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Cited by 614Hemodynamic Monitoring and TherapyNon-Invasive Vital Sign MonitoringArtificial Intelligence in Healthcare

Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis

Hatib, Feras, Jian, Zhongping, Buddi, Sai, Lee, Christine, Settels, Jos, Sibert, Karen, Rinehart, Joseph, Cannesson, Maxime·2018·Anesthesiology
DOI 10.1097/aln.0000000000002300
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Abstract

Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New: 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.

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Crossref · 10.1097/aln.0000000000002300

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  • The Hypotension Prediction Index is an algorithm created to aid in the early detection of intraoperative hypotension, defined as mean arterial pressure <65 mmHg for non-cardiac surgeries.

    “The Hypotension Prediction Index (HPI; Edwards Lifesciences, Irvine, CA) is an algorithm created to aid in the early detection of intraoperative hypotension, defined as mean arterial pressure <65 mmHg for non-cardiac surgeries (41, 43, 44).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [41]

  • The Hypotension Prediction Index was developed using an ML, logistic regression-based model analyzing components of the arterial waveform.

    “It was developed using an ML, logistic regression-based model analyzing components of the arterial waveform (41, 43, 44).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [41]

  • Multivariate regression-based models or AI-based early-warning systems have the potential to detect subtle trends in physiologic parameters over time to provide precision and reliability.

    “Multivariate regression-based models or AI-based early-warning systems have the potential to detect subtle trends in physiologic parameters over time to provide precision and reliability (38–41).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [41]

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Authors
Hatib, F., Jian, Z., Buddi, S., Lee, C., Settels, J., Sibert, K., Rinehart, J., & Cannesson, M.
Affiliations
University of California, Irvine (US)
Corresponding author
Cannesson, Maxime
ORCID
Venue
Anesthesiology
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2018-10
Volume
129
Issue
4
Pages
663-674
ISSN
0003-3022 (ISSN-L)
DOI
10.1097/aln.0000000000002300
OpenAlex
W2808701318
Cited by
614
References
34
Citations by year
2026: 82 · 2025: 98 · 2024: 112 · 2023: 89 · 2022: 73 · 2021: 51 · 2020: 64 · 2019: 39 · 2018: 5
FWCI
41.31
Type
journal article
Language
en
Open access
closed
Locations
  • Anesthesiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institute of Nursing Research (R01NR013912)
  • National Institute of General Medical Sciences (R01GM117622)
Topics
  • Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
hypotension prediction, arterial pressure waveform, machine learning, early warning algorithm, surgical patients, hemodynamic monitoring, mean arterial pressure
MeSH terms
Machine Learning, Adult, Aged, Aged, 80 and over, Algorithms, Female, Humans, Hypotension, Male, Middle Aged, Wavelet Analysis, Arterial Pressure

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