Risk factors and exploratory clustering of complications after reconstruction following Mohs surgery: A national NIH All of Us study.
retrospective_cohort · Level III
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- Record sourced from PubMed, PMID 42019268.
- Also identified by DOI 10.1016/j.bjps.2026.03.027.
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Abstract
Mohs micrographic surgery (MMS) is an important surgical technique in the management of non-melanoma skin cancer; however, knowledge of the outcomes from Mohs reconstruction remains limited. The advent of nationwide databases coupled with advanced statistical and exploratory analytic approaches affords an opportunity to address this critical knowledge gap. We conducted a retrospective cohort study of 6062 patients from the NIH All of Us Research Program who underwent reconstruction after MMS within 3 months of a skin cancer diagnosis. Demographics, comorbidities, and procedural data were extracted using OMOP fields. Complications within 30 days and 1 year were identified using SNOMED-OMOP codes. Logistic regression evaluated predictors of complications, and K-means clustering (age and body mass index (BMI)) was employed as an exploratory approach to assess risk patterns. The overall complication rates were 3.2% at 30 days and 11.9% at 1 year. In multivariable models, primary closure was associated with lower odds of 1-year complications, whereas older age and male sex were associated with higher odds. BMI significantly predicted individual complications (e.g., cellulitis and wound dehiscence) in univariate models but was not independently significant in multivariable analysis. Hypertension and diabetes were associated with cellulitis, hematoma, dehiscence, and nerve injury. K-means clustering generated four age-BMI-defined subgroups, with younger patients with higher BMI exhibiting the highest 1-year complication burden (17.6%). Complication risk after post-MMS reconstruction varies by age, BMI, comorbidities, and procedure type. Exploratory unsupervised clustering suggested that age and BMI were associated with differing complication burdens across subgroups, supporting risk stratification.