An early warning tool for predicting mortality risk of COVID-19 patients using machine learning

TL;DR

This study developed a machine learning-based nomogram and score to predict mortality risk in COVID-19 patients using blood biomarkers and demographics, enabling early intervention and resource allocation.

Contribution

It introduces a novel predictive model and score (LNLCA) for COVID-19 mortality risk based on key blood biomarkers and demographic data.

Findings

High predictive accuracy with AUC of 0.961 and 0.991 in derivation and validation cohorts.

Identified key biomarkers: lactate dehydrogenase, neutrophils, lymphocytes, C-reactive protein, and age.

Patients categorized into low, moderate, and high-risk groups for mortality prediction.

Abstract

COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.