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Prediction models: the right tool for the right problem.

Teus H Kappen1, Linda M Peelen

  • 1aDivision of Anesthesiology, Intensive Care and Emergency Medicine, University Medical Center Utrecht, Utrecht, The Netherlands bDepartment of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA cJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.

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Summary
This summary is machine-generated.

Clinicians can evaluate perioperative prediction models by understanding their intended use and relevant performance metrics. This ensures models are appropriate for clinical practice and enhance personalized patient care.

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Biostatistics

Background:

  • Perioperative prediction models offer personalized risk assessments for patients and providers.
  • The technical nature of prediction model literature poses challenges for clinical adoption.
  • This review aims to demystify prediction model assessment for clinicians.

Purpose of the Study:

  • To equip clinicians with the knowledge to critically evaluate the utility of prediction models in their practice.
  • To provide insights into assessing the performance and generalizability of clinical prediction models.
  • To guide clinicians in determining the suitability of prediction models for implementation.

Main Methods:

  • Review of recent advancements in prediction model performance characteristics.
  • Analysis of evolving understanding of prediction model limitations and generalizability.
  • Framework for assessing the clinical applicability of prediction models.

Main Results:

  • New performance metrics are continuously being developed for prediction models.
  • Enhanced understanding of the limitations associated with traditional performance characteristics.
  • Improved insights into the generalizability of prediction models across diverse populations and settings.

Conclusions:

  • Clinicians should first define the specific application of a prediction model.
  • Identifying relevant performance characteristics is crucial for model assessment.
  • Evaluating the alignment of scientific evidence with clinical practice is key for implementation.