Predicting risk factor control in patients with coronary heart disease using patients' individual and large-area residential characteristics: results from the ESC-EORP EUROASPIRE V survey. (25th November 2020) - Moerschel, K; De Bacquer, D; De Backer, G; Wood, D; Kotseva, K; Wellmann, J; Prugger, C
Abstract: Introduction: The impact of small-area residential characteristics on the development of coronary heart disease (CHD) and cardiovascular risk factors is well established. Evidence is so far lacking on the predictive value of large-area residential characteristics for cardiovascular risk. Purpose: In CHD patients, we aimed to study the predictive value of large-area residential characteristics beyond patients' individual characteristics for the control of major cardiovascular risk factors (blood pressure, cholesterol and smoking). Methods: Patients hospitalised for coronary events or interventions from 16 European countries were interviewed and examined for risk factor control (EUROASPIRE V survey). Based on patients' postal codes, we merged individual patient data with large-area residential data routinely provided by Eurostat for NUTS regions (Nomenclature of Territorial Units for Statistics). We selected patient characteristics recorded at hospitalisation (2015–16) and socioeconomic characteristics of their residential NUTS 2 and 3 regions (2015–16) to predict risk factor control at interview (2016–17). We developed risk prediction models using multi-level logistic regression: 1) baseline models (BMs) with patient data only and 2) extended models (EMs) with patient data (level 1) and large-area residential data (level 2). We employed multiple imputation to overcome sparse data and internally validated results using cross-validation. We used the c-index corresponding to the area under the curve as performance measure to assess the discriminative ability of prediction models. Results: Data from 2562 CHD patients in 16 countries could be linked to 60 NUTS 2 and 121 NUTS 3 regions by postal codes. Median time between hospitalisation and interview was 14 (range 6 to 28) months. BMs included 34 patient variables, covering demographic, socioeconomic and clinical characteristics, and EMs additionally included 11 regional socioeconomic variables concerning gross domestic product, income, education, occupation, population density, and health care. For blood pressure control, BMs and EMs showed validated c-indices ranging from 0.71 to 0.73 and from 0.73 to 0.77, respectively. Analyses for cholesterol control yielded c-indices ranging from 0.69 to 0.70 in BMs and from 0.71 to 0.73 in EMs. For smoking cessation, the c-indices ranged from 0.80 to 0.84 in BMs and from 0.83 to 0.84 in EMs. Conclusions: Prediction models based on CHD patients' individual characteristics showed a high discriminative ability regarding the control of major cardiovascular risk factors. Further consideration of large-area residential characteristics provided an additive predictive value, markedly increasing the discriminative ability of prediction models for blood pressure and cholesterol control. Socioeconomic characteristics of CHD patients' residential regions can thus help identify patients requiring more intense risk factor management. Funding Acknowledgement: Type of funding source: Foundation. Main funding source(s): German Heart Foundation