Cargando…

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits

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 m...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929610/
https://www.ncbi.nlm.nih.gov/pubmed/35298502
http://dx.doi.org/10.1371/journal.pone.0264785
_version_ 1784670896658579456
author 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
author_facet 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
author_sort Alle, Shanmukh
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8929610
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-89296102022-03-18 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 PLoS One Research Article 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. Public Library of Science 2022-03-17 /pmc/articles/PMC8929610/ /pubmed/35298502 http://dx.doi.org/10.1371/journal.pone.0264785 Text en © 2022 Alle et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
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
COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title_full COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title_fullStr COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title_full_unstemmed COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title_short COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits
title_sort covid-19 risk stratification and mortality prediction in hospitalized indian patients: harnessing clinical data for public health benefits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8929610/
https://www.ncbi.nlm.nih.gov/pubmed/35298502
http://dx.doi.org/10.1371/journal.pone.0264785
work_keys_str_mv AT alleshanmukh covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT kanakanakshay covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT siddiquisamreen covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT gargakshit covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT karthikeyanakshaya covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT mehtapriyanka covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT mishraneha covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT chattopadhyaypartha covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT devipriti covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT waghdhareswati covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT tyagiakansha covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT taraibansidhar covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT hazarikpranjalpratim covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT daspoonam covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT budhirajasandeep covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT nangiavivek covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT dewanarun covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT sethuramanramanathan covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT subramanianc covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT srivastavamashrin covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT chakravarthiavinash covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT jacobjohnny covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT namagirimadhuri covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT konalavarma covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT dashdebasish covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT sethitavpritesh covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT jhasujeet covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT agrawalanurag covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT pandeyrajesh covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT vinodpk covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits
AT priyakumarudeva covid19riskstratificationandmortalitypredictioninhospitalizedindianpatientsharnessingclinicaldataforpublichealthbenefits