医学
内科学
心房颤动
弗雷明翰风险评分
心脏病学
比例危险模型
危险系数
冲程(发动机)
置信区间
队列
CHA2DS2–血管评分
风险评估
风险因素
作者
Athanasios Samaras,Anastasios Kartas,Evangelos Akrivos,George Fotos,George Dividis,Dimitra Vasdeki,Eleni Vrana,Georgios Rampidis,Haralambos Karvounis,George Giannakoulas,Apostolos Tzikas
标识
DOI:10.1016/j.hjc.2021.01.007
摘要
This study sought to develop and validate a risk score to predict mortality in patients with atrial fibrillation (AF) after a hospitalization for cardiac reasons. The new risk score was derived from a prospective cohort of hospitalized patients with concurrent AF. The outcome measures were all-cause and cardiovascular mortality. Random forest was used for variable selection. A risk points model with predictor variables was developed by weighted Cox regression coefficients and was internally validated by bootstrapping. In total, 1130 patients with AF were included. During a median follow-up of 2 years, 346 (30.6%) patients died and 250 patients had a cardiovascular cause of death. N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin-T were the most important predictors of mortality, followed by indexed left atrial volume, history and type of heart failure, age, history of diabetes mellitus, and intraventricular conduction delay, all forming the BASIC-AF risk score (Biomarkers, Age, ultraSound, Intraventricular conduction delay, and Clinical history). The score had good discrimination for all-cause (c-index = 0.85 and 95% CI 0.82–0.88) and cardiovascular death (c-index = 0.84 and 95% CI 0.81-0.87). The predicted probability of mortality varied more than 50-fold across deciles and adjusted well to observed mortality rates. A decision curve analysis revealed a significant net benefit of using the BASIC-AF risk score to predict the risk of death, when compared with other existing risk schemes. We developed and internally validated a well-performing novel risk score for predicting death in patients with AF. The BASIC-AF risk score included routinely assessed parameters, selected through machine-learning algorithms, and may assist in tailored risk stratification and management of these patients.
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