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Sepsis clinical trials at UCSF
3 in progress, 0 open to eligible people

  • HindSight Phase II

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    Machine learning is a powerful method for creating clinical decision support (CDS) tools, but it requires training labels which reflect the desired alert behavior. In the Phase I work for this project, investigators have developed an encoding software called HindSight that examines discharged patients' electronic health records (EHR), identifies clinicians' sepsis treatment decisions and patient outcomes, and passes these labeled examples to an online algorithm for retraining InSight, a machine-learning-based CDS tool for real-time sepsis prediction. Although HindSight has been shown to be successful in improving the performance of InSight in retrospective work, it has yet to be validated in prospective settings; therefore, in this project, the clinical utility of HindSight will be assessed through a multicenter randomized controlled trial (RCT).

  • RCT of Sepsis Machine Learning Algorithm

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    The focus of this study will be to conduct a prospective, multi-center randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a machine-learning algorithm will be applied to EHR data for the detection of sepsis. For patients determined to have a high risk of sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality.

  • Subpopulation-Specific Sepsis Identification Using Machine Learning

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    The focus of this study will be to conduct a prospective, randomized controlled trial (RCT) at Cape Regional Medical Center (CRMC), Oroville Hospital (OH), and UCSF Medical Center (UCSF) in which a subpopulation-optimized algorithm will be applied to EHR data for the detection of severe sepsis. For patients determined to have a high risk of severe sepsis, the algorithm will generate automated voice, telephone notification to nursing staff at CRMC, OH, and UCSF. The algorithm's performance will be measured by analysis of the primary endpoint, in-hospital SIRS-based mortality. The secondary endpoints will be in-hospital severe sepsis/shock-coded mortality, SIRS-based hospital length of stay, and severe sepsis/shock-coded hospital length of stay.

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