Evidence map›Paper›PMID 42742858›Full record

ArticleInternational urology and nephrology2026

Sexual dysfunction in peritoneal dialysis: associated factors and exploratory machine-learning classification.

Li Deng, Dan Zhao, Qiong Cheng, Dashuang Liu, Liang Liu, Jicong Luo

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Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Li DengChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Dan ZhaoChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Qiong ChengChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Dashuang LiuChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China.
Liang LiuChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China. liuliang728@tmmu.edu.cn.ORCID https://orcid.org/0009-0004-3207-7368
Jicong LuoChongqing Clinical Research Center of Kidney and Urology Diseases, Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Kidney Disease of Chongqing, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, China. luojicong@tmmu.edu.cn.

Funding

Clinical Research Project of Xinqiao Hospital 2024HLPY-02Health Appropriate Technology Promotion Project of Chongqing Municipality 2025cyjstg009
6 · The paper itself

Abstract

backgroundSexual dysfunction (SD) is a common yet under-recognized complication in patients receiving peritoneal dialysis (PD). We investigated the prevalence of SD and its associated factors, and developed interpretable machine learning models to classify prevalent SD in PD patients.

methodsWe enrolled 209 PD patients (100 males, 109 females) in this retrospective cross-sectional study. Sexual function was assessed with the International Index of Erectile Function-5 (IIEF-5) in males and the Female Sexual Function Index (FSFI) in females. We collected demographic, clinical, biochemical, and psychosocial variables, and performed univariate analysis, Spearman correlation, and multivariate linear and logistic regression. Eight machine learning models were compared. SHapley Additive exPlanations (SHAP) were applied to the Random Forest model, selected for interpretability and balanced performance.

resultsThe overall prevalence of SD was 73.7% (154/209), with rates of 78% (78/100) in males and 69.7% (76/109) in females. In males, multivariable linear regression identified depression score (SDS; β = -0.24, P = 0.013), 24-h urine volume (β = + 0.48 per 100 mL/day, P = 0.021), marital status (β = + 8.77, P < 0.001), and α-blocker use (β = -4.14, P = 0.013) as associated variables of male erectile-function scores. In females, the associations were weaker and reflected socioeconomic and clinical factors, including education, hypertension, and α-blocker use. Among machine-learning models, the random forest + XGBoost ensemble achieved a test-set AUC of 0.630. In the SHAP analysis of the overall SD model, BMI had the highest mean absolute SHAP value, followed by 24-h urine volume and SDS.

conclusionsSexual dysfunction was common among respondents receiving peritoneal dialysis. In men, higher depression scores and lower 24-h urine volume were associated with male erectile function scores, whereas in women associations were weaker and primarily socioeconomic. The models and SHAP-derived feature rankings should therefore be considered exploratory and require prospective external validation before clinical use.

Indexed as

24-h urine volumeDepressionMachine learningPeritoneal dialysisSexual dysfunctionSHAP

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