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1 Title: “Vaccination saves lives: A real-time study of patients with chronic diseases and severe COVID-19 4
2 infection”.
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3 Author List
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45 ¶Aparna Mukherjee, PhD1; ¶Gunjan Kumar, MD1; Alka Turuk, MD1; Ashish Bhalla, MD2; Thrilok 9
6 Chander Bingi, MD3 Pankaj Bhardwaj, MD4; Tridip Dutta Baruah, MS5; Subhasis Mukherjee, MD6; 10
7 Arunansu Talukdar, PhD7; Yogiraj Ray, DM8, Mary John, MD9, Janakkumar R Khambholja, MD10; Amit 11
8 H. Patel, MD11; Sourin Bhuniya, MD12; Rajnish Joshi, MD13, Geetha R Menon, PhD14; Damodar Sahu, 12
9 PhD14; Vishnu Vardhan Rao, PhD14; Balram Bhargava, DM1; *Samiran Panda, MD1
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12 And the NCRC Study team
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14 Puspendra Mishra, MCA14; Yashmin Panchal, PGDISAD14; Lokesh Kumar Sharma, PhD1; Anup Agarwal, 18
15 MBBS15; GD Puri, MD2; Vikas Suri, MD2; Karan Singla, MD2; Rajarao Mesipogu, MD3; Vinaya Sekhar 19
16 Aedula, MD3; Mohammed Ayaz Mohiuddin, MD3; Deepak Kumar, MD4; Suman Saurabh, MD4; Sanjeev 20
17 Misra, MCh4; Pankaj Kumar Kannauje, MD5; Ajit Kumar, MD5; Arvind Shukla, PhD5; Amitava Pal, MD6, 21
18 Shreetama Chakraborty, MSc6; Moumita Dutta, MSc6; Tanushree Mondal, MD7; Sarmistha Chakravorty, 22
19 MSc7; Boudhyan Bhattacharjee, MD7; Shekhar Ranjan Paul, DTCD8; Debojyoti Majumder, MD8; 23
20 Subhranga Chatterjee, MBBS8; Abin Abraham, MD9; Divya Varghese, MD9; Maria Thomas, MD9; Nitesh 24
21 shah, MD11; Minesh Patel, MD11, Surabhi Madan, MD11; Anita Desai, PhD16; Kala Yadhav M L, MD17; 25
22 Madhumathi. R, MD17; Chetna GS, MD17; U K Ojha, MD18; Ravi Ranjan Jha18, Avinash Kumar, MD18; 26
Ashish Pathak, PhD19; Ashish Sharma, MD19; Manju Purohit, MD19; Lisa Sarangi, MD20; Mahesh Rath,
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MD20; Arti D. Shah, DNB21; Lavlesh Kumar, MD21; Princee Patel, MBBS21; Naveen Dulhani, MD22,
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Simmi Dube, MD23; Jyotsna Shrivastava, MD23; Arvind Mittal, MD23; Lipilekha Patnaik, MD24; Jagdish
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Prasad Sahoo, DM24; Sumita sharma24; V K Katyal, MD, FACC25; Ashima Katyal, MD25; Nidhi Yadav,
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MD25; Rashmi Upadhyay, MD26; Saurabh Srivastava, MD26; Anurag Srivastava, MD26; Nilay N.Suthar,
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MD10; Nehal M. Shah, MD10; Kruti Rajvansh, MD10; Hemang Purohit, MSc10; Prasanta Raghab
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Mohapatra, MD12; Manoj Kumar Panigrahi, MD12; Saurabh Saigal, MD, EDIC13; Alkesh Khurana, MD13;
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Manisha Panchal, MD27; Mayank Anderpa, MD27; Dhruv Patel, MBBS27; Veeresh Salgar, MD28; Santosh
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Algur, MBBS28; Ratnamala Choudhury, MD29; Mangala Rao. MD29; Nithya D, MSc29; Bal Kishan Gupta,
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MD30; Bhuvanesh Kumar, MD30; Jigyasa Gupta, MBBS30; Sudhir Bhandari, MD31; Abhishek Agrawal,
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MD31; Mohammad Shameem, MD, FRCP32; Nazish Fatima, MD32; Star Pala, MD33; Vijay Nongpiur,
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DM33; Soumyadip Chatterji, DM34; Sudipta Mukherjee, FNB34; Sachin K Shivnitwar, MD35; Srikanth
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Tripathy, MD35; Prajakta Lokhande, MPH35; Himanshu Dandu, MD36; Amit Gupta, MD36; Vivek Kumar,
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MD36; Nikita Sharma, MD37; Rajat Vohra, MD37; Archana Paliwal, MD37; M.Pavan Kumar, MD38;
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A.Bikshapathi Rao, MD38; Nyanthung Kikon, PGDPHM39; Rhondemo Kikon, MScIH40; K. Manohar,
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MD41; Y.Sathyanarayana Raju, MD41; Arun Madharia, MS42; Jaya Chakravarty, MD43; Manaswi Chaubey,
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MD43; Rajiv Kumar Bandaru, MD44; Mehdi Ali Mirza, DM44; Sushila Kataria, MD45; Pooja Sharma45;
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Soumitra Ghosh, MD46; Avijit Hazra, MD46.
