Evidence map›Paper›PMID 39156077›Full record

ArticleRisk management and healthcare policy2024

Web-Based Dynamic Nomogram for Predicting Risk of Mortality in Heart Failure with Mildly Reduced Ejection Fraction.

Wei Guo, Jing Tian, Yajing Wang, Yajing Zhang, Jingjing Yan, Yutao Du, Yanbo Zhang, Qinghua Han

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Article in Risk management and healthcare policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Wei GuoDepartment of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Jing TianDepartment of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.ORCID 0000-0002-0306-1947
Yajing WangDepartment of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Yajing ZhangDepartment of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Jingjing YanDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Yutao DuDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Yanbo ZhangDepartment of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
Qinghua HanDepartment of Cardiology, the 1st Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 ( 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.

Indexed as

all-cause mortalitydynamic nomogramheart failure with mildly reduced ejection fractionrisk prediction modelrisk strategy

Identifiers

PMID39156077
PMCPMC11330247

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