Novel Use of a Real-Time Prediction Model to Enhance Early Detection of Need for Massive Transfusion—Artificial Intelligence Behind the Drapes

Abstract

Intraoperative massive hemorrhage can be associated with high mortality if not treated promptly. 1 Lee et al 2 conducted a prognostic study incorporating intraoperative hemodynamic monitoring data into a validated artificial intelligence (AI)-assisted massive transfusion prediction model. The authors compared the ability of an AI prediction model using intraoperative hemodynamic monitoring with a current reference-standard model (using only preoperative data) to detect need for massive transfusion intraoperatively-10 minutes before the need for massive transfusion occurred. 2 The massive transfusion index prediction model outperformed the standard model in the internal and external validation single-center cohorts (area under the receiver operating characteristic curve, 0.972 vs area under the curve, 0.824). 2 The authors' novel use of real-time intraoperative data to predict upcoming need for a massive transfusion is intriguing and pushes the limits of most current AI clinical applications.The emerging field of surgical data science is transforming surgery and care of the surgical patient. 3 The advent of preoperative risk calculators and postoperative prediction tools for various potential surgical complications has been studied, yet correlation with actual patient outcomes in real-time remain futuristic, limiting informative prognostic discussions with patients and their families at bedside. 4 Encouragingly, more sophisticated AI predictive surgical models and safety systems have become available to help guide difficult goals-of-care discussions with patients and are able to codify intraoperative team communication, real-time patient monitoring, and intraoperative video analysis. 3,5 However, real-time prognostication for the surgical patient for most outcomes of interest remain far off.The importance of having the ability to predict resource needs rapidly, before an event occurs, is as important now as ever. At a tertiary referral center, resources like massive transfusion protocols, blood availability, and surgical expertise can be quickly mobilized in the setting of an acute surgical emergency. 6,7 However, these resources are not universally available. In settings where immediate support is not present, such as rural or resource-limited settings, having lead time to predict an event like the need for massive transfusion may well mean the difference between life and death for a patient. Once the authors' algorithm has been clinically validated with a broader patient population, we think the use of this AI predictive model could be helpful in resource-limited hospital settings, prompting resource allocation prior to an event occurring, rather than playing catch-up. Even more importantly, knowing a patient is at risk of an event, before the event occurs, may encourage and enhance communication between surgical and anesthesia teams helping mitigate the event entirely, or minimizing any untoward downstream effects.There are important limitations to this study to keep in mind. There was lack of granular detail o...