Dynamic Prediction Modeling Approaches for Cardiac Surgery

Abstract

This loss of calibration, the agreement between observed and predicted event rates, has been found to have occurred over Background-The calibration of several cardiac clinical prediction models has deteriorated over time. We compare different model fitting approaches for in-hospital mortality after cardiac surgery that adjust for cross-sectional case mix in a heterogeneous patient population.

A review of statistical updating methods for clinical prediction models

“…Following Hickey et al 13 , the need for updating of ES is likely to be attributed to a change in case-mix and various characteristics in the data that have changed over the years. In particular, the case-mix adjusted mortality rate had decreased substantially.…”

“…Dynamic updating refers to the continuous updating of one 13 or multiple 37,38 CPMs , as opposed to the previous approaches that are only conducted at fixed time points. As a result, the coefficients for an updated CPM are continuously varying with time.…”

“…Therefore, a simplified discrete proposal on the choice of forgetting parameters has been suggested for computational feasibility 38 . DM incorporates historical data, which is likely to provide a smooth and stable update to the coefficients 13 . Most of the applications so far are explanatory in nature to study the relationship between outcome and predictors, less so for the prediction purpose.…”

“…the obesity epidemic accelerating the force of diabetes morbidity); unmeasured risk factors for disease and treatment outcomes; treatments; treatment settings; adjunct treatments and wider healthcare; and data quality. Therefore, to remain valid, CPMs must evolve over time -either by renewing or updating the model at discrete timepoints 12 , or by allowing the CPM to operate dynamically, updating continuously in an online fashion 13 .…”

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators

“…This hinges on an interesting aspect not covered in this report – of diagnostic measures for assessing and only implementing recalibration when it is really needed. For a risk score predicting the mortality from cardiac surgery, repeated updates were performed to overcome the issue of calibration drift [38, 39]. Changes in the coefficients of the risk model were monitored for different temporal updating schemes, but performance measures for discrimination and calibration were not investigated.…”