Newly Published
Perioperative Medicine  |   August 2018
Supervised Machine Learning Predictive Analytics for Prediction of Postinduction Hypotension
Author Notes
  • From the Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University Langone Health, New York, New York.
  • 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).×
  • This work was presented at the American Society of Anesthesiologists Annual Meeting in Boston, Massachusetts, on October 21, 2017, and October 24, 2017.
    This work was presented at the American Society of Anesthesiologists Annual Meeting in Boston, Massachusetts, on October 21, 2017, and October 24, 2017.×
  • Submitted for publication September 18, 2017. Accepted for publication June 20, 2018.
    Submitted for publication September 18, 2017. Accepted for publication June 20, 2018.×
  • Acknowledgments: The authors would like to thank the Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University Langone Health, New York, New York, for granting time to support this work.
    Acknowledgments: The authors would like to thank the Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University Langone Health, New York, New York, for granting time to support this work.×
  • Research Support: Support was provided solely from institutional and/or departmental sources.
    Research Support: Support was provided solely from institutional and/or departmental sources.×
  • Competing Interests: The authors declare no competing interests.
    Competing Interests: The authors declare no competing interests.×
  • Correspondence: Address correspondence to Dr. Kendale: Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU Langone Medical Center, 550 First Avenue, New York, New York, 10016. Samir.Kendale@nyumc.org. 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   |   August 2018
Supervised Machine Learning Predictive Analytics for Prediction of Postinduction Hypotension
Anesthesiology Newly Published on August 2, 2018. doi:10.1097/ALN.0000000000002374
Anesthesiology Newly Published on August 2, 2018. doi:10.1097/ALN.0000000000002374
Abstract

What We Already Know about This Topic:

  • The ability to predict postinduction hypotension remains limited and challenging due to the multitude of data elements that may be considered

  • Novel machine learning algorithms may offer a systematic approach to predict postinduction hypotension, but are understudied

What This Article Tells Us That Is New:

  • Among 13,323 patients undergoing a variety of surgical procedures, 8.9% experienced a mean arterial pressure less than 55 mmHg within 10 min of induction start

  • While some machine learning algorithms perform worse than logistic regression, several techniques may be superior

  • Gradient boosting machine, with tuning, demonstrates a receiver operating characteristic area under the curve of 0.76, a negative predictive value of 19%, and positive predictive value of 96%

Background: Hypotension is a risk factor for adverse perioperative outcomes. Machine learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine learning methods can provide prediction for the risk of postinduction hypotension

Methods: Data was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.

Results: Out of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).

Conclusions: The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.