Israel, despite government loosening stringency, in the population aged 70 years or older.[20] To estimate the early impact of vaccination (Coronavac and Oxford/AstraZeneca) vaccines against the gamma variant of SARS-CoV-2 COVID-19 on deaths among the elderly in Brazil, data analysis of over 450,000 COVID-19 deaths reported that after vaccination, proportionate mortality in the elderly declined to under 13% as compared to 25%–30% before vaccination.[21]

The importance of scoring systems becomes important in triaging the patients during pandemic as large numbers of patients get hospitalized during a short span of time. APACHE II and SOFA scores have been commonly used to assess the disease severity and estimate hospital mortality rate. On multivariate analysis in our study, APACHE II and SOFA scores were found to be independently associated for predicting the survival outcome with the cutoff value much lower than previously reported cutoff values in non-COVID-19 ICU-admitted patients. Zou et al. reported APACHE II score of more than 17 to be an early indicator of mortality prediction and to modify treatment direction. Another study reported the median APACHE II and SOFA score to be 14 and 18, respectively, in survivors and nonsurvivors.[22] Clinical course of patients admitted with COVID-19 remains variable when compared to other pathogens and many patients present with serious illness at the time of admission. Despite timely interventions, these patients deteriorated during their hospital stay for a few days and did not survive. Asmarawati et al. also concluded that the APACHE II score was the best mortality predictor in COVID-19 patients requiring ICU admission.[23] Therefore, prioritizing patients based on clinical predictors may be useful in guiding further clinical and treatment decisions.[24],[25]

The strength of our study is its multicentric design, which includes a study population of critically ill patients admitted to the ICU and a comparison of vaccination drive and comorbidities between survivors and nonsurvivors. We acknowledge the limitations of this study. First, the study design was retrospective observational in nature, which included chart review of ICU-admitted patients; therefore, unmeasured confounders such as patient having critical illness, severity of ARDS, presence of circulatory shock, acute kidney injury, and need of renal replacement therapy during the course of ICU stay might have affected the study results. As a smaller percentage of patients were admitted to the ICU from a region of the world, the results may not be extrapolated to other population groups.

  Conclusions Top

The presence of comorbidities and nonvaccinated status negatively affected the survival outcome of critically ill patients admitted to the ICU during the third wave of pandemic. APACHE II score at 24 h and SOFA scores at the time of admission, determining the disease severity during the initial course of ICU admission stood out as the most significant and individual predictors of survival.

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Conflicts of interest

There are no conflicts of interest.

