Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Preoperative predictions of in-hospital mortality using electronic medical record data

Brian Hill, Robert Brown, Eilon Gabel, Christine Lee, Maxime Cannesson, Loes Olde Loohuis, Ruth Johnson, Brandon Jew, Uri Maoz, Aman Mahajan, Sriram Sankararaman, Ira Hofer, Eran Halperin
doi: https://doi.org/10.1101/329813
Brian Hill
Department of Computer Science, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert Brown
Department of Computer Science, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eilon Gabel
Dept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christine Lee
Dept. of Anesthesiology and Perioperative Care, University of California, Irvine, CA, USADepartment of Computer Science, University of California, Irvine, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Maxime Cannesson
Dept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Loes Olde Loohuis
Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ruth Johnson
Department of Computer Science, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brandon Jew
Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Uri Maoz
Dept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USACrean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USASchmid College of Science and Technology, Chapman University, Orange, CA, USAInstitute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USAAnderson School of Management, University of California, Los Angeles, USANeuroscience, California Institute of Technology, Pasadena, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aman Mahajan
Dept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sriram Sankararaman
Department of Computer Science, University of California, Los Angeles, CA, USADepartment of Human Genetics, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ira Hofer
Dept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ihofer@mednet.ucla.edu
Eran Halperin
Department of Computer Science, University of California, Los Angeles, CA, USADept. of Anesthesiology and Perioperative Medicine, University of California, Los Angeles, CA, USADepartment of Human Genetics, University of California, Los Angeles, CA, USADepartment of Biomathematics, University of California, Los Angeles, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Background Predicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient’s medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores.

Methods Data from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC).

Results We found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time.

Conclusions Features easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time.

Author summary Rapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 25, 2018.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Preoperative predictions of in-hospital mortality using electronic medical record data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
Preoperative predictions of in-hospital mortality using electronic medical record data
Brian Hill, Robert Brown, Eilon Gabel, Christine Lee, Maxime Cannesson, Loes Olde Loohuis, Ruth Johnson, Brandon Jew, Uri Maoz, Aman Mahajan, Sriram Sankararaman, Ira Hofer, Eran Halperin
bioRxiv 329813; doi: https://doi.org/10.1101/329813
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Preoperative predictions of in-hospital mortality using electronic medical record data
Brian Hill, Robert Brown, Eilon Gabel, Christine Lee, Maxime Cannesson, Loes Olde Loohuis, Ruth Johnson, Brandon Jew, Uri Maoz, Aman Mahajan, Sriram Sankararaman, Ira Hofer, Eran Halperin
bioRxiv 329813; doi: https://doi.org/10.1101/329813

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (1524)
  • Biochemistry (2479)
  • Bioengineering (1731)
  • Bioinformatics (9670)
  • Biophysics (3896)
  • Cancer Biology (2968)
  • Cell Biology (4189)
  • Clinical Trials (135)
  • Developmental Biology (2624)
  • Ecology (4098)
  • Epidemiology (2031)
  • Evolutionary Biology (6894)
  • Genetics (5205)
  • Genomics (6497)
  • Immunology (2183)
  • Microbiology (6937)
  • Molecular Biology (2751)
  • Neuroscience (17261)
  • Paleontology (126)
  • Pathology (425)
  • Pharmacology and Toxicology (705)
  • Physiology (1056)
  • Plant Biology (2488)
  • Scientific Communication and Education (643)
  • Synthetic Biology (831)
  • Systems Biology (2687)
  • Zoology (429)