International archives of allergy and immunology · 2025
Analysis of Risk Factors and Establishment of a Nomogram Prediction Model for Vitiligo Complicated with Autoimmune Thyroid Disease.
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Jiang Shibin, Wang Junlong, Zhao Xiaoling, Wang Chengcheng, Li Sha
Аннотация
INTRODUCTION: The aim of the study was to analyze the risk factors for vitiligo complicated with autoimmune thyroid disease (AITD) and establish a nomogram prediction model.
METHODS: Clinical data of 304 vitiligo patients admitted to our hospital from February 2023 to January 2025 were collected. Logistic regression was used to identify independent risk factors for vitiligo complicated with AITD. A nomogram prediction model was constructed using the rms package in R (R3.5.3). Internal validation was performed via 1,000 repeated samplings using the Bootstrap method in the caret package. The consistency index (C-index) was calculated using the rms package, and the receiver operating characteristic curve was plotted using the ROCR and rms packages.
RESULTS: The incidence of AITD in vitiligo patients was 17.76%. Hyperglycemia, a long disease duration of vitiligo (≥5 years), non-segmental vitiligo type, negative emotion, smoking, family history of AITD, and family history of other autoimmune diseases were identified as independent risk factors (all p < 0.05). The nomogram model based on these risk factors showed good consistency between observed and predicted incidences (Hosmer-Lemeshow test: χ2 = 3.920, p = 0.709), with a high C-index of 0.858 (95% confidence interval: 0.830-0.886).
CONCLUSION: Vitiligo patients, especially those with risk factors, should be screened for AITD whenever possible. The developed nomogram provides a practical tool for clinicians to identify high-risk individuals, facilitating tailored screening and early intervention strategies.
METHODS: Clinical data of 304 vitiligo patients admitted to our hospital from February 2023 to January 2025 were collected. Logistic regression was used to identify independent risk factors for vitiligo complicated with AITD. A nomogram prediction model was constructed using the rms package in R (R3.5.3). Internal validation was performed via 1,000 repeated samplings using the Bootstrap method in the caret package. The consistency index (C-index) was calculated using the rms package, and the receiver operating characteristic curve was plotted using the ROCR and rms packages.
RESULTS: The incidence of AITD in vitiligo patients was 17.76%. Hyperglycemia, a long disease duration of vitiligo (≥5 years), non-segmental vitiligo type, negative emotion, smoking, family history of AITD, and family history of other autoimmune diseases were identified as independent risk factors (all p < 0.05). The nomogram model based on these risk factors showed good consistency between observed and predicted incidences (Hosmer-Lemeshow test: χ2 = 3.920, p = 0.709), with a high C-index of 0.858 (95% confidence interval: 0.830-0.886).
CONCLUSION: Vitiligo patients, especially those with risk factors, should be screened for AITD whenever possible. The developed nomogram provides a practical tool for clinicians to identify high-risk individuals, facilitating tailored screening and early intervention strategies.