  References Top

World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available from: https://covid19.who.int. [Last accessed on 2021 Jul 10].  Back to cited text no. 1
Parasher A. COVID-19: Current understanding of its pathophysiology, clinical presentation and treatment. Postgrad Med J 2021;97:312-20.  Back to cited text no. 2
Government of India. Ministry of Health and Family Welfare. State-Wise COVID-19 Test Positivity Rates. Available from: https://www.mohfw.gov.in/. [Last accessed on 2022 Jan 20].  Back to cited text no. 3
Honardoost M, Janani L, Aghili R, Emami Z, Khamseh ME. The association between presence of comorbidities and COVID-19 severity: A systematic review and meta-analysis. Cerebrovasc Dis 2021;50:132-40.  Back to cited text no. 4
Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, et al. COVID-19 and cardiovascular disease. Circulation 2020;141:1648-55.  Back to cited text no. 5
Carbonell R, Urgelés S, Rodríguez A, Bodí M, Martín-Loeches I, Solé-Violán J, et al. Mortality comparison between the first and second/third waves among 3,795 critical COVID-19 patients with pneumonia admitted to the ICU: A multicentre retrospective cohort study. Lancet Reg Health Eur 2021;11:100243.  Back to cited text no. 6
Kalita K. Assam: Mortality Rate in Third Wave as Low as 0.17% since January 01. The Times of India; 21 January, 2022.  Back to cited text no. 7
Ghosh P. Fatality in 3rd Wave Significantly Lower than Covid 2nd Wave: Health Ministry. Hindustan Times; 20 January, 2022.  Back to cited text no. 8
Gupta R, Ghosh A, Singh AK, Misra A. Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes Metab Syndr 2020;14:211-2.  Back to cited text no. 9
Perappadan BS. Vaccines Preventing Manu COVID-19 Deaths in Third Wave: Government. The Hindu; 21 January, 2022.  Back to cited text no. 10
Verma M, Sharma S, Kumar A, Hakim A, Bhansali S, Meena R. Comorbidities and vaccination status of COVID-19 all-cause mortality at a tertiary care center of Western India. Cureus 2022;14:e21721.  Back to cited text no. 11
Asirvatham ES, Sarman CJ, Saravanamurthy SP, Mahalingam P, Maduraipandian S, Lakshmanan J. Who is dying from COVID-19 and when? An analysis of fatalities in Tamil Nadu, India. Clin Epidemiol Glob Health 2021;9:275-9.  Back to cited text no. 12
Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int J Infect Dis 2020;94:91-5.  Back to cited text no. 13
Garg S, Kim L, Whitaker M, O'Halloran A, Cummings C, Holstein R, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019 – COVID-NET, 14 States, March 1-30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:458-64.  Back to cited text no. 14
Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol 2020;5:802-10.  Back to cited text no. 15
Fadini GP, Morieri ML, Longato E, Avogaro A. Prevalence and impact of diabetes among people infected with SARS-CoV-2. J Endocrinol Invest 2020;43:867-9.  Back to cited text no. 16
Singh AK, Phatak SR, Singh R, Bhattacharjee K, Singh NK, Gupta A, et al. Antibody response after first and second-dose of ChAdOx1-nCOV (Covishield(TM)®) and BBV-152 (Covaxin(TM)®) among health care workers in India: The final results of cross-sectional coronavirus vaccine-induced antibody titre (COVAT) study. Vaccine 2021;39:6492-509.  Back to cited text no. 17
CDC COVID-19 Vaccine Breakthrough Case Investigations Team. COVID-19 vaccine breakthrough infections reported to CDC – United States, January 1-April 30, 2021. MMWR Morb Mortal Wkly Rep 2021;70:792-3.  Back to cited text no. 18
COVID-19 Monthly Report: Oregon's Monthly Surveillance Summary Novel Coronavirus (COVID-19); 2021. Available from: https://www.oregon.gov/oha/covid19/Documents/DataReports/Breakthrough-Report-08-2021.pdf. [Last accessed on 2021 Jun 12].  Back to cited text no. 19
Arbel R, Moore CM, Sergienko R, Pliskin J. How many lives do COVID vaccines save? Evidence from Israel. Am J Infect Control 2022;50:258-61.  Back to cited text no. 20
Victora PC, Castro PM, Gurzenda S, Medeiros AC, França GV, Barros PA. Estimating the early impact of vaccination against COVID-19 on deaths among elderly people in Brazil: Analyses of routinely-collected data on vaccine coverage and mortality. EClinicalMedicine 2021;38:101036.  Back to cited text no. 21
Zou X, Li S, Fang M, Hu M, Bian Y, Ling J, et al. Acute physiology and chronic health evaluation II score as a predictor of hospital mortality in patients of coronavirus disease 2019. Crit Care Med 2020;48:e657-65.  Back to cited text no. 22
Asmarawati TP, Suryantoro SD, Rosyid AN, Marfiani E, Windradi C, Mahdi BA, et al. Predictive value of sequential organ failure assessment, quick sequential organ failure assessment, acute physiology and chronic health evaluation II, and new early warning signs scores estimate mortality of COVID-19 patients requiring Intensive Care Unit. Indian J Crit Care Med 2022;26:464-71.  Back to cited text no. 23
Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-81.  Back to cited text no. 24
Siddiqui SS, Patnaik R, Kulkarni AP. General severity of illness scoring systems and COVID-19 mortality predictions: Is “old still gold?”. Indian J Crit Care Med 2022;26:416-8.  Back to cited text no. 25


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