Background Prognostic prediction for heart failure with mildly reduced ejection fraction (HFmrEF) patients remains challenging. We aimed to create and validate a mortality risk prediction model for HFmrEF patients at 6-month, 1-year, and 3-year post discharge. Methods Clinical data of 1691 HFmrEF patients registered in the heart failure registry from 2015 to 2020 at the Heart Center of our hospital were analyzed. Patients were assigned into a training (1183 patients) and validation cohort (508 patients) at 7:3 ratio. Predictive variables for mortality at various period post discharge were identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The predicting model was then established based on these variables. Model performance was evaluated with ROC curve and Decision Curve Analysis (DCA). Results Eight predictors were identified including age, NT-proBNP, hemoglobin levels, use of beta-blockers, ACEI/ARB, invasive ventilation, PCI procedures, and pulmonary artery systolic pressure values. The model achieved a C-index of 0.748 (training set) and 0.755 (validation set). Area Under the Curve (AUC) for training set at 6 months, 1 year, and 3 years were 0.813, 0.784, and 0.774, and AUC for validation set were 0.775, 0.744, and 0.770, respectively. The DCA analysis confirmed the favorable net benefit of the established nomogram model. Conclusion This predicting model might be used for post-discharge risk stratification and individual decision-making of post-discharge management in HFmrEF patients.

中文翻译：

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开发和验证出院后射血分数轻度降低的心力衰竭患者死亡风险预测模型


背景 射血分数轻度降低 （HFmrEF） 患者的心力衰竭患者的预后预测仍然具有挑战性。我们旨在创建和验证 HFmrEF 患者出院后 6 个月、 1 年和 3 年的死亡风险预测模型。方法 分析我院心脏中心2015—2020年心力衰竭登记处登记的 1691 例 HFmrEF 患者的临床资料。患者以 7：3 的比例被分配到训练队列 （1183 名患者） 和验证队列 （508 名患者）。通过最小绝对收缩和选择运算符 （LASSO） 回归分析确定出院后不同时期死亡率的预测变量。然后根据这些变量建立预测模型。使用 ROC 曲线和决策曲线分析 （DCA） 评估模型性能。结果 确定了 8 个预测因素，包括年龄、 NT-proBNP 、血红蛋白水平、β 受体阻滞剂的使用、ACEI/ARB、有创通气、PCI 操作和肺动脉收缩压值。该模型的 C 指数为 0.748（训练集）和 0.755（验证集）。训练集在 6 个月、 1 年和 3 年的曲线下面积 （AUC） 分别为 0.813 、 0.784 和 0.774，验证集的 AUC 分别为 0.775 、 0.744 和 0.770。DCA 分析证实了已建立的列线图模型的良好净收益。结论 该预测模型可用于 HFmrEF 患者出院后风险分层和出院后管理的个体决策。