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1Indian Council of Medical Research, New Delhi, India
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2 Postgraduate Institute of Medical Education & Research, Chandigarh, India
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3 Gandhi Medical College, Telangana, India
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4 All Indian Institute of Medical Sciences, Jodhpur, Rajasthan, India
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5 All Indian Institute of Medical Sciences, Raipur Chhattisgarh, India
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6 College of Medicine and Sagore Dutta Hospital, Kolkata, West Bengal, India
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7 Medical College, Kolkata, West Bengal, India
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8 Infectious Disease And Beliaghata Hospital, Kolkata, West Bengal, India
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9 Christian Medical College, Ludhiana, Punjab, India
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10 Smt. NHL, Municipal Medical College, Ahmedabad, Gujarat, India
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11 CIMS Hospital, Ahmedabad, India
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12 All India Institute Of Medical Sciences, Bhubaneswar, India
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13 All India Institute Of Medical Sciences, Bhopal, Madhya Pradesh, India
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14 National Institute of Medical Statistics, Indian Council of Medical Research, Delhi, India
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56 15 Medstar Health, Baltimore, Maryland, United States of America
© The Author(s) 2022. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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16 National Institute Of Mental Health And Neurosciences, Bangalore, Karnataka, India
17 Bowring & Lady Curzon Medical College & Research Institute, Bangalore, Karnataka, India
18 Shaheed Nirmal Mahato Medical College, Dhanbad, Jharkahnd, India
19 RD Gardi Medical College, Ujjain, Madhya Pradesh, India
20 Hi Tech Medical College and Hospital, Bhubaneswar, India
21 Dhiraj Hospital & Sumandeep Vidyapeeth, Vadodara, Ahmedabad, India
22 Late BRK Memorial Medical College, Jagdalpur, Chhattisgarh, India
23 Gandhi Medical College, Bhopal, Madhya Pradesh, India
24 Institute of Medical Sciences & SUM Hospital, Siksha ‘O’ Anusandhan deemed to be University, Bhubaneswar, India
25 Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India
26 Government Institute of Medical Sciences, Noida, Uttar Pradesh, India
27 GMERS Medical College Himmatnagar, Gujarat, India
28 Gulbarga Institute of Medical Sciences, Kalburagi, Karnataka, India
29 St. Johns Medical College, Bengaluru, Karnataka, India
30 S.P.Medical College, Bikaner, Rajasthan, India
31 SMS Medical College, Jaipur, Rajasthan, India
32 JN Medical College Aligarh Muslim University, Aligarh, Uttar Pradesh, India
33 North Eastern Indira Gandhi Regional Institute of Health and Medical Sciences, Shillong, Meghalaya, India
34 Tata Medical Centre, Kolkata, West Bengal, India
35 Dr D Y Patil Medical college Hospital and Research centre, Pune, Maharashtra, India
36 King George Medical University, Lucknow, Uttar Pradesh, India
37 Mahatma Gandhi Medical College, Jaipur, Rajasthan, India
38 Kakatiya Medical College, MGM Hospital Warangal, Telangana, India
39 Department of Health & Family Welfare, Government of Nagaland, Nagaland, India
40 Community Health Initiative, Nagaland, India
41 Nizam’s Institute of Medical Sciences, Punjagutta, Hyderabad, India
42 ESI Hospital and Gayatri Hospital, Raipur, Chhattisgarh, India
43Institute of Medical sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
44 ESIC medical College, Sanathnagar, Hyderabad. India
45 Medanta-The Medicity, Gurugram, Haryana, India
46 Institute of Postgraduate Medical Education & Research, Kolkata, West Bengal
*Corresponding Author:
Dr. Samiran Panda
Email: samiranpanda.hq@icmr.gov.in
¶These authors have contributed equally to this work.
