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Area of Science:

  • Health Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Routinely collected health data (RCD) are increasingly used for clinical research and risk prediction.
  • Heterogeneity in data recording and patient populations across clinical sites poses challenges for reliable risk prediction.
  • Existing risk prediction models may not fully account for site-specific variations.

Purpose of the Study:

  • To assess the reliability of individual risk predictions derived from RCD, considering inter-site heterogeneity.
  • To evaluate the performance of a random effects model compared to standard QRISK3 predictions for cardiovascular disease (CVD).

Main Methods:

  • Utilized a large dataset of 3.6 million patients from 392 sites in the Clinical Practice Research Datalink.
  • Employed Cox models incorporating QRISK3 predictors and a frailty (random effect) term for each site to address unmeasured site variability.
  • Analyzed variations in data recording (e.g., BMI missingness) and CVD incidence rates across general practices.

Main Results:

  • Significant variation observed in data recording completeness (BMI missingness: 18.7%-60.1%) and CVD incidence rates (0.4-1.3 per 100 patient-years) between practices.
  • Individual CVD risk predictions from the random effects model showed inconsistency with QRISK3 predictions (e.g., 10% QRISK3 risk had a 95% range of 7.2%-13.7% with random effects).
  • Random variability accounted for only a small portion of the observed prediction inconsistency; however, the random effects model demonstrated equivalent discrimination and calibration to QRISK3.

Conclusions:

  • Risk prediction models using RCD perform adequately at a population level but exhibit considerable uncertainty for individual predictions.
  • The heterogeneity between clinical sites in data and populations significantly affects the reliability of individual risk estimates.
  • Clinicians and patients require clear communication regarding the inherent uncertainty in individual risk predictions derived from routinely collected data.