Abstract
Background Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.
Objectives To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.
Methods In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined (“stacked”) to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.
Results While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66–0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64–0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63–0.66]), HF (c-statistics: 0.61 [95% CI: 0.60–0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56–0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47–0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63–0.64].
Conclusion PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.
Keywords
pharmacoepidemiology - clinical prediction model - hospital readmission - claims data - potentially inappropriate prescribing