This work was supported by the Indian Council of Medical Research
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109 Abstract
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111 Objectives: This study aims to describe the demographic and clinical profile and ascertain the 7
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determinants of outcome among hospitalised COVID-19 adult patients enrolled in the National 9
113 Clinical Registry for COVID-19 (NCRC).
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115 Methods: NCRC is an on-going data collection platform operational in 42 hospitals across India. Data 13
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of hospitalized COVID-19 patients enrolled in NCRC between 1st September 2020 to 26th October 15
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119 Results: Analysis of 29,509 hospitalised, adult COVID-19 patients [mean (SD) age: 51.1 (16.2) year; 20
120 male: 18752 (63.6%)] showed that 15678 (53.1%) had at least one comorbidity. Among 25715 21
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121 (87.1%) symptomatic patients, fever was the commonest symptom (72.3%) followed by shortness of 23
122 breath (48.9%) and dry cough (45.5%). In-hospital mortality was 14.5% (n=3957). Adjusted odds of 24
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123 dying were significantly higher in age-group ≥60 years, males, with diabetes, chronic kidney diseases, 26
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chronic liver disease, malignancy, and tuberculosis, presenting with dyspnea and neurological 28
125 symptoms. WHO ordinal scale 4 or above at admission carried the highest odds of dying [5.6 (95% 29
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126 CI: 4.6, 7.0)]. Patients receiving one [OR: 0.5 (95% CI: 0.4, 0.7)] or two doses of anti-SARS CoV-2 31
127 vaccine [OR: 0.4 (95% CI: 0.3, 0.7)] were protected from in-hospital mortality.
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Conclusions: WHO ordinal scale at admission is the most important independent predictor for in- 36
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against mortality.
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132 Keywords: SARS-CoV2, mortality, risk factors, outcome, COVID-Vaccine
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Introduction
Globally, and in India, the pandemic of SARS-CoV-2 have resulted in unprecedented morbidity and mortality with detrimental effect on healthcare systems and economies. As a response to the pandemic, the ‘National Clinical Registry for COVID-19’ was initiated by the Indian Council of Medical Research (ICMR) in September 2020, with a broad objective to collect good quality, real- time data for evidence-based decision making in clinical practice, public health program and policy.
Hospital mortality among COVID-19 patients has varied from 19-39% across various studies.1–6 It has been observed that men, elderly (>60 years of age), and those with comorbidity such as asthma, chronic obstructive pulmonary disease, tuberculosis, pneumonia, diabetes mellitus, hypertension, renal, hepatic, and cardiac diseases and individuals with history of smoking or substance use, history of kidney transplant are at higher risk of developing severe disease or progression to death. The factors associated with such outcomes have been varied; older age being consistent among many populations while others varied amongst studies. 1–8
Indian investigations have reported association of old age, presence of diabetes mellitus, presence of severe acute respiratory infection, raised inflammatory markers including interleukin-6, ferritin, lactate dehydrogenase and d-dimer with progression of COVID and/ or related in-hospital mortality9– 12. Majority of these studies enrolled a small number of participants located at a single centre. Here, we present data from a large cohort of hospitalized COVID-19 patients from 42 hospitals across the country.
The aim of this analysis is to study the demographic profile, clinical characteristics, and outcomes among hospitalised COVID-19 adult patients, enrolled in the National Clinical Registry for COVID- 19 (NCRC) and to ascertain the factors associated with predefined outcomes.
Methods
The National Clinical Registry for COVID-19 (NCRC) is a platform for on-going prospective data collection, developed and maintained by ICMR in collaboration with the Ministry of Health & Family Welfare (MOHFW), Government of India, All India Institute of Medical Sciences, New Delhi (AIIMS) and the ICMR-National Institute of Medical Statistics (NIMS). The structure and protocol of the registry are available in the public domain (https://www.icmr. gov.in/tab1ar1.html). A hub and spoke model has been adopted for this registry. At the beginning, an expression of intent was invited for participation in the registry network; willing hospitals were screened based on a site feasibility matrix. A steering committee with subject experts guides the conduct of the registry and suggests solutions to roadblocks, if any. A monitoring committee consisting of institutional principal investigators oversees the progress of the registry and explores newer ideas and initiatives, to keep the registry dynamic. The central implementation team at ICMR headquarters remains responsible for the overall execution of the project.
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173 Across the network of NCRC, participating hospitals recruited consecutive in-patients, who had 4
174 COVID-19 infection confirmed by real time- polymerase chain reaction (RT-PCR), Nucleic acid 5
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175 amplification test (NAAT) or Rapid Antigen Test (RAT). Demographic, clinical and outcome data are 7
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collected in an on-going manner by the NCRC network. A dedicated team at the respective sites is 9
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178 investigator and the central implementation team at ICMR. All the researchers were trained by the 12
179 central implementation team at ICMR via an online platform. Regular refresher trainings are 13
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teams.
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182 Data is collected using a pre-structured case report form (CRF) and is entered into an electronic 18
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183 portal, which has been developed and is being maintained by the ICMR-NIMS, Delhi. The CRFs 20
184 include socio-demographic information, symptom and comorbidity profile at the baseline, clinical 21
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186 results of laboratory investigations conducted as per treating physician and the outcomes of the 24
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188 The database platform is hosted on a secure server and is audited by the National Informatics Centre 29
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encrypted through a valid certificate. Data loss is prevented by frequent backup runs.
