Journal Article

Early prediction of level-of-care requirements in patients with COVID-19

Breen, Kerry, Paschalidis, Ioannis Ch, Hao, Boran, Xu, Tingting, Wang, Taiyao, Velmahos, George C, Sotudian, Shahabeddin, Hu, Yang, Gaitanidis, Apostolos

2020

Overview

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease. The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients’ vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available.

• Publisher: eLife Sciences Publications, Ltd,eLife Sciences Publications Ltd
• Subject: Adult / Aged / Area Under Curve / artificial intelligence / Betacoronavirus / Body Mass Index / Comorbidity / Coronavirus Infections - epidemiology / Coronavirus Infections - therapy / COVID-19 / critical care / Diabetes Mellitus - epidemiology / Female / Health Services Needs and Demand / Hospitalization - statistics & numerical data / Humans / Intensive Care Units - statistics & numerical data / Intensive Care Units - supply & distribution / machine learning / Male / Massachusetts - epidemiology / Medicine / Microbiology and Infectious Disease / Middle Aged / Nonlinear Dynamics / Pandemics / Pneumonia, Viral - epidemiology / Pneumonia, Viral - therapy / Procedures and Techniques Utilization / Respiration, Artificial - statistics & numerical data / Risk Factors / risk prediction / ROC Curve / SARS-CoV-2 / Ventilators, Mechanical - supply & distribution