Wei Guo,1 Jing Tian,1 Yajing Wang,1 Yajing Zhang,1 Jingjing Yan,2 Yutao Du,2 Yanbo Zhang,2,3 Qinghua Han1,4 

1Department of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 2Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 3Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, Shanxi Province, People’s Republic of China; 4Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, Shanxi Province, People’s Republic of China

Correspondence: Qinghua Han, Department of Cardiology, the 1st Hospital of Shanxi Medical University, No. 85 South JieFang Road, Yingze District, Taiyuan, Shanxi, People’s Republic of China, Email syhqh@sohu.com Yanbo Zhang, Department of Health Statistics, School of Public Health, Shanxi Medical University, No. 56 South XinJian Road, Yingze District, Taiyuan, Shanxi, People’s Republic of China, Email sxmuzyb@126.com

Purpose: 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.
Patients and Methods: 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.
Results: The optimal independent predictors for all-cause mortality (APSELNH: A: angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitor (ACEI/ARB/ARNI), P: percutaneous coronary intervention/coronary artery bypass graft (PCI/CABG), S: stroke, E: eGFR, L: lg of NT-proBNP, N: NYHA, H: healthcare) 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.
Conclusion: 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.

Keywords: heart failure with mildly reduced ejection fraction, all-cause mortality, risk prediction model, risk strategy, dynamic nomogram