Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.

Objective: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily facial trauma volume. This can provide epidemiologic understanding and subsequently tailor workforce distribution and scheduling.

Study Design: Retrospective cohort study.

Setting: Single Tertiary Academic Medical Center.

Methods: Facial trauma consults between 2017 and 2023 were extracted from a single Level I Trauma Center. Publicly accessible data on local concerts, National Hockey League games, National Football League games, and weather data from the National Oceanic and Atmospheric Administration data were merged with trauma data. Machine-learning random-forest (RF) plot feature identification was used to identify variables to model high-volume facial trauma days (greater than 75th percentile).

Results: For analysis, 2342 days were included. The median number of facial trauma consults was 3.0 (interquartile range: 2.0-5.0). The month of May exhibited the highest rate of high-volume trauma days (13% of days, P < .001). On RF feature identification, the strongest predictive factors included weekend day status, average temperature, precipitation, hail, high/damaging winds, and holidays. Regional events were not included in the final models. On stepwise logistic regression modeling with pertinent variables, weekend day (odds ratio [OR]: 2.20, 95% confidence interval [CI]: 1.80-2.69, P < .001), average temperature (OR: 1.02, 95% CI: 1.01-1.02, P < .001), and wind speed (0.97, 0.93-1.00, P = .049) were the only statistically significant variables.

Conclusion: Climate data were the primary factor that had predictive capacity for high-volume facial trauma days, more so than regional events. Testing models prospectively will help validate such models and help inform staffing for facial trauma coverage.