Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies
Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies
复制标题
DOI：
10.1093/eurheartj/ehs337
复制
发表时间：
2013-05-01
影响因子：
39.3
通讯作者：
Doughty, Rob N.
中科院分区：
文献类型：
--
作者：
Pocock, Stuart J.;Ariti, Cono A.;Doughty, Rob N.
使用来自多个队列研究的大型国际数据库，目的是创建一个可推广的易于使用的心力衰竭（HF）患者死亡率风险评分。MAGGIC荟萃分析包括来自30项队列研究（其中6项为临床试验）的39372例HF患者的个体数据，包括左心室射血分数（EF）降低和保留。40.2的患者在中位随访2.5年期间死亡。使用逐步变量选择的多变量分段泊松回归方法，最终模型包括13个高度显著的死亡率独立预测因子，预测强度顺序如下：年龄、较低EF、NYHA分级、血清肌酐、糖尿病、未处方β受体阻滞剂、较低收缩压、较低体重、自诊断以来的时间、当前吸烟者、慢性阻塞性肺病、男性，而不是规定ACE抑制剂或血管紧张素受体阻滞剂。与EF降低组相比，EF保留组的年龄预测死亡率更高，收缩压预测死亡率更低。转换成一个易于使用的整数风险评分确定了一个非常显着的风险梯度，与3年死亡率为10和70的底部五分位数和顶部的风险，分别在HF患者的EF降低和保留，可以量化的影响，现成的死亡率预测的整数评分访问一个易于使用的网站。该评分具有在临床环境中广泛实施的潜力。
Using a large international database from multiple cohort studies, the aim is to create a generalizable easily used risk score for mortality in patients with heart failure (HF).The MAGGIC meta-analysis includes individual data on 39 372 patients with HF, both reduced and preserved left-ventricular ejection fraction (EF), from 30 cohort studies, six of which were clinical trials. 40.2 of patients died during a median follow-up of 2.5 years. Using multivariable piecewise Poisson regression methods with stepwise variable selection, a final model included 13 highly significant independent predictors of mortality in the following order of predictive strength: age, lower EF, NYHA class, serum creatinine, diabetes, not prescribed beta-blocker, lower systolic BP, lower body mass, time since diagnosis, current smoker, chronic obstructive pulmonary disease, male gender, and not prescribed ACE-inhibitor or angiotensin-receptor blockers. In preserved EF, age was more predictive and systolic BP was less predictive of mortality than in reduced EF. Conversion into an easy-to-use integer risk score identified a very marked gradient in risk, with 3-year mortality rates of 10 and 70 in the bottom quintile and top decile of risk, respectively.In patients with HF of both reduced and preserved EF, the influences of readily available predictors of mortality can be quantified in an integer score accessible by an easy-to-use website . The score has the potential for widespread implementation in a clinical setting.