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192 Data Analysis
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193 Socio-demographic, clinical, laboratory and hospital outcome data were analysed; categorical data 38
194 presented as frequency and proportions and continuous data as mean (standard deviation) or median 39
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195 (Inter-quartile range), as appropriate. Logistic regression model was used to determine the factors 41
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associated with the outcome of the patients. For the purpose of outcome analysis, death was defined 43
197 as death due to any cause of a COVID-19 positive patient occurring during hospital stay. Patients who 44
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199 analyses, though their baseline characteristics were analysed. Age, gender, body mass index, pre- 47
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200 existing comorbidities, lag between symptom onset and admission, laboratory parameters at 49
201 admission including lactate dehydrogenase (LDH), ferritin, d-dimer, C-reactive protein (CRP) and 50
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202 neutrophil to lymphocyte ratio (NLR), severity assessment by WHO ordinal scale13 and status of anti- 52
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covid19 vaccination were used as explanatory variables in univariate analysis. Chi Square test, t-test 54
204 or rank sum test was used to examine the association between explanatory variables and outcome, as 55
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206 importance were included in the multivariate logistic regression model. As laboratory values were 58
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available for a limited number of participants, separate models were used for each of the biomarkers. Data analysis was carried out using STATA v14 (College Station, TX, US).
Ethical Aspects
Approval was obtained from the Central Ethics Committee for Human Research at ICMR as well as from the respective Institutional Ethics Committee of each of the participating centres. Considering the observational nature of the registry, and collection of anonymised data being done primarily from the routine case records of the patients, a waiver of consent was granted by the Ethics Committees.
Results
We present here an analysis of 29,509 hospitalised COVID-19 patients over the age of 18 years who were enrolled in NCRC from 1st September 2020 till 26th October 2021. The mean ± SD age of the study population was 51.1 ± 16.2 years; men (51.1 ± 16 years) being similar to that of women (51 ± 16.5 years). Almost three fourth of the participants were at 40 years of age or above and two-thirds of the study participants in this group were men. The mean ± SD body mass index of the participants was 24.8 ± 4.1 kg/m2, with one-third of the participants being within normal range, while over 64% were obese or overweight. (Table 1) Four per cent of the enrolled study participants were health care workers.
Of the 29509 patients enrolled, 3794 (12.9%) were asymptomatic at the time of admission and were admitted due to conditions other than COVID-19 and later diagnosed to have COVID-19 or developed COVID-19 during the course of hospitalisation. Among 25715 (87.1%) patients who were admitted with symptoms, fever was the most common symptom (72.3%). Shortness of breath and dry cough was recorded in 48.9% and 45.5% of patients, respectively. Some of the other symptoms were fatigue (20.7%), cough with sputum (14.5%), sore throat (13.5%), muscle ache (12.3%) and headache (11.2%). (S1 Figure)
Median haemoglobin, leucocytes count, neutrophils and lymphocytes were largely within normal limits while the inflammatory markers were raised. (Table 1)
No comorbidities were present in 13831 (46.9%) patients; 15678 (53.1%) participants had at least one comorbidity. Hypertension and diabetes mellitus were the commonest comorbidities reported among 32.4% and 26.2% of patients, respectively. Chronic cardiac disease and chronic kidney disease was present among 5.7% and 3.6% of the study participants, respectively. Other diseases including asthma, malignancy, chronic pulmonary disease, chronic liver disease, stroke, tuberculosis, chronic neurological disease, rheumatologic disease, autoimmune disease, and haematological disorders, HIV infection, Hepatitis B and Hepatitis C infection were each reported in less than 2% of patients. (Figure 1)
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243 Figure 1: Comorbidity profile of patients, n=29509
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245 Image footnote: HIV: Human Immunodeficiency Virus, HBV: Hepatitis B Virus, HCV: Hepatitis C
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249 commonly used drugs, while hydroxychloroquine, oseltavimir, faviparavir, IL-6 inhibitors including 15
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250 tocilizumab or itolizumab and convalescent plasma each was administered to less than 5% of patients. 17
251 More than half of the admitted patients (15922, 54%) required oxygen support during their hospital 18
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Figure 2 shows the monthly trend of selected therapies since the inception of the registry. A marked 23
254 increase is noticeable in the use of steroid, oxygen supplementation and remdesivir in May 2021, 24
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256 2 infection. The use of hydroxychloquine considerably declined after September 2020, while the use 27
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Figure 2: Trends of selected drugs and oxygen requirements
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Outcome data on death or discharge were available for 27251 patients; 689 patients who left against 35 260
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262 analysis. In-hospital deaths were reported in 3957 (14.5%) participants and 23294 (85.5%) were 39
263 discharged. The median duration (IQR) of hospital stay among the study participants was 7 (5, 10) 40
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<0.001). Out of the 3957 patients who expired, 3418 (86.4%) died within first 14 days of hospital stay 44
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271 presentation and WHO ordinal scale of 4 and above were associated with higher mortality. Receipt of 54
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at least one dose of anti- SARS CoV-2 vaccine was associated with lower mortality as compared to 56
273 the unvaccinated patients [114 (13.3%) vs. 1306 (21.9%), p< 0.001]. Median concentrations of 57
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275 among the patients who died as compared to those who were discharged from hospital. (Table 3)
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On sub group analysis of patients with diabetes mellitus, malignancy, tuberculosis and those admitted with WHO ordinal scale 4 and above, mortality among vaccinated was significantly lower in each individual subgroup. Among patients with liver disease and kidney disease, though the mortality was lower among the vaccinated but the difference was not statistically significant (Not shown in table).
