Implementation and Updating of Clinical Prediction Models: A Systematic Review.

Area of Science:

• Clinical Informatics
• Health Services Research
• Biostatistics

Background:

• Prognostic binary prediction models are increasingly implemented in clinical settings.
• Effective implementation and updating strategies are essential for maximizing model utility and ensuring patient safety.

Purpose of the Study:

• To summarize implementation approaches and updating methods for clinically used prediction models.
• To provide guidance for researchers on model implementation and updating.

Main Methods:

• Systematic literature search of Embase, Medline, and Web of Science (Jan 2010 - Jan 2024).
• Data extraction using Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment (PROBAST) guidelines.
• Synthesis of findings on implementation, validation, and updating practices.

Main Results:

• 37 articles describing 56 models were included; 86% had high risk of bias.
• Only 32% assessed calibration during development/internal validation; 27% underwent external validation.
• Models were primarily implemented in hospital information systems (63%), web applications (32%), or patient decision aids (5%).
• Only 13% of models were updated post-implementation.

Conclusions:

• Despite high bias and suboptimal validation, implemented models demonstrated successful impact on patient care.
• Impact assessments and model updating are critical for identifying and mitigating bias.
• Adherence to best practices in prediction modeling is needed to improve reliability and clinical utility.