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Can an App-Based Maxillofacial Trauma Score Predict the Operative Time, ICU Need and Length of Stay?
Ashutosh Kumar Singh1, Safal Dhungel2, Zeeshan Ahmad3
1Department of Oral and Maxillofacial Surgery, TU Dental Teaching Hospital, MMC, IOM, Kathmandu, Nepal.
Study Design:
Retrospective chart review.
Objective:
Injury and trauma scores are the mainstay of predicting outcomes of trauma patients. ZS (Zeeshan and Simon) maxillofacial trauma score is based on 4 previous facial trauma scores and is user friendly, app-based visually coded facial trauma scoring system. Our study was designed to seek the application of an app-based ZS maxillofacial trauma score to predict the operative time, intensive care unit (ICU) need and length of stay.
Methods:
We performed a retrospective chart review of patients who presented to a university medical college teaching hospital with maxillofacial fractures from October 2018 until October 2019. ZS maxillofacial trauma scoring app was used to calculate the ZS maxillofacial trauma severity score, which was our primary predictor variable. Our primary outcome of interest was operative time. Our secondary outcome of interest was ICU need and length of stay. Correlation analysis, linear regression and logistic regression were performed for statistical analysis. A statistical P-value of .05 was considered significant at a 95% confidence interval.
Results:
There were 95 male and 5 female patients included in the study. The age ranged from 3 to 84 years with a mean of 30.76 (SD = 14.04). A statistically significant correlation between the ZS score and operative time (r = 0.67, P < .001) was observed. ZS score predicted operative time (b = 7.67, P < .001) in our study sample. Increasing ZS trauma score was also significantly associated with ICU requirement (X (3) = 13.682, P = .003), but the length of stay could not be predicted based on ZS score.Conclusions: ZS maxillofacial trauma score can predict the operative time, and an association was seen with the need for ICU with increasing ZS score, but could not predict the length of stay or the ICU need. It has potential for future integration with electronic health record systems.
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