Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

Chansik An,Seong Woo Kim,Hyunsun Lim,Jung Hyun Chang,Dong-Wook Kim,Yoon Jung Choi

doi:10.1038/s41598-020-75767-2

Abstract

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

Highlights

A pneumonia of unknown cause detected in Wuhan, China was first reported to the World Health Organization (WHO) on December 31, 2019
The mean (± standard deviation [SD]) age was 44.97 (± 19.79) years; patients who died of COVID-19 were significantly older than those who recovered, with the mean (± SD) age being 78.17 (± 10.96) years and 43.06 (± 18.32) years, respectively
Our results demonstrate that machine learning models utilizing sociodemographic characteristics and medical history can accurately predict the prognosis of COVID-19 patients after diagnosis; the models predict the final outcome and early mortality (i.e., 14- or 30-day mortality)

Summary

Introduction

A pneumonia of unknown cause detected in Wuhan, China was first reported to the World Health Organization (WHO) on December 31, 2019. COVID-19 is the third known zoonotic coronavirus disease after SARS and the Middle East Respiratory Syndrome (MERS)[2]. Because of the rapid spread of the virus, there has been a sharp increase in the demand for medical resources required to support infected people. Despite the desperate efforts to contain the disease and slow down its spread, Medicine and Rehabilitation, National Health Insurance Service Ilsan Hospital, Goyang, Korea. When allocating limited medical resources, prediction models that estimate the risk of a poor outcome in an infected individual based on pre-diagnosis information could help to effectively triage patients. Information regarding the sociodemographic characteristics and history of medical service use of virtually all Koreans is available in the database where currently information regarding COVID-19 patients is periodically updated

Objectives

Methods

Results

Conclusion