Factors that were significantly associated with mortality in univariate analysis and those which had clinical relevance were considered in the multivariate logistic models. Odds of in-hospital mortality was significantly and independently higher among patients ≥ 40 year, male gender, with diabetes mellitus, chronic kidney diseases, chronic liver disease, malignancy, and tuberculosis, and those who presented with dyspnoea or neurological symptoms, after being adjusted for other comorbidities such as hypertension, chronic cardiac disease, stroke, severity at admission (WHO ordinal scale), and vaccination status. WHO ordinal scale being 4 and above at admission carried the highest odds of dying [5.6 (95% CI: 4.6, 7.0)]. Patients vaccinated with one and two doses of anti-SARS CoV-2 vaccine had significantly lower odds of dying [OR: 0.5 (95% CI: 0.4, 0.7) for one dose & OR: 0.4 (95% CI: 0.3, 0.7) for two doses]. (Table 4)
Data available for various baseline laboratory parameters at baseline were limited (as described in table 3). Hence, separate models were tested for each of these parameters. The odds ratio for all biomarkers including haemoglobin, LDH, IL-6, and CRP were statistically significant, but marginally over 1. The odds of death increased by 1.1 for each unit rise in baseline values of NLR and D-dimer separately [OR:1.1 95%CI: 1.1,1.1] after adjusting for age, comorbidities and severity of the illness at admission. The logistic regression models (Model 2 to 8) that included laboratory values are presented as S2 Table. The area under the curve for ROC (AUC ROC) for NLR was 0.79 (95%CI: 0.78, 0.80) and 0.7 (0.68, 0.71). (S2 Figure) Considering the optimal cut-off for NLR as 6.67, both the sensitivity and specificity to classify in-hospital death was 72%; a cut-off of 0.82 mg/L for baseline D-Dimer had a sensitivity and specificity 71% and 65%, respectively.
Discussion
Our study includes data from 29509 hospitalised COVID-19 patients from 42 hospitals across the country. Apart from the often cited factors such as diabetes mellitus, male gender and advanced age, our study highlighted the association of other comorbidities such as chronic kidney disease, chronic liver disease, malignancy and tuberculosis with increased in-hospital mortality of COVID-19 patients in Indian settings. The importance of anti-SARS-CoV-2 vaccination in protecting against mortality was also evident from our analysis.
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310 More than half of our study participants had at least one co-morbidity, most common being 4
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311 hypertension & diabetes mellitus. The proportion of COVID-19 in-patients having hypertension is 6
312 similar to the overall population level frequency of hypertension recorded among adult Indians. On 7
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the contrary proportion diabetics seem to be much higher in this cohort than the national 9
314 average.13,14,15 Multiple studies have confirmed that following SARS-CoV-2 infection, diabetics are 10
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destruction of microbes, thus virus entry and decreased viral clearance.17
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In our study, patients above 40 years of age had 1.3 times higher adjusted odds of dying than the 17
319 younger patients, which increased to 2.1 time with advanced age ≥ 60 year. Advanced age, especially 18
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321 various studies across multiple countries since the onset of the pandemic.18,15 However, the working 22
322 age population above 40 years of age also have been deeply affected as shown in our investigation. It 23
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323 would be prudent to include them in all preventive measures and triaging strategies for severity. Age- 25
324 standardized mortalities for COVID-19 in India, analysed from the Integrated Disease Surveillance 26
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Programme special surveillance data, showed that along with the elderly above 60 years of age, the 28
326 age group of 45-59 years were also affected. 20 This could be partly explained by the fact that forced 29
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329 already dysregulated immune response.
