Evaluating Social Determinants of Health Variables in Advanced Analytic and Artificial Intelligence Models for Cardiovascular Disease Risk and Outcomes: A Targeted Review.

Autor:	Snowdon JL; Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142., Scheufele EL; Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142., Pritts J; Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142., Le PT; Division of Integrative Biological and Behavioral Sciences, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892., Mensah GA; Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892., Zhang X; Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892., Dankwa-Mullan I; Center for Artificial Intelligence, Research, and Evaluation, IBM Watson Health, Cambridge, MA 02142.
Jazyk:	angličtina
Zdroj:	Ethnicity & disease [Ethn Dis] 2023 Mar 31; Vol. 33 (1), pp. 33-43. Date of Electronic Publication: 2023 Mar 31 (Print Publication: 2023).
DOI:	10.18865/1704
Abstrakt:	Introduction/purpose: Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018-2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD. Methods: PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen's kappa was used to determine interrater reliability. Results: The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed. Conclusions: Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making. Competing Interests: Conflict of Interest No conflicts of interest reported by authors.
Databáze:	MEDLINE
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