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预测射血分数降低的心力衰竭门诊患者心力衰竭住院和全因死亡率的多州模型:模型推导与外部验证

Multistate Model to Predict Heart Failure Hospitalizations and All-Cause Mortality in Outpatients With Heart Failure With Reduced Ejection Fraction: Model Derivation and External Validation.

作者信息

Upshaw Jenica N, Konstam Marvin A, Klaveren David van, Noubary Farzad, Huggins Gordon S, Kent David M

机构信息

From The CardioVascular Center (J.N.U., M.A.K., G.S.H.) and The Institute for Clinical Research and Health Policy Studies (D.v.K., F.N., D.M.K.), Tufts Medical Center, Boston, MA; and The Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands (D.v.K.).

出版信息

Circ Heart Fail. 2016 Aug;9(8). doi: 10.1161/CIRCHEARTFAILURE.116.003146.

Abstract

BACKGROUND

Outpatients with heart failure (HF) who are at high risk for HF hospitalization and death may benefit from early identification. We sought to develop and externally validate a model to predict both HF hospitalization and mortality that accounts for the semicompeting nature of the 2 outcomes and captures the risk associated with the transition from the stable outpatient state to the post-HF hospitalization state.

METHODS AND RESULTS

A multistate model to predict HF hospitalization and all-cause mortality was derived using data (n=3834) from the HEAAL study (Heart Failure Endpoint evaluation of Angiotensin II Antagonist Losartan), a multinational randomized trial in symptomatic patients with reduced left ventricular ejection fraction. Twelve easily and reliably obtainable demographic and clinical predictors were prespecified for model inclusion. Model performance was assessed in the SCD-HeFT cohort (Sudden Cardiac Death in Heart Failure Trial; n=2521). At 1 year, the probability of being alive without HF hospitalization was 94% for a typical patient in the lowest risk quintile and 77% for a typical patient in the highest risk quintile and this variability in risk continued through 7 years of follow-up. The model c-index was 0.72 in the derivation cohort, 0.66 in the validation cohort, and 0.69 in the implantable cardiac defibrillator arm of the validation cohort. There was excellent calibration across quintiles of predicted risk.

CONCLUSIONS

Our findings illustrate the advantages of a multistate modeling approach, providing estimates of HF hospitalization and death in the same model, comparison of predictors for the different outcomes and demonstrating the different trajectories of patients based on baseline characteristics and intermediary events.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00000609 and NCT00090259.

摘要

背景

心力衰竭(HF)门诊患者若有较高的HF住院和死亡风险,早期识别可能会使其受益。我们试图开发并在外部验证一个模型,以预测HF住院和死亡率,该模型考虑了这两种结局的半竞争性质,并捕捉从稳定门诊状态到HF住院后状态转变所带来的风险。

方法和结果

使用来自HEAAL研究(血管紧张素II拮抗剂氯沙坦的心力衰竭终点评估)的数据(n = 3834),推导了一个预测HF住院和全因死亡率的多状态模型,HEAAL研究是一项针对左心室射血分数降低的有症状患者的多国随机试验。预先指定了12个易于且可靠获得的人口统计学和临床预测因素纳入模型。在SCD-HeFT队列(心力衰竭试验中的心脏性猝死;n = 2521)中评估模型性能。1年时,处于最低风险五分位数的典型患者无HF住院且存活的概率为94%,处于最高风险五分位数的典型患者为77%,这种风险差异在7年随访中持续存在。该模型在推导队列中的c指数为0.72,在验证队列中为0.66,在验证队列的植入式心脏除颤器亚组中为0.69。在预测风险的各个五分位数中校准良好。

结论

我们的研究结果说明了多状态建模方法的优势,在同一模型中提供HF住院和死亡的估计值,比较不同结局的预测因素,并根据基线特征和中间事件展示患者的不同轨迹。

临床试验注册

网址:http://www.clinicaltrials.gov。唯一标识符:NCT00000609和NCT00090259。

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