Newly Published
Perioperative Medicine  |   February 2020
Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission
Author Notes
  • From Decisions, Operations, and Technology Management Area, Anderson School of Management (V.V.M., K.R.), and Department of Anesthesiology and Perioperative Medicine (E.G., I.H.), University of California Los Angeles, Los Angeles, California; Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania (A.M.).
  • 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).×
  • V.V.M. and E.G. contributed equally to this article.
    V.V.M. and E.G. contributed equally to this article.×
  • Submitted for publication August 5, 2018. Accepted for publication December 17, 2019.
    Submitted for publication August 5, 2018. Accepted for publication December 17, 2019.×
  • Correspondence: Address correspondence to Dr. Gabel: Anderson School of Management, University of California Los Angeles, 757 Westwood Plaza, Suite 3325 Los Angeles, California 90095. egabel@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 / Pharmacology / Technology / Equipment / Monitoring / Quality Improvement
Perioperative Medicine   |   February 2020
Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission
Anesthesiology Newly Published on February 7, 2020. doi:https://doi.org/10.1097/ALN.0000000000003140
Anesthesiology Newly Published on February 7, 2020. doi:https://doi.org/10.1097/ALN.0000000000003140
Abstract

Editor’s Perspective:

What We Already Know about This Topic:

  • Unplanned hospital readmissions are a focus of quality improvement, national benchmarking, and payment incentives in the United States

  • The accuracy of commonly used peer-reviewed readmission prediction algorithms at specific hospitals may be limited by hospital-specific factors

  • The potential value of novel machine learning techniques capable of incorporating hundreds of patient, process, and hospital attributes is unclear

What This Manuscript Tells Us That Is New:

  • Hospital-specific 30-day surgical readmission models using machine learning techniques provide clinically usable predictions when applied to future patients

  • A parsimonious approach limiting which data elements are considered performs as well as more comprehensive models

Background: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge.

Methods: Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features.

Results: Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge.

Conclusions: A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.