RESUMEN
BACKGROUND: Conflicting evidence exists evaluating associations between cannabis (THC) and post-traumatic DVT. METHODS: Retrospective analysis (2014-2023) of patients ≥15yrs from two Level I trauma centers with robust VTE surveillance and prophylaxis protocols. Multivariable hierarchical regression assessed the association between THC and DVT risk. THC â+ âpatients were direct matched to other drug use categories on VTE risk markers and hospital length of stay. RESULTS: Of 7365 patients, 3719 were drug-, 575 were THC â+ âonly, 2583 were other drug+, and 488 were TCH+/other drug+. DVT rates by exposure group did not differ. TCH â+ âonly patients had higher GCS scores, shorter hospital length of stay, and the lowest pelvic fracture and mortality rates. A total of 458 drug-, 453 other drug+, and 232 THC+/other drug â+ âpatients were matched to 458, 453, and 232 THC â+ âonly patients. There were no differences in DVT event rates in any paired sub-cohort set. Additionally, iteratively adjusted paired models did not show an association between THC and DVT. CONCLUSIONS: THC does not appear to be associated with increased DVT risk in patients with strict trauma chemoprophylaxis. Toxicology testing is useful for identifying substance abuse intervention opportunities, but not for DVT risk stratification in THC â+ âpatients.
RESUMEN
BACKGROUND: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. METHODS: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. RESULTS: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. CONCLUSION: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.
