| Reference code: | PT/FB/BL-2014-388.07 |
| Location: | BF-GMS
|
Title:
| An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data
|
| Publication year: | 2019
|
URL:
| https://doi.org/10.1016/j.bja.2019.07.030
|
| Abstract/Results: | ABSTRACT:
Background
Rapid, preoperative identification of patients with the 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 utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart.
Methods
We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features.
Results
Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955).
Conclusions
This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.
|
| Accessibility: | Document exists in file
|
Language:
| eng
|
Author:
| Hill, B. L.
|
Secondary author(s):
| Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., Maoz, U., Mahajan, A., Sankararaman, S., Hofer, I., Halperin, E.
|
Document type:
| Article
|
Number of reproductions:
| 1
|
Percentiles:
| 4
|
Reference:
| Hill, B. L., Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., Maoz, U., Mahajan, A., Sankararaman, S., Hofer, I., & Halperin, E. (2019). An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data. British Journal of Anaesthesia, 123(6), 877–886. https://doi.org/10.1016/j.bja.2019.07.030
|
| 2-year Impact Factor: | 6.880|2019
|
| Times cited: | 35|2024-02-12
|
| Indexed document: | Yes
|
| Quartile: | Q1
|
| Keywords: | Electronic health record / Hospital mortality / Machine learning / Perioperative outcome / Risk assessment
|
An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data |