Incidence and pattern of traumatic spine injury in a single level I trauma center of southern Iran

Purpose:Spine injury is one of the leading causes of death and mortality worldwide.The objective of this study was to determine the incidence,pattern and outcome of trauma patients with spine injury referred to the largest trauma center in southern Iran during the last 3 years.Methods:This is a cross-sectional study conducted between March 2018 and June 2021 in the largest trauma center in the southern Iran.The data collection form included the age,sex,injury location(cervical,thoracic,and lumbar),cause of injury(traffic accidents,falls,and assaults),length of hospital stay,injured segment of spine injury,severity of injury,and outcome.Statistical analyzes were performed using SPSS software version 24.Results:Totally 776 cases of spine injury were identified.The spine injury rate was 17.0%,and the mortality rate was 15.5%.Cervical spine injury(20.4%)more often occulted in motorcycle accident,and thoracic spine injury(20.1%)occulted in falls.The highest and lowest rates of spine injurys were related to lumbar spine injury(30.2%)and cervical spine injury(21.5%),respectively.There was a statistically significant relationship between the mechanism of injury and the location of spine injury(p<0.001).And patients with lumbar spine injury had the highest mortality rate(16.7%).Injury severity score(OR=1.041,p<0.001)and length of stay(OR=1.018,p<0.001)were strong predictors of mortality in trauma patients with spine injury.Conclusion:The results of the study showed that the incidence of traumatic spine injury rate was approximately 17.0%in southern of Iran.Road traffic injury and falls are the common mechanism of injury to spine.It is important to improve the safety of roads,and passengers,as well as work envi-ronment,and improve the quality of cars.Also,paying attention to the pattern of spine injury may assist to prevent the missing diagnosis of spine injury in multiple trauma patients.

Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation?Using data mining artificial intelligence

Purpose The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury.Early recognition of patients is an important case of such decision-making with risk of worse prognosis.This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.Methods The study included 1107 trauma patients,16 years and older.The patients were trauma victims of Levels I and II triage and admitted to the Rajaee(Emtiaz)Trauma Hospital,Shiraz,in 2014–2015.The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients’prognosis.Five modeling methods including the support vector machine,K-nearest neighbor algorithms,Bagging and Adaboost,and the neural network were compared by some evaluation criteria.Results Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99%accuracy.The most-fitted variables were Glasgow Coma Scale score,base deficit,and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.Conclusion Data mining could help in triage,initial treatment,and further decision-making for outcome measures in trauma patients.Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival.With artificial intelligence modeling system,diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation.Artificial intelligence monitoring may have a role in trauma care and should be further investigated.