当前位置： SCI文献检索 > BRITISH JOURNAL OF ANAESTHESIA期刊下所有文献 > An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

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.

journal_name

Br J Anaesth

journal_title

British journal of anaesthesia

authors

Hill BL,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

doi

10.1016/j.bja.2019.07.030

subject

Has Abstract

pub_date

2019-12-01 00:00:00

pages

877-886

issue

eissn

0007-0912

issn

1471-6771

pii

S0007-0912(19)30646-4

journal_volume

123

pub_type

杂志文章

在线工具

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.