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Assessing and Augmenting Predictive Models for Hospital Readmissions With Novel Variables in an Urban Safety-net Population

Bibliographic Data

ID9102407
AuthorsPatrick Ryan (0000-0002-9727-2138, Department of General Internal Medicine, corresponding author), Anna Furniss (0000-0001-5498-607X, Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus), Kristin Breslin (Ambulatory Care Services, Community Health Services, Denver Health & Hospital Authority, Denver), Rachel Everhart (0000-0003-4899-9768, Ambulatory Care Services, Community Health Services, Denver Health & Hospital Authority, Denver), Rebecca Hanratty (0000-0002-1721-5919, Department of General Internal Medicine, corresponding author), John Rice (0000-0002-3923-4424, Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus)
Year2021
Volume59
Issue12
Pages1107-1114
Publication date2021-12-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000001653
PMID34593712
OpenAlexW3202993195
LanguageEN
Citations received1
References cited43

BACKGROUND: The performance of existing predictive models of readmissions, such as the LACE, LACE+, and Epic models, is not established in urban safety-net populations. We assessed previously validated predictive models of readmission performance in a socially complex, urban safety-net population, and if augmentation with additional variables such as the Area Deprivation Index, mental health diagnoses, and housing access improves prediction. Through the addition of new variables, we introduce the LACE-social determinants of health (SDH) model. METHODS: This retrospective cohort study included adult admissions from July 1, 2016, to June 30, 2018, at a single urban safety-net health system, assessing the performance of the LACE, LACE+, and Epic models in predicting 30-day, unplanned rehospitalization. The LACE-SDH development is presented through logistic regression. Predictive model performance was compared using C-statistics. RESULTS: A total of 16,540 patients met the inclusion criteria. Within the validation cohort (n=8314), the Epic model performed the best (C-statistic=0.71, P<0.05), compared with LACE-SDH (0.67), LACE (0.65), and LACE+ (0.61). The variables most associated with readmissions were (odds ratio, 95% confidence interval) against medical advice discharge (3.19, 2.28-4.45), mental health diagnosis (2.06, 1.72-2.47), and health care utilization (1.94, 1.47-2.55). CONCLUSIONS: The Epic model performed the best in our sample but requires the use of the Epic Electronic Health Record. The LACE-SDH performed significantly better than the LACE and LACE+ models when applied to a safety-net population, demonstrating the importance of accounting for socioeconomic stressors, mental health, and health care utilization in assessing readmission risk in urban safety-net patients

EPIC · Health care · MEDLINE · Mental health · Population · Predictive modelling · Sample (material) · Socioeconomic status · Chronic Disease Management Strategies · Frailty in Older Adults · Heart Failure Treatment and Management

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Unique citing works1
Citations per year1
Citation span2026 - 2026 (1)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 1

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