Establishing quantitative performance thresholds in microvascular simulation to predict real-world surgical proficiency (Preprint)

BACKGROUND Microvascular neurosurgery training is limited by low case volumes, high complexity, and significant risks to the patient. Simulation offers a safe, high‐repetition alternative for skills acquisition for trainees. OBJECTIVE The purpose of this study was to evaluate the UpSurgeOn Mycro Box for end-to-end microvascular anastomosis by establishing quantitative predictive performance thresholds of surgical expertise. METHODS Twenty-nine participants performed a standardised end-to-end microvascular anastomosis using the Mycro Box simulator. Quantitative performance metrics were extracted for each trial. A multivariable logistic regression model with ridge penalization was trained using nested crossvalidation to classify participants as expert (i.e. status as attending neurosurgeon) versus non-expert based on these metrics. Model discrimination was assessed by balanced accuracy and area under the ROC curve (AUC), and 95% confidence intervals were obtained via bootstrapped resampling. The best performing model was used to derive quantitative performance thresholds corresponding to a 50% probability of expert-level classification. RESULTS The best-performing model incorporated operative time, number of sutures, and Objective Structured Assessment of Technical Skills (OSATS) score as predictors. This model achieved outstanding discriminative ability with a balanced classification accuracy of 94% and a ROC-AUC of 0.98. All three performance metrics were independent predictors of expertise. From the model, we generated objective proficiency benchmarks on the simulator, visualised with a faceted bivariate regression plot. CONCLUSIONS Performance on the Mycro Box simulator could predict a surgeon’s level of expertise, supporting the simulator’s construct validity. An interpretable regression model provided quantitative performance thresholds for proficiency. These findings suggest that objective metrics from simulation can define expert-level performance benchmarks, which may be used to guide training curricula and competency-based progression in neurosurgery. CLINICALTRIAL N/A