Developing Prediction Models for 30-Day Unplanned Readmission Among Children With Medical Complexity.

Hospital Pediatrics
Jana C LearyKaren M Freund

Abstract

To target interventions to prevent readmission, we sought to develop clinical prediction models for 30-day readmission among children with complex chronic conditions (CCCs). After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model. We assessed model performance using c-statistic and calibration curves and internal validation using bootstrapping. We then created readmission risk scoring systems from final model β-coefficients. Of the 2296 index admissions involving children with CCCs, 188 (8.2%) had unplanned 30-day readmissions. The model with admission characteristics included previous admissions, previous emergency department visits, number of CCC categories, and medical versus surgical admission (c-statistic 0.65). The model with hospitalization and discharge factors added discharge disposition, length of stay, and weekday discharge to the admission variables (c-statistic 0.67). Bootstrap samples had similar c-stati...Continue Reading

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Citations

Feb 23, 2020·Hospital Pediatrics·Thom TaylorDaria Salyakina
Aug 21, 2020·Journal of Pediatric Psychology·Louise E VazKatharine E Zuckerman
Jan 7, 2021·BMC Neurology·Ryan O'ConnellLouis Ehwerhemuepha
Sep 19, 2021·Hospital Pediatrics·Karen PughLouis Ehwerhemuepha

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