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COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits.
Alle, Shanmukh; Kanakan, Akshay; Siddiqui, Samreen; Garg, Akshit; Karthikeyan, Akshaya; Mehta, Priyanka; Mishra, Neha; Chattopadhyay, Partha; Devi, Priti; Waghdhare, Swati; Tyagi, Akansha; Tarai, Bansidhar; Hazarik, Pranjal Pratim; Das, Poonam; Budhiraja, Sandeep; Nangia, Vivek; Dewan, Arun; Sethuraman, Ramanathan; Subramanian, C; Srivastava, Mashrin; Chakravarthi, Avinash; Jacob, Johnny; Namagiri, Madhuri; Konala, Varma; Dash, Debasish; Sethi, Tavpritesh; Jha, Sujeet; Agrawal, Anurag; Pandey, Rajesh; Vinod, P K; Priyakumar, U Deva.
Afiliação
  • Alle S; Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India.
  • Kanakan A; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Siddiqui S; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Garg A; Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India.
  • Karthikeyan A; Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, Telangana, India.
  • Mehta P; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Mishra N; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Chattopadhyay P; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Devi P; Intel Technology India Private Limited, Bangalore, Karnataka, India.
  • Waghdhare S; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Tyagi A; Intel Technology India Private Limited, Bangalore, Karnataka, India.
  • Tarai B; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Hazarik PP; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Das P; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Budhiraja S; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Nangia V; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Dewan A; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Sethuraman R; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Subramanian C; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Srivastava M; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Chakravarthi A; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Jacob J; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Namagiri M; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Konala V; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Dash D; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Sethi T; Indraprastha Institute of Information Technology Delhi, New Delhi, India.
  • Jha S; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Agrawal A; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
  • Pandey R; Max Super Speciality Hospital (A Unit of Devki Devi Foundation), Max Healthcare, Delhi, India.
  • Vinod PK; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
  • Priyakumar UD; INtegrative GENomics of HOst-PathogEn (INGEN-HOPE) laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
PLoS One ; 17(3): e0264785, 2022.
Article em En | MEDLINE | ID: mdl-35298502
ABSTRACT
The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 / Hospitalização Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 / Hospitalização Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Índia
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