Development and validation of a model to predict complex Mohs micrographic surgery in clinical practice: REGESMOSH
Trinidad Montero-Vilchez1,2, Joan R Garcés3,4, Manuel A Rodríguez-Prieto5
1Hospital Universitario Virgen de las Nieves, Granada, Spain.
Introduction:
There is still a need to develop a simple algorithm to identify patients likely to need complex Mohs micrographic surgery (MMS) and optimize MMS schedule. The main objectives of this study are to identify factors associated with a complex MMS and develop a predictor model of the number of stages needed in surgery and the need for a complex closure.
Materials And Methods:
A nationwide prospective cohort study (REGESMOHS, the Spanish Mohs surgery registry) was conducted including all patients with a histological diagnosis of basal cell carcinoma (BCC). Factors related to three or more stages and a complex closure (that needing a flap and/or a graft) were explored and predictive models were constructed and validated to construct the REGESMOSH scale.
Results:
A total of 5226 patients that underwent MMS were included in the REGESMOHS registry, with 4402 (84%) having a histological diagnosis of BCC. A total of 3689 (88.9%) surgeries only needed one or two stages and 460 (11.1%) required three or more stages. A model to predict the need for three or more stages included tumour dimension, immunosuppression, recurrence, location in risk areas, histological aggressiveness and previous surgery. Regarding the closure type, 1616 (38.8%) surgeries were closed using a non-complex closure technique and 2552 (61.2%) needed a complex closure. A model to predict the need for a complex closure included histological aggressiveness, evolution time, patient age, maximum tumour dimension and location.
Conclusion:
We present a model to predict MMS needing ≥3 stages and a complex closure based on epidemiological and clinical data validated in a large population (with real practice variability) including different centres that could be easily implemented in clinical practice. This model could be used to optimize surgery schedule and properly inform patients about the surgery duration.
