Newly Published
Perioperative Medicine  |   June 2018
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis
Author Notes
  • From Edwards Lifesciences Critical Care, Irvine, California (F.H., Z.J., S.B., C.L., J.S.); the Department of Anesthesiology and Perioperative Care, School of Medicine (C.L., J.R., M.C.), Department of Computer Sciences (C.L.), and Department of Biomedical Engineering (C.L., M.C.), University of California, Irving, California; and the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California (K.S., M.C.).
  • Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).
    Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).×
  • Submitted for publication July 25, 2017. Accepted for publication April 24, 2018.
    Submitted for publication July 25, 2017. Accepted for publication April 24, 2018.×
  • Acknowledgments: For technical advice and discussion, the authors thank Cecilia Canales, M.D., M.P.H. For data acquisition and collection, we thank Cecilia Canales, M.D., M.P.H., Joseph de Los Santos, B.S., Esther Bahn, B.S., Michael Calderon, B.A., and Michael Ma, B.S.
    Acknowledgments: For technical advice and discussion, the authors thank Cecilia Canales, M.D., M.P.H. For data acquisition and collection, we thank Cecilia Canales, M.D., M.P.H., Joseph de Los Santos, B.S., Esther Bahn, B.S., Michael Calderon, B.A., and Michael Ma, B.S.×
  • Research Support: Edwards Lifesciences (Irvine, California) sponsored the study. Drs. Rinehart and Cannesson and Ms. Lee received funding from Edwards Lifesciences to support extraction, deidentification, and transfer of waveforms for the study. Edwards Lifesciences was involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
    Research Support: Edwards Lifesciences (Irvine, California) sponsored the study. Drs. Rinehart and Cannesson and Ms. Lee received funding from Edwards Lifesciences to support extraction, deidentification, and transfer of waveforms for the study. Edwards Lifesciences was involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.×
  • Competing Interests: Dr. Hatib, Dr. Jian, Dr. Buddi, Ms. Lee, and Mr. Settels are Edwards Lifesciences (Irvine, California) employees. Drs. Rinehart and Cannesson are co-owners of U.S. patent serial No. 61/432,081, for a closed-loop fluid administration system based on the dynamic predictors of fluid responsiveness, which has been licensed to Edwards Lifesciences. Dr. Rinehart is a consultant for Edwards Lifesciences. Dr. Cannesson is a consultant for Edwards Lifesciences, Medtronic (Boulder, Colorado), and Masimo Corp. (Irvine, California). Dr. Rinehart has received research support from Edwards Lifesciences through his department. Dr. Cannesson has received research support from Edwards Lifesciences through his department and the National Institutes of Health (Bethesda, Maryland) grant Nos. R01 GM117622 and R01 NR013912. Drs. Hatib and Jian report a patent pending on processing high-fidelity arterial pressure waveform signals to predict hypotension.
    Competing Interests: Dr. Hatib, Dr. Jian, Dr. Buddi, Ms. Lee, and Mr. Settels are Edwards Lifesciences (Irvine, California) employees. Drs. Rinehart and Cannesson are co-owners of U.S. patent serial No. 61/432,081, for a closed-loop fluid administration system based on the dynamic predictors of fluid responsiveness, which has been licensed to Edwards Lifesciences. Dr. Rinehart is a consultant for Edwards Lifesciences. Dr. Cannesson is a consultant for Edwards Lifesciences, Medtronic (Boulder, Colorado), and Masimo Corp. (Irvine, California). Dr. Rinehart has received research support from Edwards Lifesciences through his department. Dr. Cannesson has received research support from Edwards Lifesciences through his department and the National Institutes of Health (Bethesda, Maryland) grant Nos. R01 GM117622 and R01 NR013912. Drs. Hatib and Jian report a patent pending on processing high-fidelity arterial pressure waveform signals to predict hypotension.×
  • Correspondence: Address correspondence to Dr. Cannesson: Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, California 90095. mcannesson@mednet.ucla.edu. Information on purchasing reprints may be found at www.anesthesiology.org or on the masthead page at the beginning of this issue. Anesthesiology’s articles are made freely accessible to all readers, for personal use only, 6 months from the cover date of the issue.
Article Information
Perioperative Medicine / Cardiovascular Anesthesia
Perioperative Medicine   |   June 2018
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis
Anesthesiology Newly Published on June 11, 2018. doi:10.1097/ALN.0000000000002300
Anesthesiology Newly Published on June 11, 2018. doi:10.1097/ALN.0000000000002300
Abstract

Background: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors’ goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility.

Methods: The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients’ records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients’ records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm’s success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg.

Results: Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]).

Conclusions: The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients’ records.