Social Determinants of Health and Limitation of Life-Sustaining Therapy in Neurocritical Care: A CHoRUS Pilot Project

Background: Social determinants of health (SDOH) have been linked to neurocritical care outcomes. We sought to examine the extent to which SDOH explain differences in decisions regarding life-sustaining therapy, a key outcome determinant. We specifically investigated the association of a patient's home geography, individual-level SDOH, and neighborhood-level SDOH with subsequent early limitation of life-sustaining therapy (eLLST) and early withdrawal of life-sustaining therapy (eWLST), adjusting for admission severity.

Methods: We developed unique methods within the Bridge to Artificial Intelligence for Clinical Care (Bridge2AI for Clinical Care) Collaborative Hospital Repository Uniting Standards for Equitable Artificial Intelligence (CHoRUS) program to extract individual-level SDOH from electronic health records and neighborhood-level SDOH from privacy-preserving geomapping. We piloted these methods to a 7 years retrospective cohort of consecutive neuroscience intensive care unit admissions (2016-2022) at two large academic medical centers within an eastern Massachusetts health care system, examining associations between home census tract and subsequent occurrence of eLLST and eWLST. We matched contextual neighborhood-level SDOH information to each census tract using public data sets, quantifying Social Vulnerability Index overall scores and subscores. We examined the association of individual-level SDOH and neighborhood-level SDOH with subsequent eLLST and eWLST through geographic, logistic, and machine learning models, adjusting for admission severity using admission Glasgow Coma Scale scores and disorders of consciousness grades.

Results: Among 20,660 neuroscience intensive care unit admissions (18,780 unique patients), eLLST and eWLST varied geographically and were independently associated with individual-level SDOH and neighborhood-level SDOH across diagnoses. Individual-level SDOH factors (age, marital status, and race) were strongly associated with eLLST, predicting eLLST more strongly than admission severity. Individual-level SDOH were more strongly predictive of eLLST than neighborhood-level SDOH.

Conclusions: Across diagnoses, eLLST varied by home geography and was predicted by individual-level SDOH and neighborhood-level SDOH more so than by admission severity. Structured shared decision-making tools may therefore represent tools for health equity. Additionally, these findings provide a major warning: prognostic and artificial intelligence models seeking to predict outcomes such as mortality or emergence from disorders of consciousness may be encoded with self-fulfilling biases of geography and demographics.