Artificial intelligence evaluation of focused assessment with sonography in trauma.

BACKGROUND: The focused assessment with sonography in trauma (FAST) is a widely used imaging modality to identify the location of life-threatening hemorrhage in a hemodynamically unstable trauma patient. This study evaluates the role of artificial intelligence in interpretation of the FAST examination abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity. METHODS: Focused assessment with sonography for trauma examination images from 2015 to 2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with more than 3,500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant and left upper quadrant views were obtained and read by a surgeon or radiologist. Positivity was defined as fluid present in the hepatorenal or splenorenal fossa, while adequacy was defined by the presence of both the liver and kidney or the spleen and kidney for the right upper quadrant or left upper quadrant views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated. RESULTS: A total of 6,608 images, representing 109 cases were included for analysis within the "adequate" and "positive" data sets. The models relayed 88.7% accuracy, 83.3% sensitivity, and 93.6% specificity for the adequate test cohort, while the positive cohort conferred 98.0% accuracy, 89.6% sensitivity, and 100.0% specificity against similar models. Augmentation improved the accuracy and sensitivity of the positive models to 95.1% accurate and 94.0% sensitive. DenseNet121 demonstrated the best accuracy across tasks. CONCLUSION: Artificial intelligence can detect positivity and adequacy of FAST examinations with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. Artificial intelligence is a feasible modality to improve patient care imaging interpretation accuracy and should be pursued as a point-of-care clinical decision-making tool. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level III.

Surgical Site Infections in Colorectal Resections: What is the Cost?

INTRODUCTION: Although surgical site infections (SSIs) associated with colectomy are tracked by the National Healthcare Safety Network/Center for Disease Control, untracked codes, mainly related to patients undergoing proctectomy, are not. These untracked codes are performed less often yet they may be at a greater risk of SSI due to their greater complexity. Determining the impact and predictors of SSI are critical in the development of quality improvement initiatives. METHODS: Following an institutional review board approval, National Surgery Quality Improvement Program, institutional National Surgery Quality Improvement Program, and financial databases were queried for tracked colorectal resections and untracked colorectal resections (UCR). National data were obtained for January 2019-December 2019, and local procedures were identified between January 2013 and December 2019. Data were analyzed for preoperative SSI predictors, operative characteristics, outcomes, and 30-day postdischarge costs (30dPDC). RESULTS: Nationally, 71,705 colorectal resections were identified, and institutionally, 2233 patients were identified. UCR accounted for 7.9% nationally and 11.8% of all colorectal resections institutionally. Tracked colorectal resection patients had a higher incidence of SSI predictors including sepsis, hypoalbuminemia, coagulopathy, hypertension, and American Society of Anesthesiologists class. UCR patients had a higher rate of SSIs [12.9% (P < 0.001), 15.2% (P = 0.064)], readmission, and unplanned return to the operating room. Index hospitalization and 30dPDC were significantly higher in patients experiencing an SSI. CONCLUSIONS: SSI was associated with nearly a two-fold increase in index hospitalization costs and six-fold in 30dPDC. These data suggest opportunities to improve hospitalization costs and outcomes for patients undergoing UCR through protocols for SSI reduction and preventing readmissions.