The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Predicting risk of rupture and rupture-preventing re-intervention utilising repeated measures on aneurysm sac diameter following endovascular abdominal aortic aneurysm repair

Background: Clinical and imaging surveillance practices following endovascular aneurysm repair (EVAR) for intact abdominal aortic aneurysm (AAA) vary considerably and compliance with recommended lifelong surveillance is poor. This study developed a dynamic prognostic model to enable stratification of patients at risk of future secondary rupture or rupture preventing re-intervention (RPR) to enable the development of personalised surveillance intervals. Method: Baseline data and repeat measurements of post-operative aneurysm sac diameter from the EVAR-1 and EVAR-2 trials were used to develop the model with external validation in a cohort from Helsinki. Longitudinal mixed-effects models were fitted to trajectories of sac diameter and model-predicted sac diameter and rate of growth were used in prognostic Cox proportional hazards models. Results: 785 patients from the EVAR trials were included of which 155 (20%) suffered at least one rupture or RPR during follow-up. An increased risk was associated with pre-operative AAA size, rate of sac growth, and the number of previously detected complications. A prognostic model using only predicted sac growth had good discrimination at 2-years (C-index = 0.68), 3- years (C-index= 0.72) and 5-years (C-index= 0.75) post-operation and had excellent external validation (C-indices 0.76 to 0.79). After 5-years post-operation, growth rates above 1mm/year had a sensitivity of over 80% and specificity over 50% in identifying events occurring within 2 years. Conclusion: Secondary sac growth is an important predictor of rupture or RPR. A dynamic prognostic model has the potential to tailorsurveillance by identifying a large proportion of patients who may require less intensive follow-up

Estimated association between deprivation and the rate of next disease transition.

Panels (a) to (e) show the HRs for the association of second to fifth IMD quintiles with the diagnosis of CVD, T2D, CKD, HF, and MH, respectively, grouped by the number of existing conditions and comorbidity status. The first IMD quintile (the least deprived group) is treated as the reference category. Estimated HRs are shown by black dots, with the associated 95% CIs represented by coloured bands. For transitions into each condition, the superimposed dotted lines represent meta-regression fits based on the corresponding HRs for each IMD category. Abbreviations: CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure; HR, hazard ratio; IMD, the English Index of Multiple Deprivation; imd2–imd5, second–fifth IMD quintiles; MH, mental health conditions; T2D, type 2 diabetes.</p

Baseline sociodemographic characteristics of study participants.

Baseline sociodemographic characteristics of study participants.</p

Estimated association between age and the rate of next disease transition.

Panels (a) to (e) show the HRs for the association of age at entry to the current state (per 10 years) with the diagnosis of CVD, T2D, CKD, HF, and MH, respectively, grouped by the number of existing conditions and comorbidity status. Estimated HRs are shown by orange dots, with the associated 95% CIs represented by black bands. For transitions into each condition, the superimposed dotted line represents meta-regression fit based on the corresponding HRs. Abbreviations: CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure; HR, hazard ratio; MH, mental health conditions; T2D, type 2 diabetes.</p

Estimated association between ethnicity and the rate of next disease transition.

Panels (a) to (e) show the HRs for the association of non-White ethnic groups with the diagnosis of CVD, T2D, CKD, HF, and MH, respectively, grouped by the number of existing conditions and comorbidity status. White ethnicity is treated as the reference category. Estimated HRs are shown by black dots, with the associated 95% CIs represented by coloured bands. For transitions into each condition, the superimposed dotted lines represent meta-regression fits based on the corresponding HRs for each ethnicity category. Abbreviations: CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure; HR, hazard ratio; MH, mental health conditions; T2D, type 2 diabetes.</p

Supporting information for the main manuscript.

Fig A. Flowchart for sample selection. Text A. The multistate modelling approach. Text B. Model checking. Text C. Meta-regression. Table A. Summary of disease states by sociodemographic characteristics. Table B. Summary of disease transitions. Table C. Estimated hazard ratios, 95% confidence intervals, and p-values from the main multistate analysis. Fig B. Plots of the estimated cumulative hazard of the Cox–Snell residuals. Fig C. Estimated association between each sociodemographic characteristic and the rate of CVD or MH diagnosis based on their respective narrower definitions. Fig D. Results of the multistate analysis obtained with COPD replacing HF. Fig E. Results of the multistate analysis with COPD included as the sixth condition. Fig F. Estimated association between deprivation (IMD deciles) and the rate of disease transition by number of existing conditions and comorbidity status. Fig G. Results of the multistate analysis obtained based on the expanded cohort including patients with missing ethnicity information. Fig H. Results of the multistate analysis with transitions specified via semiparametric Cox models. COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; HF, heart failure; IMD, the English Index of Multiple Deprivation; MH, mental health conditions. (DOCX)</p

Code lists for ethnicity and the conditions considered in our study.

Code lists for ethnicity and the conditions considered in our study.</p

Estimated association between gender and the rate of next disease transition.

Panels (a) to (e) show the HRs for the association of gender (male) with the diagnosis of CVD, T2D, CKD, HF, and MH, respectively, grouped by the number of existing conditions and comorbidity status. The female gender is treated as the reference category. Estimated HRs are shown by orange dots, with the associated 95% CIs represented by black bands. For transitions into each condition, the superimposed dotted line represents meta-regression fit based on the corresponding HRs. Abbreviations: CI, confidence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HF, heart failure; HR, hazard ratio; MH, mental health conditions; T2D, type 2 diabetes.</p

The multistate diagram depicting disease progression.

The initial state, labelled “None,” represents participants with no comorbidities at the start of the study. Intermediate comorbidity states are colour coded as follows: cardiovascular disease (CV, orange), type 2 diabetes (T2, yellow), chronic kidney disease (CK, brown), mental health conditions (MH, blue), and heart failure (HF, purple). Each coloured arrow indicates a transition, with the colour corresponding to the disease that is diagnosed when the transition takes place (for instance, orange arrows indicate transitions into a CVD diagnosis). The terminal state, “Death,” is directly reachable from all other states. The “n” below each comorbidity state denotes the total number of participants who entered that state during the study period.</p