Guo Wei, Tian Jing, Wang Yajing, Zhang Yajing, Yan Jingjing, Du Yutao, Zhang Yanbo, Han Qinghua
Department of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Risk Manag Healthc Policy. 2024 Aug 13;17:1959-1972. doi: 10.2147/RMHP.S474862. eCollection 2024.
This study aimed to develop an integrative dynamic nomogram, including N-terminal pro-B type natural peptide (NT-proBNP) and estimated glomerular filtration rate (eGFR), for predicting the risk of all-cause mortality in HFmrEF patients.
790 HFmrEF patients were prospectively enrolled in the development cohort for the model. The least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest (RSF) were employed to select predictors for all-cause mortality. Develop a nomogram based on the Cox proportional hazard model for predicting long-term mortality (1-, 3-, and 5-year) in HFmrEF. Internal validation was conducted using Bootstrap, and the final model was validated in an external cohort of 338 consecutive adult patients. Discrimination and predictive performance were evaluated by calculating the time-dependent concordance index (C-index), area under the ROC curve (AUC), and calibration curve, with clinical value assessed via decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to assess the contributions of NT-proBNP and eGFR to the nomogram. Finally, develop a dynamic nomogram using the "Dynnom" package.
The optimal independent predictors for all-cause mortality (, ) were incorporated into the dynamic nomogram. The C-index in the development cohort and validation cohort were 0.858 and 0.826, respectively, with AUCs exceeding 0.8, indicating good discrimination and predictive ability. DCA curves and calibration curves demonstrated clinical applicability and good consistency of the nomogram. NT-proBNP and eGFR provided significant net benefits to the nomogram.
In this study, the dynamic APSELNH nomogram developed serves as an accessible, functional, and effective clinical decision support calculator, offering accurate prognostic assessment for patients with HFmrEF.
本研究旨在开发一种综合动态列线图，纳入N末端B型利钠肽原（NT-proBNP）和估算肾小球滤过率（eGFR），以预测射血分数保留的心力衰竭（HFmrEF）患者的全因死亡风险。
790例HFmrEF患者被前瞻性纳入模型开发队列。采用最小绝对收缩和选择算子（LASSO）回归及随机生存森林（RSF）来选择全因死亡的预测因素。基于Cox比例风险模型开发一个列线图，用于预测HFmrEF患者的长期死亡率（1年、3年和5年）。使用Bootstrap进行内部验证，并在338例连续成年患者的外部队列中对最终模型进行验证。通过计算时间依赖性一致性指数（C指数）、ROC曲线下面积（AUC）和校准曲线来评估辨别力和预测性能，并通过决策曲线分析（DCA）评估临床价值。采用综合辨别力改善（IDI）和净重新分类改善（NRI）来评估NT-proBNP和eGFR对列线图的贡献。最后，使用“Dynnom”软件包开发动态列线图。
将全因死亡的最佳独立预测因素纳入动态列线图。开发队列和验证队列中的C指数分别为0.858和0.826，AUC均超过0.8，表明具有良好的辨别力和预测能力。DCA曲线和校准曲线证明了列线图的临床适用性和良好一致性。NT-proBNP和eGFR为列线图提供了显著的净效益。
在本研究中，所开发的动态APSELNH列线图是一种便捷、实用且有效的临床决策支持计算器，可为HFmrEF患者提供准确的预后评估。
