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Predictive model for atrial fibrillation in hypertensive diabetic patients
Author(s) -
Abellana Rosa,
GonzalezLoyola Felipe,
VerduRotellar JoseMaria,
Bustamante Alejandro,
Palà Elena,
CluaEspuny Josep Lluis,
Montaner Joan,
Pedrote Alonso,
ValGarcia Jose Luis,
Ribas Segui Domingo,
Muñoz Miguel Angel
Publication year - 2021
Publication title -
european journal of clinical investigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.164
H-Index - 107
eISSN - 1365-2362
pISSN - 0014-2972
DOI - 10.1111/eci.13633
Subject(s) - atrial fibrillation , medicine , cardiology , stroke (engine) , diabetes mellitus , population , cohort , blood pressure , valvular heart disease , heart failure , myocardial infarction , kidney disease , proportional hazards model , mechanical engineering , environmental health , engineering , endocrinology
Background Several scores to identify patients at high risk of suffering atrial fibrillation have been developed. Their applicability in hypertensive diabetic patients, however, remains uncertain. Our aim is to develop and validate a diagnostic predictive model to calculate the risk of developing atrial fibrillation at five years in a hypertensive diabetic population. Methods The derivation cohort consisted of patients with both hypertension and diabetes attended in any of the 52 primary healthcare centres of Barcelona; the validation cohort came from the 11 primary healthcare centres of Terres de l’Ebre (Catalonia South) from January 2013 to December 2017. Multivariable Cox regression identified clinical risk factors associated with the development of atrial fibrillation. The overall performance, discrimination and calibration of the model were carried out. Results The derivation data set comprised 54 575 patients. The atrial fibrillation rate incidence was 15.3 per 1000 person/year. A 5‐year predictive model included age, male gender, overweight, heart failure, valvular heart disease, peripheral vascular disease, chronic kidney disease, number of antihypertensive drugs, systolic and diastolic blood pressure, heart rate, thromboembolism, stroke and previous history of myocardial infarction. The discrimination of the model was good (c‐index = 0.692; 95% confidence interval, 0.684‐0.700), and calibration was adequate. In the validation cohort, the discrimination was lower (c‐index = 0.670). Conclusions The model accurately predicts future atrial fibrillation in a population with both diabetes and hypertension. Early detection allows the prevention of possible complications arising from this disease.