Big data insights into predictors of acute compartment syndrome

•The TQP data identified several predictors of fasciotomy including new information on substance abuse disorder, cirrhosis, and smoking.•Big data approach shows us that ACS is primarily linked to the extent of soft tissue damage.•Sex, BMI, cirrhosis, tobacco smoking, and fracture pattern had predictive value on actual myonecrosis.•Amputation resulted after 5.4% of fasciotomies. There remain gaps in knowledge regarding the pathophysiology, initial diagnosis, treatment, and outcome of acute compartment syndrome (ACS). Most reported clinical outcomes are from smaller studies of heterogeneous patients. For a disease associated with a financial burden to society that represents billions of dollars worldwide the literature does not currently establish baseline diagnostic parameters and risk factors that may serve to predict treatment and outcomes. This study looks at a very large cohort of trauma patients obtained from four recent years of the Trauma Quality Programs data from the American College of Surgeons. From 3,924,127 trauma cases - 203,500 patients with tibial fractures were identified and their records examined for demographic information, potential risk factors for compartment syndrome, an associated coded diagnosis of muscle necrosis, and presence of other outcomes associated with compartment syndrome. A recurrent multiple logistic regression model was used to identify factors predictive of fasciotomy. The results were compared to the reported results from the literature to validate the findings. The rate of fasciotomy treatment for ACS was 4.3% in the cohort of identified patients. The analysis identified several clinical predictors of fasciotomy. Proximal and midshaft tibial fractures (P