Zhao Hengli, Li Peixin, Zhong Guoheng, Xie Kaiji, Zhou Haobin, Ning Yunshan, Xu Dingli, Zeng Qingchun
State Key Laboratory of Organ Failure Research, Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Shock and Microcirculation, Southern Medical University, Guangzhou, China.
Front Cardiovasc Med. 2022 Nov 30;9:1042139. doi: 10.3389/fcvm.2022.1042139. eCollection 2022.
Heart failure with mildly reduced ejection fraction (HFmrEF) has been recently recognized as a unique phenotype of heart failure (HF) in current practical guideline. However, risk stratification models for mortality and HF re-hospitalization are still lacking. This study aimed to develop and validate a novel machine learning (ML)-derived model to predict the risk of mortality and re-hospitalization for HFmrEF patients.
We assessed the risks of mortality and HF re-hospitalization in HFmrEF (45-49%) patients enrolled in the TOPCAT trial. Eight ML-based models were constructed, including 72 candidate variables. The Harrell concordance index (C-index) and DeLong test were used to assess discrimination and the improvement in discrimination between models, respectively. Calibration of the HF risk prediction model was plotted to obtain bias-corrected estimates of predicted versus observed values.
Least absolute shrinkage and selection operator (LASSO) Cox regression was the best-performing model for 1- and 6-year mortality, with a highest C-indices at 0.83 (95% CI: 0.68-0.94) over a maximum of 6 years of follow-up and 0.77 (95% CI: 0.64-0.89) for the 1-year follow-up. The random forest (RF) showed the best discrimination for HF re-hospitalization, scoring 0.80 (95% CI: 0.66-0.94) and 0.85 (95% CI: 0.71-0.99) at the 6- and 1-year follow-ups, respectively. For risk assessment analysis, Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were the most important predictor of readmission outcome in the HFmrEF patients.
ML-based models outperformed traditional models at predicting mortality and re-hospitalization in patients with HFmrEF. The results of the risk assessment showed that KCCQ score should be paid increasing attention to in the management of HFmrEF patients.
射血分数轻度降低的心力衰竭（HFmrEF）最近在当前实用指南中被确认为心力衰竭（HF）的一种独特表型。然而，目前仍缺乏用于死亡率和HF再住院风险分层的模型。本研究旨在开发并验证一种基于机器学习（ML）的新型模型，以预测HFmrEF患者的死亡风险和再住院风险。
我们评估了参加TOPCAT试验的HFmrEF（45 - 49%）患者的死亡风险和HF再住院风险。构建了8个基于ML的模型，包括72个候选变量。分别使用Harrell一致性指数（C指数）和DeLong检验来评估模型的区分度以及模型之间区分度的改善情况。绘制HF风险预测模型的校准图，以获得预测值与观察值的偏差校正估计。
对于1年和6年死亡率，最小绝对收缩和选择算子（LASSO）Cox回归是表现最佳的模型，在最长6年的随访中，C指数最高为0.83（95%CI：0.68 - 0.94），1年随访时为0.77（95%CI：0.64 - 0.89）。随机森林（RF）对HF再住院的区分度最佳，在6年和1年随访时的得分分别为0.80（95%CI：0.66 - 0.94）和0.85（95%CI：0.71 - 0.99）。对于风险评估分析，堪萨斯城心肌病问卷（KCCQ）子量表得分是HFmrEF患者再入院结局的最重要预测因素。
在预测HFmrEF患者的死亡率和再住院率方面，基于ML的模型优于传统模型。风险评估结果表明，在HFmrEF患者的管理中应越来越重视KCCQ评分。
