Screening for frequent hospitalization risk among community-dwelling older adult between 2016 and 2023 : machine learning-driven item selection, scoring system development, and prospective validation

Abstract

Background: Screening for frequent hospitalizations in the community can help prevent super-utilizers from growing in the inpatient population. However, the determinants of frequent hospitalizations have not been systematically examined, their operational definitions have been inconsistent, and screening among community members lacks tools. Nor do we know if what determined frequent hospitalizations before COVID-19 continued to be the determinant of frequent hospitalizations at the height of the pandemic. Hence, the current study aims to identify determinants of frequent hospitalization and their screening items developed from the Comprehensive Geriatric Assessment (CGA), as our 273-item CGA is too lengthy to administer in full in community or primary care settings. The stability of the identified determinants will be examined in terms of the prospective validity of pre-COVID-selected items administered at the height of the pandemic. Methods: Comprehensive Geriatric Assessments (CGAs) were administered between 2016 and 2018 in the homes of 1,611 older adults aged 65+ years. Learning models were deployed to select CGA items to maximize the classification of different operational definitions of frequent hospitalizations, ranging from the most inclusive definition, wherein two or more hospitalizations over 2 years, to the most exclusive, wherein two or more hospitalizations must appear during year two, reflecting different care needs. In addition, the CGA items selected by the best-performing learning model were then developed into a random-forest-based scoring system for assessing frequent hospitalization risk, the validity of which was tested during 2018 and again prospectively between 2022 and 2023 in a sample of 329 older adults recruited from a district adjacent to where the CGAs were initially performed. Results: Seventeen items were selected from the CGA by our best-performing algorithm (DeepBoost), achieving 0.90 AUC in classifying operational definitions of frequent hospitalizations differing in temporal distributions and care needs. The number of medications prescribed and the need for assistance with emptying the bowel, housekeeping, transportation, and laundry were selected using the DeepBoost algorithm under the supervision of all operational definitions of frequent hospitalizations. On the other hand, reliance on walking aids, ability to balance on one’s own, history of chronic obstructive pulmonary disease (COPD), and usage of social services were selected in the top 10 by all but the operational definitions that reflect the greatest care needs. The prospective validation of the original risk-scoring system using a sample recruited from a different district during the COVID-19 pandemic achieved an AUC of 0.82 in differentiating those rehospitalized twice or more over 2 years from those who were not. Conclusion: A small subset of CGA items representing one’s independence in aspects of (instrumental) activities of daily living, mobility, history of COPD, and social service utilization are sufficient for community members at risk of frequent hospitalization. The determinants of frequent hospitalization represented by the subset of CGA items remain relevant over the course of COVID-19 pandemic and across sociogeography.