Improving Cardiovascular Risk Prediction: A Tale of Sisyphus

Survival analysis in prognostic studies has usually relied on Kaplan– Meier and/or multivariable Cox proportional hazard models. Earlier studies suggesting the superiority of 24 h ambulatory blood pressure (ABP) over and above office blood pressure (BP), and the incremental prognostic value of night-time ABP are no exception. One of the most important limitations of conventional survival analysis is the censoring of the survival time at the end of the observation period, for subjects not experiencing an event of interest. During the last decade, methodologies integrating the concept of competing risk analysis have gained popularity for remediation of this limitation. For prognosis-oriented research questions, Fine and Gray developed a sub-distribution hazard model. For more aetiology-oriented research questions, multivariable cause-specific proportional hazard (sometimes termed cause-specific Cox regression) models have been recommended. Applying competing risk analysis is precisely what Mortensen et al. have done in their analysis of the large International Database on Ambulatory blood pressure monitoring in relation to Cardiovascular Outcomes (IDACO). In addition, they developed a prediction model of 10 year person-specific absolute risks of cardiovascular (CV) mortality and CV events. It is a retrospective multi-centre analysis on six cohorts of subjects representing the general population of various countries: Copenhagen (Denmark), Ohasama (Japan), Noorderkempen (Belgium), Uppsala (Sweden), Montevideo (Uruguay), and Maracaibo (Venezuela). On a total of 10 425 subjects, 7929 were considered to have adequate BP readings and sufficient data on covariates and outcome to be included. Mean age was 57 years, mean body mass index was 26 kg/m, and mean office systolic BP was 135 mmHg. Prevalence of diabetes and treated hypertension was 9 and 23%, respectively. Of note, mean systolic BP was 147 mmHg in Uppsala and 171 mmHg (! ) in Maracaibo, the other means being around 130 mmHg. Minimum median follow-up was 9.1 years. During the follow-up period, a total of 563 participants died from CV events and 758 died from non-CV events. Further, a total of 1173 were diagnosed with a fatal or non-fatal CV event. Without doubt, it is a large database with long and detailed follow-up. For the present analyses, the authors removed six of the original studies included in the IDACO database, presumably because of a too short follow-up period. Earlier, in part of the original IDACO database (Belgium, Denmark, Japan, and Sweden), ABP proved superior to office BP in predicting CV but not total and non-CV mortality. When two correlated variables such as ABP and office BP are entered in a classical Cox regression model, one of them may appear more important than the other. The background idea of the study under scrutiny is that a somewhat higher hazard ratio for an endpoint (as usually occurs for ABP vs. office BP) does not necessarily translate into better long-term prediction for an individual person (personspecific absolute risk models). It is a well-accepted proposition. The long-term prediction of a person’s risk not only depends on the association of BP with CV events but also on the competing risk of nonCV mortality. In the study of Mortensen et al., cause-specific Cox regression models failed to show clinically relevant added value for 24 h ABP in 10 year risk prediction in a model containing office and 24 h ABP next to common risk indicators. Moreover, night-time BP did not improve 10 year prediction obtained from daytime measurements. For an otherwise healthy population, sufficient prognostic accuracy for CV risks can be achieved with office BP. The interpretation of aetiological research in the presence of competing risks should bear in mind that, because of competing events handled as censored observations, the number of patients at risk during follow-up decreases (removal from the remaining risk sets). The