Abstract
BackgroundCOVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU.ObjectiveThe aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care.MethodsUsing a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations.ResultsOur experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00).ConclusionsOur results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.
Highlights
COVID-19 was first discovered in December 2019 in China and has since rapidly spread to over 200 countries [1]
In terms of predictive performance (Table 3), we found that the overall best identified models by AUC were gradient boosting http (XGB) for predicting SARS-CoV-2 test results, random forest (RF) for predicting hospital admissions for patients who are SARS-CoV-2 positive, and support vector machine (SVM) for predicting intensive care unit (ICU) admission for patients who are SARS-CoV-2 positive with AUCs of 0.66, 0.92, and 0.98, respectively
We found that the differences in predictive performance between the best XGB model for predicting SARS-CoV-2 test results and the other predictive models was significant at a prespecified significance level of α=.05 (t test) for all but the area under the precision recall curve (AUPR) metric, where neural network (NN) achieved a significantly better AUPR of 0.22, and the difference to SVM was not significant at the prespecified significance level
Summary
COVID-19 was first discovered in December 2019 in China and has since rapidly spread to over 200 countries [1]. A clinical predictive model that accurately identifies patients that are likely to test positive for SARS-CoV-2 a priori could help prioritize limited SARS-CoV-2 testing capacity. Developing accurate clinical prediction models for SARS-CoV-2 is difficult as relationships between clinical data, hospitalization, and intensive care unit (ICU) admission have not yet been established conclusively due to the recent emergence of SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU
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