A feasibility study of partial REBOA data in a high-volume trauma center.

PURPOSE: Resuscitative endovascular balloon occlusion of the aorta (REBOA) is used to temporize patients with infradiaphragmatic hemorrhage. Current guidelines advise < 30 min, to avoid ischemia/ reperfusion injury, whenever possible. The technique of partial REBOA (P-REBOA) has been developed to minimize the effects of distal ischemia. This study presents our clinical experience with P-REBOA, comparing outcomes to complete occlusion (C-REBOA). PATIENTS AND METHODS: Retrospective analysis of patients' electronic data and local REBOA registry between January 2016 and May 2019. INCLUSION CRITERIA: adult trauma patients who received Zone I C-REBOA or P-REBOA for infradiaphragmatic hemorrhage, who underwent attempted exploration in the operating room. Comparison of outcomes based on REBOA technique (P-REBOA vs C-REBOA) and occlusion time (> 30 min, vs ≤ 30 min) RESULTS: 46 patients were included, with 14 treated with P-REBOA. There were no demographic differences between P-REBOA and C-REBOA. Prolonged (> 30 min) REBOA (regardless of type of occlusion) was associated with increased mortality (32% vs 0%, p = 0.044) and organ failure. When comparing prolonged P-REBOA with C-REBOA, there was a trend toward lower ventilator days [19 (11) vs 6 (9); p = 0.483] and dialysis (36.4% vs 16.7%; p = 0.228) with significantly less vasopressor requirement (72.7% vs 33.3%; p = 0.026). CONCLUSION: P-REBOA can be delivered in a clinical setting, but is not currently associated with improved survival in prolonged occlusion. In survivors, there is a trend toward lower organ support needs, suggesting that the technique might help to mitigate ischemic organ injury. More clinical data are needed to clarify the benefit of partial occlusion REBOA.

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

PURPOSE: In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy for major arterial injury on digital subtraction angiography (DSA). American Association for the Surgery of Trauma (AAST) liver injury grading is coarse and subjective, with limited diagnostic utility in this setting. Volumetric measurements of hepatic injury burden could improve prediction. We hypothesized that in a cohort of patients that underwent catheter-directed hepatic angiography following admission trauma CT, a deep learning quantitative visualization method that calculates % liver parenchymal disruption (the LPD index, or LPDI) would add value to CE assessment for prediction of major hepatic arterial injury (MHAI). METHODS: This retrospective study included adult patients with BHI between 1/1/2008 and 5/1/2017 from two institutions that underwent admission trauma CT prior to hepatic angiography (n = 73). Presence (n = 41) or absence (n = 32) of MHAI (pseudoaneurysm, AVF, or active contrast extravasation on DSA) served as the outcome. Voxelwise measurements of liver laceration were derived using an existing multiscale deep learning algorithm trained on manually labeled data using cross-validation with a 75-25% split in four unseen folds. Liver volume was derived using a pre-trained whole liver segmentation algorithm. LPDI was automatically calculated for each patient by determining the percentage of liver involved by laceration. Classification and regression tree (CART) analyses were performed using a combination of automated LPDI measurements and either manually segmented CE volumes, or CE as a binary sign. Performance metrics for the decision rules were compared for significant differences with binary CE alone (the current standard of care for predicting MHAI), and the AAST grade. RESULTS: 36% of patients (n = 26) had contrast extravasation on CT. Median [Q1-Q3] automated LPDI was 4.0% [1.0-12.1%]. 41/73 (56%) of patients had MHAI. A decision tree based on auto-LPDI and volumetric CE measurements (CEvol) had the highest accuracy (0.84, 95% CI 0.73-0.91) with significant improvement over binary CE assessment (0.68, 95% CI 0.57-0.79; p = 0.01). AAST grades at different cut-offs performed poorly for predicting MHAI, with accuracies ranging from 0.44-0.63. Decision tree analysis suggests an auto-LPDI cut-off of ≥ 12% for minimizing false negative CT exams when CE is absent or diminutive. CONCLUSION: Current CT imaging paradigms are coarse, subjective, and limited for predicting which BHIs are most likely to benefit from AE. LPDI, automated using deep learning methods, may improve objective personalized triage of BHI patients to angiography at the point of care.

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative visualization and measurement algorithm predict interventions and outcomes including (a) need for angioembolization (AE), pelvic packing (PP), or massive transfusion (MT), and (b) in-hospital mortality. METHODS: We performed a single-institution retrospective analysis of 253 patients with bleeding pelvic fractures who underwent admission abdominopelvic trauma CT between 2008 and 2017. Included patients had hematoma volumes of 30 mL or greater, were 18 years and older, and underwent contrast-enhanced CT before surgical or angiographic intervention. Automated pelvic hematoma volume measurements were previously derived using a deep-learning quantitative visualization and measurement algorithm through cross-validation. A composite dependent variable of need for MT, AE, or PP was used as the primary endpoint. The added utility of hematoma volume was assessed by comparing the performance of multivariable models with and without hematoma volume as a predictor. Areas under the receiver operating characteristic curve (AUCs) and sensitivities, specificities, and predictive values were determined at clinically relevant thresholds. Adjusted odds ratios of automated pelvic hematoma volumes at 200 mL increments were derived. RESULTS: Median age was 47 years (interquartile range, 29-61), and 70% of patients were male. Median Injury Severity Score was 22 (14-36). Ninety-four percent of patients had injuries in other body regions, and 73% had polytrauma (Injury Severity Score, ≥16). Thirty-three percent had Tile/Orthopedic Trauma Association type B, and 24% had type C pelvic fractures. A total of 109 patients underwent AE, 22 underwent PP, and 53 received MT. A total of 123 patients received all 3 interventions. Sixteen patients died during hospitalization from causes other than untreatable (abbreviated injury scale, 6) head injury. Variables incorporated into multivariable models included age, sex, Tile/Orthopedic Trauma Association grade, admission lactate, heart rate (HR), and systolic blood pressure (SBP). Addition of hematoma volume resulted in a significant improvement in model performance, with AUC for the composite outcome (AE, PP, or MT) increasing from 0.74 to 0.83 (p < 0.001). Adjusted unit odds more than doubled for every additional 200 mL of hematoma volume. Increase in model AUC for mortality with incorporation of hematoma volume was not statistically significant (0.85 vs. 0.90, p = 0.12). CONCLUSION: Hematoma volumes measured using a rapid automated deep learning algorithm improved prediction of need for AE, PP, or MT. Simultaneous automated measurement of multiple sources of bleeding at CT could augment outcome prediction in trauma patients. LEVEL OF EVIDENCE: Diagnostic, level IV.