Construction and validation of a nomogram for predicting fatigue in climacteric women.

Objective: The aim was to develop and validate a nomogram for evaluating the risk of fatigue in climacteric women and to assess its clinical application value.

Methods: Clinical information was collected from 402 climacteric women who visited a tertiary hospital in Shanghai between November 2023 and April 2024. Network analysis methods were utilized to analyze the core symptom (fatigue). The study participants were then randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for fatigue in climacteric women. A nomogram prediction model was established based on these independent risk factors. The predictive performance of the model was evaluated using the concordance index, area under the curve, receiver operating characteristic curve, Hosmer-Lemeshow test, and calibration curve analysis. Additionally, decision curve analysis was performed to assess the model's performance in clinical applications.

Results: Fatigue is identified as the core symptom in climacteric women. Educational level, chronic diseases, and depression status are independent influencing factors for fatigue in menopausal women. The area under the curve for the training cohort and validation cohort are 0.813 (95% CI, 0.743-0.884) and 0.759 (95% CI, 0.637-0.879), respectively, indicating that the model possesses good discriminative ability. The calibration curve shows good consistency between the predicted probabilities and actual probabilities in both the training and validation cohorts. Additionally, the P values for the Hosmer-Lemeshow test in the training and validation sets are 0.233 and 0.197, respectively, indicating good model calibration. Finally, the decision curve analysis curve demonstrates that the model has good clinical utility.

Conclusions: A simple nomogram based on three independent factors (educational level, chronic diseases, and depression status) can aid in clinically predicting the risk of fatigue in climacteric women.