Clinical Predictive Modeling of Anastomotic Leaks Using Hyperspectral Imaging

Abstract

INTRODUCTION: Anastomotic leaks (AL) remain virtually unchanged despite advancements in minimally invasive surgical techniques and postoperative care. Adequate intraoperative bowel perfusion assessment remains a standard of surgical care to prevent AL. Although several platforms offer objective assessment of bowel perfusion, their reliable use has not demonstrated AL prevention and involves cumbersome steps through sterile preparation or use of dyes. We harnessed machine learning to address these 2 concerns by generating a clinical-predictive modeling tool for AL using a well-established platform of tissue perfusion called hyperspectral imaging. METHODS: We analyzed oxyhemoglobin, deoxyhemoglobin, and oxygen saturation levels of pre- and postanastomotic bowel segments intraoperatively at Brigham and Women’s Hospital using the contactless, point-and-shoot platform called HyperView. Patients 18 years or older undergoing open or laparoscopic-assisted abdominal surgery were included in the study. We followed patients postoperatively for development of AL and perianastomotic abscesses, and preliminarily analyzed the data using random forests to assess variable importance. We will perform a final analysis of the cohort by applying support vector machine to train a model with 80% training and 20% testing partition using a multiple kernel approach. RESULTS: Preliminary results demonstrate a strong correlation of oxyhemoglobin and oxygen saturation levels in preanastomotic intestinal limbs with the incidence of AL. The variation in oxygen saturation and mean oxyhemoglobin levels in the common channel postanastomoses is correlated with development of perianastomotic abscesses. CONCLUSION: We report an innovative intraoperative assessment tool using a noninvasive, contactless imaging technology for intestinal perfusion assessment that can clinically predict incidence of AL and guide intraoperative decisions.