Developing the Aesthetic Postoperative Complication Score for Detecting Major Morbidity in Facial Aesthetic Surgery

Abstract

INTRODUCTION: Facial aesthetic surgery encompasses a wide variety of procedures with a complication rate that is difficult to estimate. To explore this further, we sought to estimate major complication rates in patients undergoing facial aesthetic procedures and to develop a risk assessment tool to stratify risk. METHODS: We utilized the Tracking Operation and Outcomes for Plastic Surgeons database from 2003 to 2018. The database was evaluated to include major facial aesthetic procedures selected based on CPT codes. All infra-cervical procedures were excluded. Procedures included were blepharoplasty (upper and lower), rhytidectomy (forehead, neck with platysma tightening, glabellar, SMAS flap, cheek, chin, and neck), repair or brow ptosis, repair of blepharoptosis, canthopexy (lateral and medial), genioplasty, augmentation of mandibular body, primary rhinoplasty (minor and major), revision rhinoplasty (minor and major), subcutaneous injection/fat transfer, cervicoplasty, and otoplasty. Demographics, comorbidities, and procedures were analyzed with univariate analysis for initial selection. Clinically relevant cutoff points were used to dichotomize significant continuous variables for the model. A backward stepwise multivariate regression model was developed to determine the risk factors for prediction. Goodness-of-fit was assessed with Hosmer-Lemeshow test. Regression coefficients were multiplied by two and rounded up to the nearest integer, and then summed to create the total score. Area under receiver operating characteristic curves were used to measure performance and choose optimal predictive models. Lastly, sensitivity analysis was performed with a complete case analysis to evaluate robustness of the model. RESULTS: A total of 38,569 patients were identified to have had a facial aesthetic procedure. The major complication rate for this adult population undergoing at least one facial aesthetic procedure was 1.44% (460). From the available demographics and perioperative variables, those statistically significant in univariate analysis included current/former smoker, diabetes mellitus, body mass index, ASA classification, canthoplasty, blepharoptosis repair, rhytidectomy in forehead, rhytidectomy with platysma thickening, rhytidectomy with SMAS flap, rhytidectomy with check/chin/neck, cervicoplasty, subcutaneous injection of filler/fat graft, primary rhinoplasty, lower blepharoplasty, and over three procedures were performed at the same time. In the final stepwise backward regression model undergoing rhytidectomy with platysmal tightening, rhytidectomy of cheek/chin/neck, cervicoplasty, over three surgeries at the same time, BMI ≥ 25, and ASA class ≥ 2 were the variables fit for calculating the risk prediction score [n = 13,349; AUC: 0.70, SE: 0.02, (0.66–0.74)]. The sum of the calculated integers gives a total score of 7 with cervicoplasty counting for two points and the rest of the covariates each given one point. Sensitivity analysis from each level of our score (0–7) showed the cutoff point of ≥2 to best balance sensitivity and specificity, with 58% and 70%, respectively. At this cutoff point, 70% of cases were correctly classified as a major complication (n = 12,764). CONCLUSIONS: Despite low morbidity rates, we were able to develop an acceptable risk prediction score with a cutoff value of ≥2 correctly classifying approximately 70% of major morbidity in adult patients undergoing face and neck aesthetic surgery.