Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study
Introduction About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delay...
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BMJ Publishing Group
2019-12-01
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| author | Christine Lee Maxime Cannesson Ira Hofer Joseph Rinehart Kathirvel Subramaniam Pierre Baldi Artur Dubrawski Michael R Pinsky |
| author_facet | Christine Lee Maxime Cannesson Ira Hofer Joseph Rinehart Kathirvel Subramaniam Pierre Baldi Artur Dubrawski Michael R Pinsky |
| author_sort | Christine Lee |
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| description | Introduction About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delayed treatment leads to increased morbidity and mortality. The goal of this proposal is to develop, validate and test real-time intraoperative risk prediction tools based on clinical data and high-fidelity physiological waveforms to predict haemodynamic instability during surgery.Methods and analysis We will initiate our work using an existing annotated intraoperative database from the University of California Irvine, including clinical and high-fidelity waveform data. These data will be used for the training and development of the machine learning model (Carnegie Mellon University) that will then be tested on prospectively collected database (University of California Los Angeles). Simultaneously, we will use existing knowledge of haemodynamic instability patterns derived from our intensive care unit cohorts, medical information mart for intensive care II data, University of California Irvine data and animal studies to create smart alarms and graphical user interface for a clinical decision support. Using machine learning, we will extract a core dataset, which characterises the signatures of normal intraoperative variability, various haemodynamic instability aetiologies and variable responses to resuscitation. We will then employ clinician-driven iterative design to create a clinical decision support user interface, and evaluate its effect in simulated high-risk surgeries.Ethics and dissemination We will publish the results in a peer-reviewed publication and will present this work at professional conferences for the anaesthesiology and computer science communities. Patient-level data will be made available within 6 months after publication of the primary manuscript. The study has been approved by University of California, Los Angeles Institutional review board. (IRB #19–0 00 354). |
| format | Article |
| id | doaj-art-8ff011af1e0e44c88599e0838befb4eb |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-8ff011af1e0e44c88599e0838befb4eb2024-12-03T04:35:13ZengBMJ Publishing GroupBMJ Open2044-60552019-12-0191210.1136/bmjopen-2019-031988Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective studyChristine Lee0Maxime Cannesson1Ira Hofer2Joseph Rinehart3Kathirvel Subramaniam4Pierre Baldi5Artur Dubrawski6Michael R Pinsky7Bioengineering, UC Irvine, Irvine, California, USA4 Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USAAnesthesiology, UCLA, Los Angeles, California, USAAnesthesiology, UC Irvine, Irvine, California, USAAnesthesiology, UPMC, Pittsburgh, Pennsylvania, USAComputer Sciences, UC Irvine, Irvine, California, USAComputer Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, USACritical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USAIntroduction About 42 million surgeries are performed annually in the USA. While the postoperative mortality is less than 2%, 12% of all patients in the high-risk surgery group account for 80% of postoperative deaths. New onset of haemodynamic instability is common in surgical patients and its delayed treatment leads to increased morbidity and mortality. The goal of this proposal is to develop, validate and test real-time intraoperative risk prediction tools based on clinical data and high-fidelity physiological waveforms to predict haemodynamic instability during surgery.Methods and analysis We will initiate our work using an existing annotated intraoperative database from the University of California Irvine, including clinical and high-fidelity waveform data. These data will be used for the training and development of the machine learning model (Carnegie Mellon University) that will then be tested on prospectively collected database (University of California Los Angeles). Simultaneously, we will use existing knowledge of haemodynamic instability patterns derived from our intensive care unit cohorts, medical information mart for intensive care II data, University of California Irvine data and animal studies to create smart alarms and graphical user interface for a clinical decision support. Using machine learning, we will extract a core dataset, which characterises the signatures of normal intraoperative variability, various haemodynamic instability aetiologies and variable responses to resuscitation. We will then employ clinician-driven iterative design to create a clinical decision support user interface, and evaluate its effect in simulated high-risk surgeries.Ethics and dissemination We will publish the results in a peer-reviewed publication and will present this work at professional conferences for the anaesthesiology and computer science communities. Patient-level data will be made available within 6 months after publication of the primary manuscript. The study has been approved by University of California, Los Angeles Institutional review board. (IRB #19–0 00 354).https://bmjopen.bmj.com/content/9/12/e031988.full |
| spellingShingle | Christine Lee Maxime Cannesson Ira Hofer Joseph Rinehart Kathirvel Subramaniam Pierre Baldi Artur Dubrawski Michael R Pinsky Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study BMJ Open |
| title | Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study |
| title_full | Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study |
| title_fullStr | Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study |
| title_full_unstemmed | Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study |
| title_short | Machine learning of physiological waveforms and electronic health record data to predict, diagnose and treat haemodynamic instability in surgical patients: protocol for a retrospective study |
| title_sort | machine learning of physiological waveforms and electronic health record data to predict diagnose and treat haemodynamic instability in surgical patients protocol for a retrospective study |
| url | https://bmjopen.bmj.com/content/9/12/e031988.full |
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