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• W3132656205 abstract "Abstract Introduction 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. Methods: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. Over 50 Clinical and Laboratory parameters were studied based on the patient’s initial clinical state at admission and laboratory parameters within the first 24 hours. The Machine Learning (ML) modelling was performed using Gradient Boosting Algorithm. ‘Time to event’ using Cox Proportional Hazard Model was used and combined with Gradient Boosting 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. Results: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 – 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. Conclusion: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." @default.
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• W3132656205 date "2021-02-16" @default.
• W3132656205 modified "2023-09-25" @default.
• W3132656205 title "Development and Validation of a multivariable prediction model using Machine Learning to predict the outcome of admitted COVID-19 patients at the time of admission (AICOVID)" @default.
• W3132656205 doi "https://doi.org/10.21203/rs.3.rs-225458/v1" @default.
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