Implementation and Updating of Clinical Prediction Models: A Systematic Review

Abstract

Objective

To summarize the implementation approaches and updating methods of clinically implemented models and consecutively advise researchers on the implementation and updating.

Patients and Methods

We included studies describing the implementation of prognostic binary prediction models in a clinical setting. We retrieved articles from Embase, Medline, and Web of Science from January 1, 2010, to January 1, 2024. We performed data extraction, based on Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis and Prediction Model Risk of Bias Assessment guidelines, and summarized.

Results

The search yielded 1872 articles. Following screening, 37 articles, describing 56 prediction models, were eligible for inclusion. The overall risk of bias was high in 86% of publications. In model development and internal validation, 32% of the models was assessed for calibration. External validation was performed for 27% of the models. Most models were implemented into the hospital information system (63%), followed by a web application (32%) and a patient decision aid tool (5%). Moreover, 13% of models have been updated following implementation.

Conclusion

Impact assessments generally showed successful model implementation and the ability to improve patient care, despite not fully adhering to prediction modeling best practice. Both impact assessment and updating could play a key role in identifying and lowering bias in models.