Published models that predict hospital readmission: a critical appraisal

Introduction The number of readmission risk prediction models available has increased rapidly, and these models are used extensively for health decision-making. Unfortunately, readmission models can be subject to flaws in their development and validation, as well as limitations in their clinical usefulness. Objective To critically appraise readmission models in the published literature using Delphi-based recommendations for their development and validation. Methods We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. Guided by CAMPR, two researchers independently appraised published readmission models in two recent systematic reviews and concurrently extracted data to generate reference lists of eligibility criteria and risk factors. Results We found that published models (n=81) followed 6.8 recommendations (45%) on average. Many models had weaknesses in their development, including failure to internally validate (12%), failure to account for readmission at other institutions (93%), failure to account for missing data (68%), failure to discuss data preprocessing (67%) and failure to state the model's eligibility criteria (33%). Conclusions The high prevalence of weaknesses in model development identified in the published literature is concerning, as these weaknesses are known to compromise predictive validity. CAMPR may support researchers, clinicians and administrators to identify and prevent future weaknesses in model development.

Automated summary

The study critically appraised readmission models in the literature using expert recommendations, finding that many models had weaknesses in development such as lack of internal validation, consideration of readmission at other institutions, missing data, discussion of data preprocessing, and stating eligibility criteria. The high prevalence of these weaknesses identified in published models may compromise predictive validity, and CAMPR may help to identify and prevent future weaknesses in model development.