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

Objectives: To target interventions to prevent readmission, we sought to develop clinical prediction models for 30-day readmission among children with complex chronic conditions (CCCs).

Methods: 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.