Evaluating the impact of prediction models: lessons learned, challenges, and recommendations
Teus H Kappen1, Wilton A van Klei1, Leo van Wolfswinkel1
1Division of Anesthesiology, Intensive Care and Emergency Medicine, University Medical Center Utrecht, Utrecht University, P.O. Box 85500, Mail stop F.06.149, 3508 GA Utrecht, The Netherlands.
Diagnostic and Prognostic Research
|May 17, 2019
Summary
Conducting prospective impact studies for clinical prediction models is crucial but resource-intensive. Researchers should carefully consider model preparation, prediction presentation, and study design to ensure positive impacts on clinical decision-making and patient outcomes.
Area of Science:
- Clinical Epidemiology
- Health Informatics
- Medical Decision Making
Background:
- Clinical prediction models aim to improve healthcare decisions and patient outcomes.
- Quantifying model impact typically involves prospective comparative studies, often cluster-randomized trials.
- These impact studies can be time-consuming and resource-intensive.
Purpose of the Study:
- To evaluate lessons learned from two prospective impact studies of a clinical prediction model in surgical patients.
- To provide guidance for researchers on preparing prediction models for practice.
- To inform the selection of appropriate impact study designs.
Main Methods:
- Conducted two distinct prospective impact studies on a clinical prediction model for surgical patients.
- Analyzed experiences to identify key considerations for future impact studies.
- Focused on model implementation, prediction presentation, and study design choices.
Main Results:
- Prospective impact studies offer valuable insights into prediction model effectiveness.
- Careful preparation of the model and its predictions is essential for successful implementation.
- Choosing the right study design is critical for accurately assessing impact.
Conclusions:
- Researchers should carefully weigh the benefits and resource requirements of prospective impact studies.
- Guidance is provided on optimizing prediction model implementation and study design.
- Informing decisions on conducting large-scale impact studies for clinical prediction models.
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