• Artificial Intelligence May Predict Early Sepsis After Liver Transplantation

      Kamaleswaran, Rishikesan; Sataphaty, Sanjaya K; Mas, Valeria R; Eason, James D; Maluf, Daniel G (Frontiers Media S.A., 2021-09-06)
      Background: Sepsis, post-liver transplantation, is a frequent challenge that impacts patient outcomes. We aimed to develop an artificial intelligence method to predict the onset of post-operative sepsis earlier. Methods: This pilot study aimed to identify "physiomarkers" in continuous minute-by-minute physiologic data streams, such as heart rate, respiratory rate, oxygen saturation (SpO2), and blood pressure, to predict the onset of sepsis. The model was derived from a cohort of 5,748 transplant and non-transplant patients across intensive care units (ICUs) over 36 months, with 92 post-liver transplant patients who developed sepsis. Results: Using an alert timestamp generated with the Third International Consensus Definition of Sepsis (Sepsis-3) definition as a reference point, we studied up to 24 h of continuous physiologic data prior to the event, totaling to 8.35 million data points. One hundred fifty-five 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 (XGB) classifier was then trained on the ranked features by 5-fold cross validation on all patients (n = 5,748). We identified that the average sensitivity, specificity, positive predictive value (PPV), and area under the receiver-operator curve (AUC) of the model after 100 iterations was 0.94 ± 0.02, 0.9 ± 0.02, 0.89 ± 0.01, respectively, and 0.97 ± 0.01 for predicting sepsis 12 h before meeting criteria. Conclusion: The data suggest that machine learning/deep learning can be applied to continuous streaming data in the transplant ICU to monitor patients and possibly predict sepsis.
    • Clinical and Genetic Risk Factors of Recurrent Nonalcoholic Fatty Liver Disease After Liver Transplantation

      Satapathy, Sanjaya K; Tran, Quynh T; Kovalic, Alexander J; Bontha, Sai Vineela; Jiang, Yu; Kedia, Satish; Karri, Saradashri; Mupparaju, Vamsee; Podila, Pradeep S B; Verma, Rajanshu; et al. (Lippincott Williams and Wilkins, 2021-02-05)
      INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD) has been increasingly reported among recipients of liver transplantation (LT). We aimed to identify clinical and genetic risk factors responsible for the development of early recurrent NAFLD in nonalcoholic steatohepatitis transplant recipients. METHODS: Forty-six total single nucleotide polymorphisms with known association with NAFLD were tested among both recipient and donor liver samples in 66 LT recipients with nonalcoholic steatohepatitis to characterize influences on NAFLD recurrence at ∼1 year post-LT (median interval from LT to biopsy: 377 days). RESULTS: Recurrent NAFLD was identified in 43 (65.2%) patients, 20 (30.3%) with mild recurrence, and 23 (34.8%) with moderate to severe NAFLD. On adjusted analysis, change in the body mass index (BMI) (ΔBMI) was significantly associated with NAFLD recurrence, whereas post-LT diabetes mellitus was associated with increased severity of NAFLD recurrence. ADIPOR1 rs10920533 in the recipient was associated with increased risk of moderate to severe NAFLD recurrence, whereas the minor allele of SOD2 rs4880 in the recipient was associated with reduced risk. Similar reduced risk was noted in the presence of donor SOD2 rs4880 and HSD17B13 rs6834314 polymorphism. DISCUSSION: Increased BMI post-LT is strongly associated with NAFLD recurrence, whereas post-LT diabetes mellitus was associated with increased severity of NAFLD recurrence. Both donor and recipient SOD2 rs4880 and donor HSD17B13 rs6834314 single nucleotide polymorphisms may be associated with reduced risk of early NAFLD recurrence, whereas presence of the minor allele form of ADIPOR1 rs10920533 in the recipient is associated with increased severity NAFLD recurrence. Copyright © 2021 The Author(s).