Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Considering that most patients with low or no significant risk factors can safely undergo noncardiac surgery without additional cardiac evaluation, and given the excessive evaluations often performed in patients undergoing intermediate or higher risk noncardiac surgeries, practical preoperative risk...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of medical Internet research Jg. 27; S. e66366
Hauptverfasser: Kwun, Ju-Seung, Ahn, Houng-Beom, Kang, Si-Hyuck, Yoo, Sooyoung, Kim, Seok, Song, Wongeun, Hyun, Junho, Oh, Ji Seon, Baek, Gakyoung, Suh, Jung-Won
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Canada Gunther Eysenbach MD MPH, Associate Professor 09.04.2025
JMIR Publications
Schlagworte:
Accuracy
Aged
Algorithms
Archives & records
Cardiac arrest
Cardiac surgery
Cerebrovascular Disorders
Clinical medicine
Clinical outcomes
Computerized medical records
Creatinine
Data
Datasets
Disease
Electronic health records
Emergency medical care
Female
Generalizability
Health care expenditures
Health information
Health records
Health services
Heart attacks
Heart failure
Hospitals
Humans
Indexes
Informatics
Laboratories
Learning
Machine Learning
Male
Medical records
Mortality
Myocardial infarction
Original Paper
Outpatient services
Patients
Perioperative care
Postoperative Complications
Prediction models
Retrospective Studies
Risk Assessment
Risk Factors
Risk reduction
Stroke
Surgeons
Surgery
Surgical Procedures, Operative - adverse effects
Treatment outcomes
Vascular surgery
perioperative risk evaluation
cerebrovascular
noncardiac surgeries
clinical practice
electronic health records
machine learning
EHR
noncardiac surgery
common data model
perioperative
prediction models
predictive modeling
risk
ML
ISSN:1438-8871, 1439-4456, 1438-8871
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Considering that most patients with low or no significant risk factors can safely undergo noncardiac surgery without additional cardiac evaluation, and given the excessive evaluations often performed in patients undergoing intermediate or higher risk noncardiac surgeries, practical preoperative risk assessment tools are essential to reduce unnecessary delays for urgent outpatient services and manage medical costs more efficiently. This study aimed to use the Observational Medical Outcomes Partnership Common Data Model to develop a predictive model by applying machine learning algorithms that can effectively predict major adverse cardiac and cerebrovascular events (MACCE) in patients undergoing noncardiac surgery. This retrospective observational network study collected data by converting electronic health records into a standardized Observational Medical Outcomes Partnership Common Data Model format. The study was conducted in 2 tertiary hospitals. Data included demographic information, diagnoses, laboratory results, medications, surgical types, and clinical outcomes. A total of 46,225 patients were recruited from Seoul National University Bundang Hospital and 396,424 from Asan Medical Center. We selected patients aged 65 years and older undergoing noncardiac surgeries, excluding cardiac or emergency surgeries, and those with less than 30 days of observation. Using these observational health care data, we developed machine learning-based prediction models using the observational health data sciences and informatics open-source patient-level prediction package in R (version 4.1.0; R Foundation for Statistical Computing). A total of 5 machine learning algorithms, including random forest, were developed and validated internally and externally, with performance assessed through the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve, and calibration plots. All machine learning prediction models surpassed the Revised Cardiac Risk Index in MACCE prediction performance (AUROC=0.704). Random forest showed the best results, achieving AUROC values of 0.897 (95% CI 0.883-0.911) internally and 0.817 (95% CI 0.815-0.819) externally, with an area under the precision-recall curve of 0.095. Among 46,225 patients of the Seoul National University Bundang Hospital, MACCE occurred in 4.9% (2256/46,225), including myocardial infarction (907/46,225, 2%) and stroke (799/46,225, 1.7%), while in-hospital mortality was 0.9% (419/46,225). For Asan Medical Center, 6.3% (24,861/396,424) of patients experienced MACCE, with 1.5% (6017/396,424) stroke and 3% (11,875/396,424) in-hospital mortality. Furthermore, the significance of predictors linked to previous diagnoses and laboratory measurements underscored their critical role in effectively predicting perioperative risk. Our prediction models outperformed the widely used Revised Cardiac Risk Index in predicting MACCE within 30 days after noncardiac surgery, demonstrating superior calibration and generalizability across institutions. Its use can optimize preoperative evaluations, minimize unnecessary testing, and streamline perioperative care, significantly improving patient outcomes and resource use. We anticipate that applying this model to actual electronic health records will benefit clinical practice.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Undefined-3
ISSN:1438-8871
1439-4456
1438-8871
DOI:10.2196/66366