RESUMEN
BACKGROUND: Maintaining balanced blood product ratios during damage control resuscitation (DCR) is independently associated with improved survival. We hypothesized that real-time performance improvement (RT-PI) would increase adherence to DCR best practice. STUDY DESIGN AND METHODS: From December 2020-August 2021, we prospectively used a bedside RT-PI tool to guide DCR in severely injured patients surviving at least 30 min. RT-PI study patients were compared to contemporary control patients at our institution and historic PROMMTT study patients. A subset of patients transfused ≥6 U red blood cells (RBC) in 6 h (MT+) was also identified. The primary endpoint was percentage time in a high ratio range (≥3:4) of plasma (PLAS):RBC and platelet (PLT):RBC over 6 h. Secondary endpoints included time to massive transfusion protocol activation, time to calcium and tranexamic acid (TXA) dosing, and cumulative 6-h ratios. RESULTS: Included patients (n = 772) were 35 (24-51) years old with an Injury Severity Score of 27 (17-38) and 42% had penetrating injuries. RT-PI (n = 10) patients spent 96% of the 6-h resuscitation in a high PLAS:RBC range, no different versus CONTROL (n = 87) (96%) but more than PROMMTT (n = 675) (25%, p < .001). In the MT+ subgroup, optimal PLAS:RBC and PLT:RBC were maintained for the entire 6 h in RT-PI (n = 4) versus PROMMTT (n = 391) patients for both PLAS (p < .001) and PLT ratios (p < .001). Time to TXA also improved significantly in RT-PI versus CONTROL patients (27 min [22-31] vs. 51 min [29-98], p = .035). CONCLUSION: In this prospective study, RT-PI was associated with optimized DCR. Multicenter validation of this novel approach to optimizing DCR implementation is warranted.
RESUMEN
BACKGROUND: Damage-control resuscitation (DCR) improves trauma survival; however, consistent adherence to DCR principles through multiple phases of care has proven challenging. Clinical decision support may improve adherence to DCR principles. In this study, we designed and evaluated a DCR decision support system using an iterative development and human factors testing approach. METHODS: The phases of analysis included initial needs assessment and prototype design (Phase 0), testing in a multidimensional simulation (Phase 1), and testing during initial clinical use (Phase 2). Phase 1 and Phase 2 included hands-on use of the decision support system in the trauma bay, operating room, and intensive care unit. Participants included trauma surgeons, trauma fellows, anesthesia providers, and trauma bay and intensive care unit nurses who provided both qualitative and quantitative feedback on the initial prototype and all subsequent iterations. RESULTS: In Phase 0, 14 (87.5%) of 16 participants noted that they would use the decisions support system in a clinical setting. Twenty-four trauma team members then participated in simulated resuscitations with decision support where 178 (78.1%) of 228 of tasks were passed and 27 (11.8%) were passed with difficulty. Twenty-three (95.8%) completed a postsimulation survey. Following iterative improvements in system design, Phase 2 evaluation included 21 trauma team members during multiple real-world trauma resuscitations. Of these, 15 (71.4%) completed a formal postresuscitation survey. Device-level feedback on a Likert scale (range, 0-4) confirmed overall ease of use (median score, 4; interquartile range, 4-4) and indicated the system integrated well into their workflow (median score, 3; interquartile range, 2-4). Final refinements were then completed in preparation for a pilot clinical study using the decision support system. CONCLUSIONS: An iterative development and human factors testing approach resulted in a clinically useable DCR decision support system. Further analysis will determine its applicability in military and civilian trauma care. LEVEL OF EVIDENCE: Therapeutic/Care Management, Level V.