Diagnoses and medications associated with delayed ejaculation.

Peyronie’s disease data mining ejaculation erectile dysfunction

Journal

Sexual medicine
ISSN: 2050-1161
Titre abrégé: Sex Med
Pays: England
ID NLM: 101631053

Informations de publication

Date de publication:
Aug 2023
Historique:
received: 12 01 2023
revised: 27 06 2023
accepted: 27 06 2023
medline: 7 8 2023
pubmed: 7 8 2023
entrez: 7 8 2023
Statut: epublish

Résumé

Delayed ejaculation (DE) is a disorder that can cause significant distress for sexually active men. The etiology of DE is largely idiopathic, with even less being known about clinical factors associated with the condition. We sought to use data mining techniques to examine a broad group of health conditions and pharmaceutical treatments to identify factors associated with DE. Using an insurance claims database, we evaluated all men with a diagnosis of DE and matched them to a cohort (1:1) of men with other male sexual disorders of urologic origin (ie, erectile dysfunction [ED] and Peyronie's disease [PD]). Given the low prevalence of DE, we incorporated the random forest approach for classification of DE vs controls, with a plethora of predictors and cross-validation with the least absolute shrinkage and selection operator (LASSO). We used both a high-performance generalized linear model and a multivariate logistic model. The area under the curve was reported to demonstrate classifier performance, and odds ratios were used to indicate risks of each predictor. We also evaluated for differences in the prevalence of conditions in DE by race/ethnicity. Clinical factors (ie, diagnoses and medications) associated with DE were identified. In total, 11 602 men with DE were matched to a cohort of men with PD and ED. We focused on the 20 factors with the strongest association with DE across all models. The factors demonstrating positive associations with DE compared to other disorders of male sexual dysfunction (ie, ED and PD) included male infertility, testicular dysfunction, anxiety, disorders of lipid metabolism, alpha adrenergic blocker use, anemia, antidepressant use, and psychoses such as schizophrenia or schizoaffective disorder. In addition, the prevalence of several conditions varied by race/ethnicity. For example, male infertility was present in 5% of Asian men compared to <2% of men of other races. Several medical conditions and pharmacologic treatments are associated with DE, findings that may provide insight into the etiology of DE and offer treatment options. This study is to our knowledge the first to use using data mining techniques to investigate the association between medical conditions/pharmacologic agents and the development of subsequent DE. The generalizability of our findings is limited given that all men were commercially insured. DE is associated with multiple medical conditions, a finding that may help identify the etiology for this disorder.

Sections du résumé

Background UNASSIGNED
Delayed ejaculation (DE) is a disorder that can cause significant distress for sexually active men. The etiology of DE is largely idiopathic, with even less being known about clinical factors associated with the condition.
Aim UNASSIGNED
We sought to use data mining techniques to examine a broad group of health conditions and pharmaceutical treatments to identify factors associated with DE.
Methods UNASSIGNED
Using an insurance claims database, we evaluated all men with a diagnosis of DE and matched them to a cohort (1:1) of men with other male sexual disorders of urologic origin (ie, erectile dysfunction [ED] and Peyronie's disease [PD]). Given the low prevalence of DE, we incorporated the random forest approach for classification of DE vs controls, with a plethora of predictors and cross-validation with the least absolute shrinkage and selection operator (LASSO). We used both a high-performance generalized linear model and a multivariate logistic model. The area under the curve was reported to demonstrate classifier performance, and odds ratios were used to indicate risks of each predictor. We also evaluated for differences in the prevalence of conditions in DE by race/ethnicity.
Outcomes UNASSIGNED
Clinical factors (ie, diagnoses and medications) associated with DE were identified.
Results UNASSIGNED
In total, 11 602 men with DE were matched to a cohort of men with PD and ED. We focused on the 20 factors with the strongest association with DE across all models. The factors demonstrating positive associations with DE compared to other disorders of male sexual dysfunction (ie, ED and PD) included male infertility, testicular dysfunction, anxiety, disorders of lipid metabolism, alpha adrenergic blocker use, anemia, antidepressant use, and psychoses such as schizophrenia or schizoaffective disorder. In addition, the prevalence of several conditions varied by race/ethnicity. For example, male infertility was present in 5% of Asian men compared to <2% of men of other races.
Clinical Implications UNASSIGNED
Several medical conditions and pharmacologic treatments are associated with DE, findings that may provide insight into the etiology of DE and offer treatment options.
Strengths and Limitations UNASSIGNED
This study is to our knowledge the first to use using data mining techniques to investigate the association between medical conditions/pharmacologic agents and the development of subsequent DE. The generalizability of our findings is limited given that all men were commercially insured.
Conclusion UNASSIGNED
DE is associated with multiple medical conditions, a finding that may help identify the etiology for this disorder.

Identifiants

pubmed: 37547871
doi: 10.1093/sexmed/qfad040
pii: qfad040
pmc: PMC10397419
doi:

Types de publication

Journal Article

Langues

eng

Pagination

qfad040

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of The International Society of Sexual Medicine.

Déclaration de conflit d'intérêts

Dr. Eisenberg, is an advisor to the companies Dadi, Roman, Sandstone, Hannah and Underdog and also serves as a consultant to Gilead.

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Auteurs

Evan Mulloy (E)

Department of Urology, Stanford University School of Medicine, Palo Alto, CA, United States.

Amy Zhang (A)

Department of Urology, Stanford University School of Medicine, Palo Alto, CA, United States.

Federico Balladelli (F)

Department of Urology, Stanford University School of Medicine, Palo Alto, CA, United States.
Division of Experimental Oncology/Unit of Urology, IRCCS Ospedale San Raffaele, Milan, Italy.

Francesco Del Giudice (F)

Department of Urology, Stanford University School of Medicine, Palo Alto, CA, United States.
Department of Maternal Infant and Urologic Sciences, "Sapienza" University of Rome, Rome, Italy.

Frank Glover (F)

Emory University School of Medicine, Atlanta, GA United States.

Michael L Eisenberg (ML)

Department of Urology, Stanford University School of Medicine, Palo Alto, CA, United States.

Classifications MeSH