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Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.
JACC: Heart Failure ( IF 13.0 ) Pub Date : 2019-10-09 , DOI: 10.1016/j.jchf.2019.06.013
Suveen Angraal 1 , Bobak J Mortazavi 2 , Aakriti Gupta 3 , Rohan Khera 4 , Tariq Ahmad 5 , Nihar R Desai 6 , Daniel L Jacoby 5 , Frederick A Masoudi 7 , John A Spertus 8 , Harlan M Krumholz 9
Affiliation  

OBJECTIVES This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. BACKGROUND Although risk assessment models are available for patients with HF with reduced ejection fraction, few have assessed the risks of death and hospitalization in patients with HFpEF. METHODS The following 5 methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. Model discrimination and calibration were estimated using receiver-operating characteristic curves and Brier scores, respectively. The top prediction variables were assessed by using the best performing models, using the incremental improvement of each variable in 5-fold cross-validation. RESULTS The RF was the best performing model with a mean C-statistic of 0.72 (95% confidence interval [CI]: 0.69 to 0.75) for predicting mortality (Brier score: 0.17), and 0.76 (95% CI: 0.71 to 0.81) for HF hospitalization (Brier score: 0.19). Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, whereas hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization. CONCLUSIONS These models predict the risks of mortality and HF hospitalization in patients with HFpEF and emphasize the importance of health status data in determining prognosis. (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist [TOPCAT]; NCT00094302).

中文翻译:

保留射血分数的机器学习对心力衰竭死亡率和住院率的预测。

目的本研究旨在为TOPCAT(醛固酮拮抗剂治疗保留的心脏功能性心力衰竭)试验中的射血分数(HFpEF)保持不变的心衰患者建立死亡率和心力衰竭(HF)住院预测模型。背景技术尽管风险评估模型可用于射血分数降低的HF患者,但很少有人评估HFpEF患者的死亡和住院风险。方法有以下5种方法:对变量进行前向选择的Logistic回归;使用套索正则化进行逻辑回归以进行变量选择;随机森林(RF);梯度下降提升;和支持向量机 在3年的随访中,他们被用来训练评估死亡率和心衰住院风险的模型,并使用5倍交叉验证进行了验证。分别使用接收器操作特性曲线和Brier分数估算模型判别力和校准值。通过使用性能最佳的模型对最高预测变量进行评估,并使用每个变量在5倍交叉验证中的增量改进。结果RF是表现最好的模型,其平均C统计量为0.72(95%置信区间[CI]:0.69至0.75),可预测死亡率(Brier评分:0.17)和0.76(95%CI:0.71至0.81)。 HF住院治疗(Brier评分:0.19)。血尿素氮水平,体重指数和堪萨斯城心肌病问卷(KCCQ)子量表得分与死亡率密切相关,而血红蛋白水平,血尿素氮,以前的高频住院时间,KCCQ评分是高频住院的最重要预测指标。结论这些模型预测了HFpEF患者的死亡风险和HF住院风险,并强调了健康状况数据在确定预后中的重要性。(用醛固酮拮抗剂治疗保留的心功能性心力衰竭[TOPCAT]; NCT00094302)。
更新日期:2019-10-10
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