Development of a Nomogram Model for Predicting Anxiety Risk in Patients With Hyperhidrosis: A Study Involving 234 Patients

Primary focal hyperhidrosis (HH) carries a substantial psychosocial burden, and anxiety is a common but underrecognized comorbidity. We developed and internally validated a nomogram to estimate individualized anxiety risk in primary HH and examined robustness in prespecified sensitivity analyses. We retrospectively analyzed 234 consecutive patients with primary HH at a single center (June 2020–August 2023). Eligibility followed Hornberger criteria; secondary causes were excluded by history/physical examination, medication review, and targeted laboratory tests when indicated. Anxiety was diagnosed clinically according to CCMD‐3. After LASSO prescreening with a priori clinical inclusion, the final multivariable logistic model retained 10 readily available predictors: diabetes, intolerance to cold, intolerance to heat, male sex, fatigue, and sweating at the head, hand, foot, chest, and neck. Internal validation used 1000‐bootstrap optimism correction to assess discrimination and calibration; clinical utility was quantified with decision‐curve analysis (DCA) across threshold probabilities of 0.02–0.36. The nomogram showed good discrimination (optimism‐corrected C‐index 0.826) and good calibration (bootstrap‐corrected MAE 0.033, Brier 0.219). Three prespecified sensitivity analyses yielded consistent conclusions: SA1 (no diabetes) C‐index 0.827, Brier 0.221, MAE 0.043; SA2 (craniofacial involvement + number of involved sites) C‐index 0.839, Brier 0.220, MAE 0.028; and SA3 (stricter exclusion) C‐index 0.811, Brier 0.211, MAE 0.045. Across thresholds of 0.02–0.36, all models demonstrated positive net benefit versus treat‐none with largely overlapping curves. This transparent, internally validated tool may help identify HH patients at high risk of anxiety and support timely psychological intervention; external multicenter validation is warranted.