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

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.

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 $P=0.025$ .

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.

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.