Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)

Abstract In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the...

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Autores principales:	Sujoy Kar, Rajesh Chawla, Sai Praveen Haranath, Suresh Ramasubban, Nagarajan Ramakrishnan, Raju Vaishya, Anupam Sibal, Sangita Reddy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4bb232ea10b045828e4c97aab2c2c3d3
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spelling	oai:doaj.org-article:4bb232ea10b045828e4c97aab2c2c3d32021-12-02T17:41:04ZMultivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)10.1038/s41598-021-92146-72045-2322https://doaj.org/article/4bb232ea10b045828e4c97aab2c2c3d32021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92146-7https://doaj.org/toc/2045-2322Abstract In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired—8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient’s initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. ‘Time to event’ using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired—8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52–3.53], Male Gender (HR 1.72, 95% CI 1.06–2.85), Respiratory Distress (HR 1.79, 95% CI 1.32–2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83–1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72–5.38), Coronary Artery Disease (HR 1.56, 95% CI − 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03–2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87–4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23–2.32), INR (HR 1.71, 95% CI 1.31–2.13), LDH (HR 4.02, 95% CI 2.66–6.07) and Ferritin (HR 2.48, 95% CI 1.32–4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on ‘time to event’ at the admission, accurately predicting patient outcomes.Sujoy KarRajesh ChawlaSai Praveen HaranathSuresh RamasubbanNagarajan RamakrishnanRaju VaishyaAnupam SibalSangita ReddyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sujoy Kar Rajesh Chawla Sai Praveen Haranath Suresh Ramasubban Nagarajan Ramakrishnan Raju Vaishya Anupam Sibal Sangita Reddy Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
description	Abstract In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individualized mortality risk scores based on the anonymized clinical and laboratory data at admission and determine the probability of Deaths at 7 and 28 days. Data of 1393 admitted patients (Expired—8.54%) was collected from six Apollo Hospital centers (from April to July 2020) using a standardized template and electronic medical records. 63 Clinical and Laboratory parameters were studied based on the patient’s initial clinical state at admission and laboratory parameters within the first 24 h. The Machine Learning (ML) modelling was performed using eXtreme Gradient Boosting (XGB) Algorithm. ‘Time to event’ using Cox Proportional Hazard Model was used and combined with XGB Algorithm. The prospective validation cohort was selected of 977 patients (Expired—8.3%) from six centers from July to October 2020. The Clinical API for the Algorithm is http://20.44.39.47/covid19v2/page1.php being used prospectively. Out of the 63 clinical and laboratory parameters, Age [adjusted hazard ratio (HR) 2.31; 95% CI 1.52–3.53], Male Gender (HR 1.72, 95% CI 1.06–2.85), Respiratory Distress (HR 1.79, 95% CI 1.32–2.53), Diabetes Mellitus (HR 1.21, 95% CI 0.83–1.77), Chronic Kidney Disease (HR 3.04, 95% CI 1.72–5.38), Coronary Artery Disease (HR 1.56, 95% CI − 0.91 to 2.69), respiratory rate > 24/min (HR 1.54, 95% CI 1.03–2.3), oxygen saturation below 90% (HR 2.84, 95% CI 1.87–4.3), Lymphocyte% in DLC (HR 1.99, 95% CI 1.23–2.32), INR (HR 1.71, 95% CI 1.31–2.13), LDH (HR 4.02, 95% CI 2.66–6.07) and Ferritin (HR 2.48, 95% CI 1.32–4.74) were found to be significant. The performance parameters of the current model is at AUC ROC Score of 0.8685 and Accuracy Score of 96.89. The validation cohort had the AUC of 0.782 and Accuracy of 0.93. The model for Mortality Risk Prediction provides insight into the COVID Clinical and Laboratory Parameters at admission. It is one of the early studies, reflecting on ‘time to event’ at the admission, accurately predicting patient outcomes.
format	article
author	Sujoy Kar Rajesh Chawla Sai Praveen Haranath Suresh Ramasubban Nagarajan Ramakrishnan Raju Vaishya Anupam Sibal Sangita Reddy
author_facet	Sujoy Kar Rajesh Chawla Sai Praveen Haranath Suresh Ramasubban Nagarajan Ramakrishnan Raju Vaishya Anupam Sibal Sangita Reddy
author_sort	Sujoy Kar
title	Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
title_short	Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
title_full	Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
title_fullStr	Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
title_full_unstemmed	Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)
title_sort	multivariable mortality risk prediction using machine learning for covid-19 patients at admission (aicovid)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4bb232ea10b045828e4c97aab2c2c3d3
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Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID)

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