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Studies from various parts of India and the world have reported co-morbidities in various 36
331 combinations to be associated with in-patient mortality in COVID-19 patients.16,173,24,25,26. Along with 37
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the more recognized risk factors such as diabetes mellitus, chronic kidney disease, and malignancy, 39
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333 our investigation unearthed the independent association of chronic liver disease with higher odds of 41
334 dying among COVID-19 in-patients. Another retrospective analysis from a single centre in South 42
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335 India linked chronic liver disease with in-hospital mortality of Covid-19 patients.27 Increased systemic 44
336 inflammation, immune dysfunction, coagulopathy and intestine dysbiosis are the hypothesised 45
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mechanisms. Important to note in this context is that, the pandemic has been associated with poor 47
338 eating habits and increased alcohol intake, which might lead to an increase in severity of liver 48
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340 Presence of tuberculosis (on-treatment TB) was an important factor associated with higher in-hospital 52
341 mortality in our cohort. This finding carries a significant relevance in a high TB burden country like 53
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342 India. Increased severity and mortality have been reported in COVID-19 patients with tuberculosis in 55
343 two metanalyses which included only 26 and 34 Indian patients, respectively 30,20. Our study provides 56
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more robust supportive evidence for association of present tuberculosis status withCOVID-19 58
345 mortality.
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Severity of illness at admission as evident by presenting complaints of respiratory or neurological symptoms and WHO ordinal scale 4 or above had higher odds of dying in our registry participants. Similar observations have been reported from a few single centre studies from India, where majority of non-survivors required early oxygen supplementation (oxygen requirement is at WHO ordinal scale 4 and above) 22,23.
The baseline laboratory markers including neutrophil-lymphocyte ratio (NLR), LDH, D-Dimer, IL-6 and CRP were higher among the non-survivors, though on multivariate analysis, the odds ratio was marginally above one with minimal clinical relevance, except for NLR and D-dimer. Previous studies have shown that raised biomarkers such as IL-6, CRP, LDH, and NLR are associated with higher mortality.32-35 Considering these markers are non-specific indicators of inflammation, the baseline values of Il-6, CRP or LDH seem to offer limited benefit in meaningful prediction of mortality. NLR being a readily available marker can be used to prognosticate outcomes at admission as can D-Dimer. However, guidelines for clinical management from other countries have also stated that there is no consensus in the evidence supporting use of any of the inflammatory markers or D-dimer at baseline to stratify the risk and decide therapeutics. 36
Importantly, the current study underlined the protection provided by COVID-19 vaccination against in-hospital mortality. COVID-19 vaccine, irrespective its type, reduced the odds of dying by 50% with one dose and by 60% with two doses. Other smaller, single centre studies from both South and North India have demonstrated the effectiveness of COVID-19 vaccination in reducing mortality.37,38 These findings along with mathematical modelling based projections39 underscore the key role of vaccine in mitigating the impact of COVID pandemic and managing the burden it poses on the healthcare system.
Limitations
As this was a record-based study in hospitals maintaining paper-based records, the identification of symptoms, comorbidities, complications and laboratory parameters relied on the accuracy of the records maintained. Secondly, the patients who were transferred to other institutes or who had left against medical advice were not followed up and could not be included in mortality analysis as their outcomes were unknown.
Strengths
The current analysis from the National Clinical Registry for COVID-19, to the best of our knowledge, is the largest widely representative to examine the association of demographic, clinical characteristics, and laboratory parameters with mortality among hospitalised COVID-19 patients in India. This data captures information from various geographical zones of India involving multiple centres.
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383 Conclusion
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384 The current investigation highlights the importance of age ≥ 40 years and comorbidities like chronic 6
385 liver disease, and tuberculosis as predictors of in-patient mortality along with the oft-reported risk 7
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factors such as male gender, diabetes mellitus, chronic kidney disease, and baseline severity of illness. 9
387 WHO ordinal scale 4 and above was an important independent factor associated with in-patient 10
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388 mortality. Interestingly, the baseline values of CRP, IL-6 and LDH offered little help in predicting the 12
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389 outcome, though NLR and D-dimer can be used to classify in-hospital outcomes with a sensitivity and 14
390 specificity ranging from 65% to 72%. On an encouraging note, vaccination against COVID-19 15
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clearly lowered the risk of dying from the disease and featured as an important armamentarium in our 17
392 fight against COVID-19 pandemic.
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Author statements
Author Contributions
SP is the guarantor. Study design, data analysis, data interpretation and manuscript writing team: AM, GK, AT, AB, TCB, PB, TDB, SM, AT, YR, MJ, JRK, AHP, SB, RJ, GRM, DS, VVR, BB, SP, AA. Monitoring and Conduct of the study: AM, GK, AT, LKS, PM, YP. Patient enrolment, conduct of study, clinical care and data collection: AB, TCB, PB, TDB, SM, AT, YR, MJ, JRK, AHP, SB, RJ, GDP, VS, KS, RM, VSA, MAM, DK, SS, SM, PKK, AK, AS, AP, SC, MD, TM, SC, BB, SRP, DM, SC, AA, DV, MT, NS, MP, SM, AD, KYL, MR, CGS, UKO, RRJ, AK, AP, AS, MP, LS, MR, ADS, LK, PP, ND, SD, JS, AM, LP, JPS, SS, VKK, AK, NY, RU, SS, AS, NNS, NMS, KR, HP, PRM, MKP, SS, AK, MP, MA, DP, VS, SA, RC, MR, ND, BKG, BK, JG, SB, AA, MS, NF, SP, VN, SC, SM, SKS, ST, PL, HD, AG, VK, NS, RV, AP, MPK, ABR, NK, RK, KM, YSR, AM, JC, MC, RKB, MAM, SK, PS, SG, AH.
