Developing Machine Learning Models for Predicting Intensive Care Unit Resource Use During the COVID-19 Pandemic
Abstract
Importance
The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed.
Objective
We investigate whether Machine Learning (ML) can be used for predictions of intensive care requirements 5 and 10 days into the future.
Design
Retrospective design where health Records from 34,012 SARS-CoV-2 positive patients was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days ( n = 5, 10).
Setting
Two Danish regions, encompassing approx. 2.5 million citizens.
Participants
All patients from the bi-regional area with a registered positive SARS-CoV-2 test from March 2020 to January 2021.
Main outcomes
Prediction of future 5- and 10-day requirements of ICU admission and ventilator use. Mortality was also predicted.
Results
Models predicted 5-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) of 0.986 and 5-day risk of use of ventilation with an ROC-AUC of 0.995. The corresponding 5-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R 2 ) of 0.930 and use of ventilation with an R 2 of 0.934. Performance was comparable but slightly reduced for 10-day forecasting models.
Conclusions
Random Forest-based modelling can be used for accurate 5- and 10-day forecasting predictions of ICU resource requirements.
Funding
The study was funded by grants from the Novo Nordisk Foundation to MS (#NNF20SA0062879 and #NNF19OC0055183) and MN (#NNF20SA0062879).
The foundation took no part in project design, data handling and manuscript preparation.
KEY POINTS
Question
Can machine learning models (ML) be used for predicting hospital and intensive care unit (ICU) resource requirements, and thus assist in logistics crisis management during the COVID-19 pandemic?
Findings
Retrospective study of the resource use of 34.012 COVID-19 patients during the first and second COVID-19 wave in Denmark. ML models were trained for the purpose of predicting the number of patients needing ICU admission and ventilators 5 and 10 day after their first positive SARS-CoV-2 test. The study demonstrates that ML models can accurately predict intensive care admission requirements with 5-day area under the receiver operator characteristic curve (ROC-AUC) of 0.986 and need for ventilator support with a ROC-AUC of 0.995. 10-day predictions were comparable.
Meaning
The study demonstrates that ML modelled could be a useful tool for hospital planners during crisis management, including the current COVID-19 pandemic.
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