Lopes Renato D, Pieper Karen S, Stevens Susanna R, Solomon Scott D, McMurray John J V, Pfeffer Marc A, Leimberger Jeffrey D, Velazquez Eric J
Duke Clinical Research Institute, Duke University Medical Center, Durham, NC
Duke Clinical Research Institute, Duke University Medical Center, Durham, NC.
J Am Heart Assoc. 2016 Jun 27;5(6):e003045. doi: 10.1161/JAHA.115.003045.
Most studies of risk assessment or stratification in patients with myocardial infarction (MI) have been static and fail to account for the evolving nature of clinical events and care processes. We sought to identify predictors of mortality, cardiovascular death or nonfatal MI, and cardiovascular death or nonfatal heart failure (HF) over time in patients with HF, left ventricular systolic dysfunction, or both post-MI.
Using data from the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial, we developed models to estimate the association between patient characteristics and the likelihood of experiencing an event from the time of a follow-up visit until the next visit. The intervals are: hospital arrival to discharge or 14 days, whichever occurs first; hospital discharge to 30 days; 30 days to 6 months; and 6 months to 3 years. Models were also developed to predict the entire 3-year follow-up period using baseline information. Multivariable Cox proportional hazards modeling was used throughout with Wald chi-squares as the comparator of strength for each predictor. For the baseline model of overall mortality, the 3 strongest predictors were age (adjusted hazard ratio [HR], 1.35; 95% CI, 1.28-1.42; P<0.0001), baseline heart rate (adjusted HR, 1.17; 95% CI, 1.14-1.21; P<0.0001), and creatinine clearance (≤100 mL/min; adjusted HR, 0.86; 95% CI, 0.84-0.89; P<0.0001). According to the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices, the updated model had significant improvement over the model with baseline covariates only in all follow-up periods and with all outcomes.
Patient information assessed closest to the time of the outcome was more valuable in predicting death when compared with information obtained at the time of the index hospitalization. Using updated patient information improves prognosis over using only the information available at the time of the index event.
大多数关于心肌梗死（MI）患者风险评估或分层的研究都是静态的，未能考虑临床事件和护理过程的动态变化。我们试图确定心肌梗死后出现心力衰竭（HF）、左心室收缩功能障碍或两者兼有的患者在一段时间内的死亡率、心血管死亡或非致命性心肌梗死，以及心血管死亡或非致命性心力衰竭的预测因素。
利用缬沙坦急性心肌梗死试验（VALIANT）的数据，我们建立了模型，以估计患者特征与从一次随访到下次随访期间发生事件的可能性之间的关联。这些时间段为：入院至出院或14天（以先发生者为准）；出院至30天；30天至6个月；以及6个月至3年。还建立了模型，使用基线信息预测整个3年的随访期。整个过程采用多变量Cox比例风险模型，以Wald卡方检验作为每个预测因素强度的比较指标。对于总体死亡率的基线模型，3个最强的预测因素是年龄（调整后风险比[HR]，1.35；95%置信区间[CI]，1.28 - 1.42；P < 0.0001）、基线心率（调整后HR，1.17；95% CI，1.14 - 1.21；P < 0.0001）和肌酐清除率（≤100 mL/min；调整后HR，0.86；95% CI，0.84 - 0.89；P < 0.0001）。根据综合判别改善（IDI）和净重新分类改善（NRI）指数，在所有随访期和所有结局中，更新后的模型与仅包含基线协变量的模型相比有显著改善。
与首次住院时获得的信息相比，最接近结局时间评估的患者信息在预测死亡方面更有价值。使用更新后的患者信息比仅使用首次事件时可用的信息能改善预后。
