New predictive models of heart failure mortality using time-series measurements and ensemble models.
New predictive models of heart failure mortality using time-series measurements and ensemble models.
复制标题
使用时间序列测量和集成模型的心力衰竭死亡率的新预测模型。
DOI：
10.1161/circheartfailure.110.958496
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Mann,DouglasL
中科院分区：
文献类型：
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作者：
Subramanian,Devika;Subramanian,Venkataraman;Deswal,Anita;Mann,DouglasL
背景与心力衰竭相关的发病率和死亡率仍然很高。各种各样的人口统计学和临床因素以及生物标志物与死亡率增加相关。尽管如此，大多数心力衰竭死亡率的多变量预测模型具有C统计量特征的预测准确性方法和结果我们分析了963例参加维司力农生存评价试验（VEST）的患者的数据，包括在基线和第8、16和24周采样的2种细胞因子（肿瘤坏死因子和白细胞介素-6）及其受体的循环水平。我们使用标准临床变量和细胞因子及细胞因子受体水平的时间序列，使用独立成分分析处理细胞因子测量值之间的共线性，并使用L2惩罚逐步回归进行变量选择，建立多变量logistic回归模型。我们还使用这些数据建立了集成模型，使用温和的提升。我们使用时间序列细胞因子测量的多变量逻辑回归模型预测1年死亡率显著优于基线模型（P=0.001），C统计量为0.81±0.03。在没有细胞因子的情况下，基线模型的C-统计量为0.73±0.03，并且在仅添加基线细胞因子和细胞因子受体水平的情况下，模型的C-统计量为0.74±0.04。具有连续细胞因子测量值的100个决策树桩的集成模型具有0.84±0.02的显著更好（P=0.04）的C-统计量。一个包含基线细胞因子数据而不包含连续测量的总体模型的C统计量为0.74± 0.04。结论通过使用Logistic回归模型，结合细胞因子和细胞因子受体水平等生物标志物的连续测量，可以显著提高慢性心力衰竭患者1年死亡率预测的准确性。包络模型捕捉大量患者群体的固有变异性，并通过使用时间序列测量来提高预测准确性。
BackgroundMorbidity and mortality rates associated with heart failure remain high. A wide variety of demographic and clinical factors as well as biomarkers are associated with increased mortality rates. Despite this, most multivariate predictive models for heart failure mortality have predictive accuracies characterized by a C-statistic (area under the receiver operating curve) of ≈0.74.Methods and ResultsWe analyzed data on 963 patients enrolled in the Vesnarinone Evaluation of Survival Trial (VEST), including circulating levels of 2 cytokines (tumor necrosis factor and interleukin-6) and their receptors sampled at baseline and at 8, 16, and 24 weeks. We built multivariate logistic regression models by using standard clinical variables and time-series of cytokine and cytokine receptor levels, using independent components analysis to handle collinearity among cytokine measurements, and L2-penalized stepwise regression for variable selection. We also built ensemble models with these data, using gentle boosting. Our multivariate logistic regression model using time-series cytokine measurements predicts 1-year mortality rates significantly better (P=0.001) than the baseline model, with a C-statistic of 0.81±0.03. Without the cytokines, the baseline model has a C-statistic of 0.73±0.03, and, with only baseline cytokine and cytokine receptor levels added, the model has a C-statistic of 0.74±0.04. An ensemble model of 100 decision stumps with serial cytokine measurements has a significantly better (P=0.04) C-statistic of 0.84±0.02. An ensemble model with baseline cytokine data and without the serial measurements has a C-statistic of 0.74±0.04.ConclusionsSignificant gains in accuracy of one year mortality prediction in chronic heart failure can be obtained by using logistic regression models that incorporate serial measurements of biomarkers such as cytokine and cytokine receptor levels. Ensemble models capture inherent variability in large patient populations, and boost predictive accuracy through the use of time-series measurements.