TY - JOUR
T1 - Joint longitudinal and time-to-event modelling compared with standard Cox modelling in patients with type 2 diabetes with and without established cardiovascular disease
T2 - An analysis of the EXSCEL trial
AU - Oulhaj, Abderrahim
AU - Aziz, Faisal
AU - Suliman, Abubaker
AU - Iqbal, Nayyar
AU - Coleman, Ruth L.
AU - Holman, Rury R.
AU - Sourij, Harald
N1 - Funding Information:
EXSCEL was conducted jointly by the Duke Clinical Research Institute and the University of Oxford Diabetes Trials Unit, in an academic collaboration with the sponsor Amylin Pharmaceuticals, a wholly owned subsidiary of AstraZeneca. Harald Sourij and Abderrahim Oulhaj had full access to the analysis dataset and are the guarantors of this work. Rury R. Holman is an Emeritus National Institutes of Health Research Senior Investigator.
Publisher Copyright:
© 2023 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - cardiovascular disease
KW - joint longitudinal modelling
KW - major adverse cardiovascular events
KW - type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85147423598&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147423598&partnerID=8YFLogxK
U2 - 10.1111/dom.14975
DO - 10.1111/dom.14975
M3 - Article
C2 - 36635232
AN - SCOPUS:85147423598
SN - 1462-8902
VL - 25
SP - 1261
EP - 1270
JO - Diabetes, Obesity and Metabolism
JF - Diabetes, Obesity and Metabolism
IS - 5
ER -
