Comparison of chest X-ray radiography AI model to comorbidities for predicting intensive care unit admission for COVID-19.

Abstract

Purpose: There are practical limitations to using comorbidities alone in predicting the need for admission to the intensive care unit (ICU). We compared the classification performance of a deep learning/artificial intelligence (DL/AI) model on chest X-ray radiography (CXR) for predicting admission of a patient to the ICU to using two common comorbidity indices, using data collected during the COVID-19 pandemic as a use case.

Approach: CXR imaging studies and clinical data of patients who tested positive for COVID-19 between February 2020 and January 2022 were retrospectively collected, yielding 8357 CXR imaging studies from 5046 patients. Classification performance by a DL/AI model in the task of predicting ICU admission within 24 h of imaging was compared to (a) the Charlson comorbidity index (CCI) and (b) the age-adjusted version of the Charlson comorbidity index (ACCI) using the area under the receiver operating characteristic curve (AUC). The AUC from each comorbidity index was compared with the DL/AI model, with a Bonferroni-corrected significance level of $P=0.025$ .

Results: The prediction of ICU admission using the DL/AI model (median AUC [95% CI]: 0.78, [0.74, 0.81]) demonstrated statistical superiority to using the CCI and ACCI comorbidity indices with improvements in AUC of 0.16 ( $P<0.001$ ) and 0.12 ( $P<0.001$ ), respectively.

Conclusions: A DL/AI model on CXR for predicting ICU admission within 24 h of imaging obtained superior performance compared with two clinical comorbidity indices in a use case. This work serves as a use case to demonstrate the potential for some medical imaging deep learning models to help improve patient care and resource planning for ICU departments.