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AIMS In patients with coronary heart disease (CHD), we investigated whether it is possible to accurately assess the probability of short-term control of risk factors (blood pressure, cholesterol, smoking) based on individual and large-area residential characteristics. METHODS AND RESULTS We merged individual data of participants from EUROASPIRE V who were hospitalized for CHD (2014-2017) and interviewed and examined for risk factor control (2016-2017), with large-area residential data provided by Eurostat for Nomenclature of Territorial Units for Statistics (NUTS) regions using postal codes. Data from 2562 CHD patients in 16 countries were linked to data from 60 NUTS 2 and 121 NUTS 3 regions. The median time between hospitalization and interview was 14 months. We developed prediction models to assess the probability of risk factor control at interview using data from the time of hospitalization: (i) baseline models including 35 variables on patients' demographic, clinical, and socio-economic characteristics and (ii) extended models additionally considering nine variables on large-area residential characteristics. We calculated and internally validated c-indices to assess the discriminative ability of prediction models. Baseline models showed good discrimination with c-indices of 0.69, 0.70, and 0.76 for blood pressure control, cholesterol control, and smoking cessation, respectively. Extended models for blood pressure, cholesterol, and smoking yielded improved c-indices of 0.72, 0.71, and 0.78, respectively. CONCLUSION Our results indicate that the probability of risk factor control in CHD patients can be accurately assessed using individual and large-area residential characteristics, allowing for an identification of patients who are less likely to achieve risk factor targets.