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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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 |
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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 |
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