Evidence map›Paper›PMID 40583026›Full record

ArticleThe world journal of men's health2026

Development and Validation of a Dynamic Online Nomogram Prediction Model for Assessing the Risk of Erectile Dysfunction.

Guodong Liu, Yuyang Zhang, Xu Wu, Hui Gao, Hui Jiang, Xiansheng Zhang

Abstract read
In one paragraph

Article in The world journal of men's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guodong Liu *Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0003-3190-9148
Yuyang Zhang *Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-0462-2574
Xu WuDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-2141-3918
Hui GaoDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-0153-865X
Hui JiangDepartment of Urology, Peking University First Hospital Institute of Urology, Peking University Andrology Center, Beijing, China. jianghui@bjmu.edu.cn.ORCID https://orcid.org/0000-0002-6256-2733
Xiansheng ZhangDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0001-6003-5182

Funding

National Natural Science Foundation of China 82371635
6 · The paper itself

Abstract

purposeErection dysfunction (ED) represents a globally prevalent men's health problem and affected by a variety of factors. This study aimed to develop a dynamic nomogram model to assess the probability of ED in a population through a multitude of factors. MATERIALS AND

methodsA total of 2,668 subjects from the National Health and Nutrition Examination Survey were included in this study. The entire dataset was randomly divided into training and validation sets, with the training set comprising 70% of the data and the validation set comprising 30%. The Least Absolute Shrinkage and Selection Operator and multivariate logistic regression analysis determined the predictors for constructing the nomogram, and the model was evaluated by Concordance Index (C-index), calibration curve, Hosmer-Lemeshow test, and decision curve analysis.

resultsThe nomogram model consisted of 9 predictors, which were age, education, stroke, lymphocyte, diabetes, poverty income ratio, prostate disease, activity, and hypertension. The C-index for the training set was 0.828 and for the validation set was 0.825, indicating that the model shows good clinical applicability and calibration of the model on both the training and validation sets. Additionally, we created an online dynamic nomogram (https://wvknly-liu-guodong.shinyapps.io/dynnomapp/) that anyone can evaluate on a web page.

conclusionsOur dynamic nomogram can integrate multiple risk factors to provide a personalized risk assessment that is highly clinically predictive and provides a valuable tool for early intervention and prospective management of ED. This could help physicians to identify and manage high-risk populations early and provide personalized treatment plans.

Indexed as

Erectile dysfunctionNomogramsNutrition surveysPredictive learning models

Identifiers

PMID40583026
PMCPMC13036249

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.