Development of a Risk Prediction Model for Complications Following Forehead Flaps for Nasal and Periorbital Reconstruction.

BACKGROUND

Forehead flaps are a workhorse for nasal reconstruction, but complications occur in up to 30% of patients and risk factors are not well characterized. This study aimed to identify risk factors for complications, and provide clinicians a method to stratify patient risk to facilitate shared decision-making.

MATERIALS AND

METHODS

This retrospective study included patients who underwent forehead flaps between 2007 and 2020. Demographic and treatment characteristics were abstracted, in addition to clinical outcomes data. Multivariable regression was conducted, with step-wise variable elimination to determine inclusion in the final model. From the final regression, a risk-stratification scheme was developed.

RESULTS

One hundred ninety-seven patients underwent forehead flap reconstruction, with a mean age of 68.5 years. Mean follow-up time was 42 months. There were 50 (25.4%) patients who developed a complication, including impaired nasal function (18.8%), flap congestion (5.1%), infection (2.5%), poor donor site healing (2.5%) wound dehiscence (2.0%), and flap congestion (1.5%). On univariate analysis, female sex, immunosuppression, prior radiotherapy, and larger resection area were associated with complications ( P <0.05). On multivariable analysis, female sex [odds ratio (OR): 3.89, P <0.001], hypoalbuminemia (OR: 3.70, P =0.01), and prior wide local excision (OR: 3.62, P =0.04) were predictors of complications. A clinical calculator was developed incorporating these risk factors, with a C-statistic of 0.85, indicating strong predictive value.

CONCLUSIONS

We conducted the most comprehensive review of risk factors for the development of complications after forehead flap reconstruction. From this analysis, a novel, implementable, risk-stratification scheme was developed to equip surgeons with the ability to provide individualized risk assessment to patients and address preoperative comorbidities to optimize outcomes.