Sources of Funding
The study was supported by the Indian Council of Medical Research, New Delhi.
Conflict of interest disclosure
AM, GK, AT, LKS, SP are employed by the Indian Council of Medical Research, the funding source of the study. AA was employed by the Indian Council of Medical research at the beginning of the study.
No other author has declared any conflict of interest.
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Table 1: Demographic, symptom and laboratory profile at the time of admission (n=29509)
Characteristic Values
Age groups
18-39 year
40-59 year
60 year and above
7742 (26.2) 11664 (39.5) 10103 (34.2)
Gender Male Female Transgender
18752 (63.6) 10754 (36.4) 3 (0.01)
Patients who had taken at least one dose of anti- SARS CoV-2 vaccine, n=7438
909 (12.2)
#BMI, kg/m2, mean ± SD, n=12046 Underweight
Normal
Overweight
Obese
24.8 ± 4.1 361 (3) 3863 (32.1) 2765 (22.9) 5057 (42)
Symptom onset to admission in days, Median (IQR) 4 (2,6)
Total bilirubin, mg/dL Median (IQR), n=15091 0.6 (0.4, 0.8)
Hemoglobin, g/dL (Mean ± SD), n= 18506 12.2 ± 2.2
WBC count (Cells /mm3), Median (IQR), n=18171 7400 (5200, 11000)
Neutrophils, % , Median (IQR), n=16053 76.5 (65.5, 85)
Lymphocytes, %, Median (IQR), n= 15971 17 (10, 26.6)
Neutrophil to lymphocyte ratio (NLR), Median (IQR), n=15942 4.4 (2.5, 8.5)
Platelet count, 1000s/ml3 Median (IQR), n=18089 212 (158, 278)
Ferritin, ng/mL, Median (IQR), n=7166 2059 (1019, 3192)
LDH, IU/L, Median (IQR), n=8515 400 (265, 649)
CRP, mg/dL, Median (IQR), n=9962 32 (7.7, 90.3)
IL-6, pg/mL Median (IQR), n=2192 17 (5.8, 53.3)
D-Dimer, mg/L, Median (IQR), n=8142 0.6 (0.3, 1.9)
admission,
n=26909
WHO Ordinal scale## on day 1 of 3
4 5 6 7
13860 (51.5) 9864 (36.7) 2580 (9.6) 526 (2)
79 (0.3)
Values expressed in n (%) unless specified.
#(Underweight: <18.5 kg/m2, normal weight: 18.5-22.9 kg/m2, overweight: 23-24.9 kg/m2, obese: ≥25 kg/m2) Ref: WHO expert consultation group. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet Public Health. 2004; 363 (9403):157-163. #https://www.who.int/blueprint/priority-diseases/key- action/COVID19_Treatment_Trial_Design_Master_Protocol_synopsis_Final_18022020.pdf
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Table 2: Proportional mortality among hospitalized COVID-19 patients
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Characteristic Mortality (%) Odds ratio (95% CI) P value
Age
18-39 years (n=7169)
40-59 years (n=10760)
60+ years (n=9322)
529 (7.4) 1455 (13.5) 1973 (21.2)
(Reference) 2 (1.8,2.2) 3.4 (3.0, 3.7)
– <0.001 <0.001
Gender
Male (n=17240)
Female (n=10008)
2613 (15.2) 1344 (13.4)
1.2 (1.1, 1.2) (reference)
<0.001 –
Vaccinated with Anti-SARS CoV-2 vaccine
Unvaccinated (n= 5964)
Vaccinated with one dose (n=550)
Vaccinated with two doses (n=305)
1306 (21.9) 85 (15.5) 29 (9.5)
(Reference) 0.7 (0.5, 0.8) 0.4 (0.3, 0.6)
<0.001 <0.001
Diabetes Mellitus
Yes (n=7126)
No (n=20125)
1397 (19.6) 2560 (12.7)
1.7 (1.6,1.8) (Reference)
<0.001
Hypertension
Yes (n=8872)
No (n=18379)
1686 (19) 2272 (12.4)
1.7 (1.6,1.8) (Reference)
<0.001
Chronic Cardiac Disease
Yes (n=1519)
No (n=25732)
296 (19.5) 3661 (14.2)
1.5 (1.3,1.7)
<0.001
Chronic Kidney Disease
Yes (n=934)
No (n=26317)
323 (34.6) 3634 (13.8)
3.3 (2.9,3.8) (Reference)
<0.001
Chronic Liver Disease
Yes (n=250)
No (n=27001)
80 (32) 3877 (14.4)
2.8 (2.1, 3.7) (Reference)
<0.001
Malignancy
Yes (n=413)
