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    Artificial Intelligence May Predict Early Sepsis After Liver Transplantation

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    Author
    Kamaleswaran, Rishikesan
    Sataphaty, Sanjaya K
    Mas, Valeria R
    Eason, James D
    Maluf, Daniel G
    Date
    2021-09-06
    Journal
    Frontiers in Physiology
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fphys.2021.692667
    Abstract
    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.
    Rights/Terms
    Copyright © 2021 Kamaleswaran, Sataphaty, Mas, Eason and Maluf.
    Keyword
    artificial intelligence
    liver transplant
    machine learning
    physiological data streams
    sepsis
    surgery
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/16745
    ae974a485f413a2113503eed53cd6c53
    10.3389/fphys.2021.692667
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