Key result
Potentially inappropriate prescribing predicted all-cause 90-day readmission with a c-statistic of 0.63 (95% CI 0.63-0.64) when integrating risks from disease-specific models.
Why the study?
Existing readmission prediction models rely on hospital-specific data fields that are not readily transferable, whereas standardized statutory health insurance routine data could enable automatic identification of readmission risks.
Can potentially inappropriate prescribing (PIP) criteria predict 90-day hospital readmissions using routine health insurance claims data?
Observational
Yes
Can potentially inappropriate prescribing (PIP) criteria predict 90-day hospital readmissions using routine health insurance claims data?
Potentially inappropriate prescribing criteria can be used in machine-learning models to predict 90-day hospital readmissions using routine health insurance claims data.
PIP criteria may support claims-based readmission prediction; hypothesis-generating and should not yet change practice.
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.
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Gerharz et al. (2022) conducted an observational in Acute myocardial infarction, heart failure, stroke/TIA/atrial fibrillation, COPD, type-2 diabetes, and osteoporosis. Potentially inappropriate prescribing (PIP) via STOPP/START criteria was evaluated on All-cause readmission within 90 days (c-statistic 0.63, 95% CI 0.63-0.64). Potentially inappropriate prescribing predicted all-cause 90-day readmission with a c-statistic of 0.63 (95% CI 0.63-0.64) when integrating risks from disease-specific models.
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