Artificial Intelligence May Predict Early Sepsis After Liver Transplantation - Kamaleswaran, Rishikesan; Sataphaty, Sanjaya K.; Mas, Valeria R.; Eason, James D.; Maluf, Daniel G. (Frontiers in Physiology, 2021)
Background: Sepsis post liver transplantation is a frequent challenge impacting patient outcomes. We aimed to develop an artificial intelligence method to predict earlier the onset of post-operative sepsis. Methods: This pilot study aimed to identify ‘physiomarkers’ in continuous minute-by-minute physiologic data streams to predict the onset of sepsis. The model was derived from a cohort of 5,748 transplant and non-transplant patients across ICUs over 36-months with 92 post-liver transplant patients that developed sepsis. Results: Using an alert timestamp generated by the Sepsis-3 definition as a reference point, we studied up to 24 prior hours of continuous physiologic data previous to the event, totaling 8.35 million data points. 150 features were generated using signal processing and statistical methods. Feature selection identified 52 highly ranked features, many of which included blood pressures. An eXtreme Gradient Boost (XGBoost) classifier was then trained on the ranked features using 5-fold cross validation on all patients (n=5,748). We identified that the average sensitivity, specificity, PPV and AUC after 100-iterations of the model was 0.94 ±0.02, 0.90 ±0.02, 0.89 ±0.01, 0.97 ±0.01 on predicting sepsis 12 hours before meeting criteria. Conclusion: Our data suggests that machine learning/deep learning can be applied to continuous streaming data in the transplant ICU to monitor patients and may predict sepsis.