Background: The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a Level I trauma center.
Methods: Trauma admission data from Trauma Registry of the American College of Surgeons and weather data from the National Oceanic and Atmospheric Administration was collected for all adult trauma patients from July 2013-June 2016. The ANN was constructed using temporal (time, day of week), and weather factors (daily high, active precipitation) to predict four points of daily trauma activity: number of traumas, number of penetrating traumas, average Injury Severity Score (ISS), and number of immediate operative cases per day. We trained a two-layer feed-forward network with 10 sigmoid hidden neurons via the Levenberg-Marquardt back propagation algorithm, and performed k-fold cross validation and accuracy calculations on 100 randomly generated partitions.
Results: Ten thousand six hundred twelve patients over 1,096 days were identified. The ANN accurately predicted the daily trauma distribution in terms of number of traumas, number of penetrating traumas, number of OR cases, and average daily ISS (combined training correlation coefficient r = 0.9018 ± 0.002; validation r = 0.8899 ± 0.005; testing r = 0.8940 ± 0.006).
Conclusion: We were able to successfully predict trauma and emergent operative volume, and acuity using an ANN by integrating local weather and trauma admission data from a Level I center. As an example, for June 30, 2016, it predicted 9.93 traumas (actual: 10), and a mean ISS of 15.99 (actual: 13.12). This may prove useful for predicting trauma needs across the system and hospital administration when allocating limited resources.
Level of evidence: Prognostic/epidemiological, level III.