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. 2021 Aug 30:8:100251.
doi: 10.1016/j.ajpc.2021.100251. eCollection 2021 Dec.

Development and validation of a polysocial risk score for atherosclerotic cardiovascular disease

Zulqarnain Javed  1 , Javier Valero-Elizondo  2   3 , Ramzi Dudum  4 , Safi U Khan  2 , Prachi Dubey  5 , Adnan A Hyder  6 , Jiaqiong Xu  2   7 , Usama Bilal  8   9 , Bita A Kash  7   10   11 , Miguel Cainzos-Achirica  1   2   3 , Khurram Nasir  1   2   3
Affiliations

Development and validation of a polysocial risk score for atherosclerotic cardiovascular disease

Zulqarnain Javed et al. Am J Prev Cardiol. .

Abstract

Objective: To date, the extent to which social determinants of health (SDOH) may help identify individuals with atherosclerotic cardiovascular disease (ASCVD) - beyond traditional risk factors - has not been quantified using a cumulative social disadvantage approach. The objective of this study was to develop, and validate, a polysocial risk score (PsRS) for prevalent ASCVD in a nationally representative sample of adults in the United States (US).

Methods: We used data from the 2013-2017 National Health Interview Survey. A total of 38 SDOH were identified from the database. Stepwise and criterion-based selection approaches were applied to derive PsRS, after adjusting for traditional risk factors. Logistic regression models were fitted to assign risk scores to individual SDOH, based on relative effect size magnitudes. PsRS was calculated by summing risk scores for individual SDOH, for each participant; and validated using a separate validation cohort.

Results: Final sample comprised 164,696 adults. PsRS included 7 SDOH: unemployment, inability to pay medical bills, low income, psychological distress, delayed care due to lack of transport, food insecurity, and less than high school education. PsRS ranged from 0-20 and exhibited excellent calibration and discrimination. Individuals with the highest PsRS (5th quintile) had nearly 4-fold higher ASCVD prevalence, relative to those with the lowest risk scores (1st quintile). Area under receiver operating curve (AU-ROC) for PsRS with SDOH alone was 0.836. Addition of SDOH to the model with only demographic and clinical risk factors (AU-ROC=0.852) improved overall discriminatory power, with AU-ROC for final PsRS (demographics + clinical + SDOH) = 0.862.

Conclusions: Cumulatively, SDOH may help identify individuals with ASCVD, beyond traditional cardiovascular risk factors. In this study, we provide a unique validated PsRS for ASCVD in a national sample of US adults. Future study should target development of similar scores in diverse populations, and incorporate longitudinal study designs.

Keywords: ASCVD; ASCVD, atherosclerotic cardiovascular disease; Cardiovascular disease; Polysocial risk score; PsRS, polysocial risk score; SDOH, social determinants of health; Social determinants of health.

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Conflict of interest statement

Dr. Nasir is on the advisory board of Amgen (CA), Esperion (MI) and Novartis (NJ), and his research is partly supported by the Jerold B. Katz Academy of Translational Research (TX). No other conflicts of interest relevant to the content of this manuscript were reported by the authors. The funding source had no involvement in the preparation of this article.

Figures

Fig 1
Fig. 1
Distribution of Aggregate SDOH Burden, Overall and by ASCVD Status. Abbreviations: SDOH, social determinants of health; ASCVD, atherosclerotic cardiovascular disease.
Fig 2
Fig. 2
Prevalence of ASCVD by PsRS Quintiles Abbreviations: PsRS, polysocial risk score; ASCVD, atherosclerotic cardiovascular disease
Central Illustration 1
Central Illustration 1
Improving ASCVD Prediction Using A Unique Polysocial Risk Score (PsRS) Abbreviations: OR=Odds Ratio; PsRS, Polysocial Risk Score; SDOH, Social Determinants of Health; ASCVD, Atherosclerotic Cardiovascular Disease Top left: Association between individual SDOH and ASCVD; top right: distribution of PsRS, by SDOH domains; bottom left: Receiver Operating Curves (ROC), showing improved model discrimination with SDOH; bottom right: ASCVD distribution by percentiles of PsRS Base Model = age + sex + race + cardiovascular risk factors; Final Model = Base Model + 7 SDOH, obtained using various model selection approaches for PsRS derivation (see Methods: PsRS Derivation); All (38 SDOH) Model: Base Model + Initial Set of 38 SDOH (see Methods: Exploratory Analysis).
See this image and copyright information in PMC

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