Why the study?
To demonstrate the gain in predictive performance when cardiovascular disease risk prediction tools incorporate repeated rather than only single measurements of risk factors.
Does a Bayesian joint model using repeated measures of risk factors improve the prediction of future MACE compared to a standard Cox model in patients with type 2 diabetes?
Does a Bayesian joint model using repeated measures of risk factors improve the prediction of future MACE compared to a standard Cox model in patients with type 2 diabetes?
Incorporating repeated measurements of cardiovascular risk factors into predictive models significantly improves the prediction of future MACE in patients with type 2 diabetes compared to using single timepoint measurements.
Repeated measures may sharpen MACE prediction in diabetes; leaves open whether this changes clinical decisions or requires prospective validation.
AIM: To demonstrate the gain in predictive performance when cardiovascular disease (CVD) risk prediction tools (RPTs) incorporate repeated rather than only single measurements of risk factors. MATERIALS AND METHODS: We used data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial to compare the quality of predictions of future major adverse cardiovascular events (MACE) with the Cox proportional hazards model (using single values of risk factors) compared to the Bayesian joint model (using repeated measures of risk factors). The risk of MACE was calculated in patients with type 2 diabetes with and without established CVD. We assessed the predictive ability of the following cardiovascular risk factors: glycated haemoglobin, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides, estimated glomerular filtration rate, low-density lipoprotein cholesterol (LDL-C), total cholesterol, and systolic blood pressure (SBP) using the time-dependent area under the receiver-operating characteristic curve (aROC) for discrimination and the time-dependent Brier score for calibration. RESULTS: In participants without history of CVD, the aROC of SBP increased from 0.62 to 0.69 when repeated rather than only single measurements of SBP were incorporated into the predictive model. Similarly, the aROC increased from 0.67 to 0.80 when repeated rather than only single measurements of both SBP and LDL-C were incorporated into the predictive model. For all other investigated cardiovascular risk factors, the measures of discrimination and calibration both improved when using the joint model as compared to the Cox proportional hazards model. The improvement was evident in participants with and without history of CVD but was more pronounced in the latter group. CONCLUSIONS: The analysis demonstrates that the joint modelling approach, considering trajectories of cardiovascular risk factors, provides superior predictive performance compared to standard RPTs that use only a single timepoint.
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Oulhaj et al. (2023) studied this question.
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