ArticleInternational journal of general medicine2026
Development and External Validation of a Nomogram for Individualized Risk Prediction of Hyperprolactinemia in Chronic Kidney Disease: A Retrospective Multicenter Study.
Article in International journal of general medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Hyperprolactinemia (HPRL) is a prevalent endocrine disorder in patients with chronic kidney disease (CKD), yet individualized risk prediction tools remain limited. This study aimed to develop and externally validate a nomogram for predicting HPRL risk in patients with CKD. Methods: In this retrospective multicenter study, 346 patients with CKD were enrolled from three tertiary centers. Patients from Centers 1 and 2 constituted the development cohort (n = 250, 108 HPRL events), whereas patients from Center 3 served as an independent external validation cohort (n = 96, 36 HPRL events). Demographic, clinical, and laboratory characteristics were compared between patients with and without HPRL. Candidate predictors were screened using univariable and LASSO logistic regression, then entered into multivariable logistic regression. Significant variables were incorporated into the final nomogram. Model performance was assessed via AUC, calibration plots, and decision curve analysis (DCA). Results: Univariable analysis identified age, body mass index (BMI <18.5 or ≥28kg/m Conclusion: This externally validated nomogram, based on readily available clinical variables, demonstrated good performance for individualized HPRL risk estimation in patients with CKD and may support risk-based prioritization of prolactin assessment and further clinical evaluation.
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