No (n=26838)
89 (21.6) 3868 (14.4)
1.6 (1.3,2.1) (Reference)
<0.001
Stroke
Yes (n=190)
No (n=27061)
64 (33.7) 3893 (14.4)
3.0 (2.2, 4.1) (Reference)
<0.001
Tuberculosis
Yes (n=169)
No (n=27082)
41 (24.3) 3916 (14.5)
1.9 (1.3, 2.7) (reference)
<0.001
Shortness of breath or fast breathing at admission
Yes (n=11798)
No (n=15453)
2772 (23.5) 1185 (7.7)
3.7 (3.4, 4.0) (Reference)
<0.001
Altered sensorium/ seizures at admission
Yes (n=412)
No (n=26839)
185 (44.9) 3772 (14.1)
5.0 (4.1, 6.1) (Reference)
<0.001
Ordinal scale 4 or above at admission
Yes (n=13860)
No (n=13049)
3101 (26.3) 433 (3.4)
10.3 (9.2, 11.4) (Reference)
<0.001
BMI
Underweight & normal (n= 3944)
Overweight & obese (n= 7307)
387 (9.8) 766 (10.5)
(Reference) 1.1 (0.9, 1.2)
– 0.26
*P value calculated by bivariate logistic regression
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Table 3: Median laboratory parameters among patients who died and those who survived
Laboratory Parameter Median (IQR) P Value
Hemoglobin Median (IQR), g/dL
Among those who died (n=2160)
Among survivors (n=15098)
12 (10.1, 13.4) 12.5 (11.1, 13.8)
<0.001
Random Blood Sugar, Median (IQR)
Among those who died (n=980)
Among the survivors (n=7003)
180 (132, 255) 138 (105, 228)
<0.001
Neutrophil lymphocyte Ratio Median (IQR)
Among those who died (n=1762)
Among survivors (n=13156)
10.7 (6.1, 19) 3.9 (2.3, 7.3)
<0.001
LDH Median (IQR), IU/L
Among those who died (n=1021)
Among survivors (n=6962)
690.6 (458, 959) 365 (248, 575)
<0.001
IL-6 Median (IQR), pg/mL
Among those who died (n=347)
Among survivors (n=1711)
59.2 (21, 180) 13 (4.7, 36.7)
<0.001
CRP Median (IQR), mg/dL
Among those who died (n=1231)
Among survivors (n=8156)
84.3 (39.6, 141.8) 25.1 (6.1, 78.6)
<0.001
D-Dimer Median (IQR), mg/L
Among those who died (n=1007)
Among survivors (n=6658)
1.6 (0.7, 5.9) 0.5 (0.3, 1.4)
<0.001
Symptom onset to admission
Among those who died (n=21382)
Among survivors (n=3502)
4 (2,6) 4 (2,6)
0.54
*p value calculated by rank sum test
Table 4: Adjusted odds ratio Determinants of hospital deaths using logistic regression
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Model 1 (n=6159)
Odds ratio (95% CI)
Age Categories
18-39 years
40-59 years
60 years and above
(Reference) 1.3 (1.1, 1.6) 1.9 (1.6, 2.3)
Gender (Male) 1.3 (1.1, 1.5)
Vaccinated with Anti-SARS CoV-2 vaccine
One dose
Two doses
0.5 (0.4, 0.7) 0.4 (0.3, 0.7)
Diabetes Mellitus 1.4 (1.2, 1.6)
Hypertension 0.9 (0.8, 1.1)
Chronic Cardiac Disease 0.8 (0.6, 1.0)
Chronic Kidney Disease 2.8 (2.0, 3.7)
Chronic Liver Disease 2.4 (1.1, 5.2)
Malignancy 1.9 (1.2, 3.2)
Stroke 1.0 (0.5, 2.3)
Tuberculosis 3.0 (1.5, 6.1)
Shortness of breath or fast breathing 1.3 (1.1, 1.5)
Altered sensorium/ seizures 3.6 (2.4, 5.4)
WHO ordinal scale 4 and above at admission 5.6 (4.6, 7.0)
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Figure 1: Comorbidity profile of patients, n=29509 151x107mm (118 x 118 DPI)
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Manuscripts submitted to QJM: An International Journal of Medicine
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Manuscripts submitted to QJM: An International Journal of Medicine
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Figure 2: Trends of selected drugs and oxygen requirements 194x137mm (118 x 118 DPI)
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deemagclinic
deemag and chest clinic 