Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction

Abstract

Objective Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance. Methods This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance. Results Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695–0.7]; ref) to AUC = 0.708 (95% CI [0.705–0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4). Discussion We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction. Conclusion These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

“…SDOH integration and use maturation will catalyze clinical operations and research improvement gains in many ways. Actionable, standardized, EHR-based SDOH collection will offer rich, robust data for (1) mature analytics for clinical operations; (2) real-time clinical predictive analytics feeding back into EHRs for clinical decision support [3,[59][60][61]; (3) secondary research use internally and for multi-site sharing, including for point-of-care trials and observational studies; (4) machine learning/artificial intelligence research leveraging "big data" [62,63]; and (5) more accurate SDOH profiles for new healthcare and health equity interventions to inform and drive policy changes [14,54,64,65]. All are necessary components of mature, full-cycle translational science in a learning health system [61,[66][67][68][69][70][71].…”

Readmission Risk Based on Debility and Psychosocial Measures: The Western 9 Algorithm

“…Many studies have stressed the importance of psycho-social determinants of health such as food insecurity, housing instability, and education level, as important drivers of health outcomes [1]. However, few studies have examined whether the addition of these determinants into risk prediction models improves risk prediction accuracy, especially for culturally and linguistically diverse groups.…”