Proposal of a novel clinical score to predict heart failure incidence in long-term survivors of acute coronary syndromes. (15th December 2017)

• Record Type: Journal Article
• Authors: Rodríguez-Mañero, Moisés; Cordero, Alberto; Kreidieh, Omar; García-Acuña, Jose Mª.; Seijas, Jose; Agra-bermejo, Rosa María; Abou-Jokh, Charigan; Álvarez-Rodríguez, Leyre; Álvarez-Iglesias, Diego; López-Palop, Ramón; Cid, Belen; Carrillo, Pilar; González-Juanatey, Jose R.
• Is Part Of: International journal of cardiology. Volume 249(2017)
• Page Start: 301
• Page End: 307
• Publication Date: 2017-12-15
• Subjects: Coronary artery disease -- Heart failure -- Myocardial infarction; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2017.07.084
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: Introduction: HF remains a frequent complication following MI and adversely affects prognosis. The objective of this study was to identify predictors of HF following MI and to design a risk score for its prediction. Methods: Retrospective study of all consecutive patients admitted for MI. Primary end point was time to incident HF. Patients with previous history of HF were excluded. Death was modelled as competing risk. Results: 5737 patients were included. Mean age was 66.32 ± 12.80. During a median follow-up of 47.0 months (23.0–73.0), 686 patients (12%) developed HF. Age, diabetes mellitus, peripheral artery disease, renal insufficiency, chronic obstructive pulmonary disease, persistent atrial fibrillation, haemoglobin, troponin peak, diuretic at admission, ventricular function, and revascularization were independent predictors for HF development. According to this multivariate regression analysis, we developed a novel score that allows for the identification of patients at high (≥ 16), medium (9–15) and low risk (< 9) for HF development, with an AUC of 0.77 (IC 95%, 0.76–0.78; p = 0.008). Conclusions: Clinical comorbidities were determinant for the development of HF following MI. A simple score effectively categorize patients into low, intermediate, and high-risk. This could be important in order to intensify medical treatment or consider additional interventions.