International journal of general medicine · 2026
Development and External Validation of a Nomogram for Individualized Risk Prediction of Hyperprolactinemia in Chronic Kidney Disease: A Retrospective Multicenter Study.
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Ji Ming, Liu Dongrui, Peng Xiaoren, Zhao Hongjing, Wang Zhaowei, Deng Weijuan, Zhang Hao, Zhang Hanbin, Cao Changchun
Аннотация
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/m2), estimated glomerular filtration rate (eGFR), lupus nephritis, renal arteriosclerosis, comorbid diabetes, renal replacement therapy, dialysis duration, urinary microalbumin (U-Alb), 24-hour urinary total protein (24-UTP), parathyroid hormone (PTH), fasting plasma glucose (FPG), and urinary albumin-to-creatinine ratio (U-ACR) as factors significantly associated with HPRL. Multivariable regression revealed that age, BMI <18.5 or ≥28, reduced eGFR, comorbid diabetes, and dialysis duration were independent predictors (all P < 0.05). The resulting nomogram demonstrated excellent discriminative ability, with an AUC of 0.914 (95% CI, 0.879-0.949; sensitivity, 88.9%; specificity, 79.6%) in the development cohort and 0.896 (95% CI, 0.834-0.958; sensitivity, 77.8%; specificity, 83.3%) in the validation cohort. Calibration curves showed good agreement between predicted and observed outcomes, and DCA suggested potential net benefit across a range of threshold probabilities.
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.
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/m2), estimated glomerular filtration rate (eGFR), lupus nephritis, renal arteriosclerosis, comorbid diabetes, renal replacement therapy, dialysis duration, urinary microalbumin (U-Alb), 24-hour urinary total protein (24-UTP), parathyroid hormone (PTH), fasting plasma glucose (FPG), and urinary albumin-to-creatinine ratio (U-ACR) as factors significantly associated with HPRL. Multivariable regression revealed that age, BMI <18.5 or ≥28, reduced eGFR, comorbid diabetes, and dialysis duration were independent predictors (all P < 0.05). The resulting nomogram demonstrated excellent discriminative ability, with an AUC of 0.914 (95% CI, 0.879-0.949; sensitivity, 88.9%; specificity, 79.6%) in the development cohort and 0.896 (95% CI, 0.834-0.958; sensitivity, 77.8%; specificity, 83.3%) in the validation cohort. Calibration curves showed good agreement between predicted and observed outcomes, and DCA suggested potential net benefit across a range of threshold probabilities.
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.