Novel ThyPRO-39-based nomogram for identifying impaired quality of life in Graves' hyperthyroidism outpatients: development and internal validation

基于ThyPRO-39的新型列线图用于识别Graves甲亢门诊患者的生活质量受损:开发和内部验证

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Abstract

OBJECTIVE: To identify factors independently associated with quality-of-life (QoL) impairment in Graves' hyperthyroidism (GH) patients and develop a clinically applicable nomogram for outpatient settings. METHODS: A total of 402 GH patients were recruited from the outpatient clinic of Endocrinology Department of Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, from January 2024 to June 2025. Participants were surveyed using a general information questionnaire, the Thyroid-specific Patient-Reported Outcome Short-Form (ThyPRO-39), the Pittsburgh Sleep Quality Index (PSQI), the Hamilton Depression Scale (HAMD), and the Hamilton Anxiety Scale (HAMA). Univariate and multivariate analyses identified independent predictors of QoL impairment. A nomogram was developed and validated using the area under the receiver operating characteristic curve (AUC), bootstrap-calibrated plots, and decision curve analysis (DCA). An online dynamic calculator (dynnom) was also developed to facilitate clinical application. RESULTS: Multivariate analysis revealed that thyroid eye disease (TED), goiter, sleep disturbances, anxiety, and depressive symptoms were independent predictors of QoL impairment. The nomogram demonstrated an excellent discriminative ability: AUC was 0.886 in the training group and 0.844 in the validation group. Bootstrap calibration showed good consistency between predicted and observed probabilities. DCA revealed favorable net clinical benefit across a 0.2-0.6 threshold range. The associated online calculator further improved clinical usability. CONCLUSIONS: The nomogram integrating TED, goiter, sleep, and emotional factors provides a reliable tool for early identification of GH patients at high risk of QoL impairment in outpatient settings. Future external multicenter validation is needed to improve its generalizability.

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