ArticleBMC medical research methodology2020
Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.Current diabetes reports · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundThere is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches.
methodsWe conducted a retrospective cohort study of 17,284 adult diabetes patients with 44,203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CWGEE) using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes.
resultsThe readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population's readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P < 0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6000-8000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples.
conclusionsHospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.
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