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

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.