Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Richard Riley
  • Thomas P A Debray
  • Gary S. Collins

Abstract

Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad-hoc manner. This fails to address the fact that the performance of a CPMs worsen over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, “living” (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.

Bibliographical metadata

Original languageEnglish
JournalDiagnostic and Prognostic Research
Publication statusAccepted/In press - 7 Dec 2020