Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Shin, Seo Jeong; Park, Jungchan; Lee, Seung Hwa; Yang, Kyung‐Soo; Park, Rae Woong

doi:10.2196/32771

Cited by 8 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We applied machine learning techniques with the XGB algorithm, known as the best performing algorithm 14 . In our previous study, we compared performances of various machine learning algorithms for prediction of patients with mortality after MINS, and XGB was shown to be the best performing algorithm 15 .…”

Section: Discussionmentioning

confidence: 99%

“…The median value of preoperative cTn level was 6 ng/L (IQR [6][7][8][9][10][11]. The median values of postoperative cTn were 7 ng/L (IQR [6][7][8][9][10][11][12][13][14][15] in patients without MINS and 11 ng/L (IQR 6-34) in those with MINS. The median period to peak cTn level was 0.9 days after surgery, and MINS was detected within 48 h after surgery in 77.9% (1168/1499) of MINS patients.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Prediction model for myocardial injury after non-cardiac surgery using machine learning

Park

Shin

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Myocardial injury after non-cardiac surgery (MINS) is strongly associated with postoperative outcomes. We developed a prediction model for MINS and have provided it online. Between January 2010 and June 2019, a total of 6811 patients underwent non-cardiac surgery with normal preoperative level of cardiac troponin (cTn). We used machine learning techniques with an extreme gradient boosting algorithm to evaluate the effects of variables on MINS development. We generated two prediction models based on the top 12 and 6 variables. MINS was observed in 1499 (22.0%) patients. The top 12 variables in descending order according to the effects on MINS are preoperative cTn level, intraoperative inotropic drug infusion, operation duration, emergency operation, operation type, age, high-risk surgery, body mass index, chronic kidney disease, coronary artery disease, intraoperative red blood cell transfusion, and current alcoholic use. The prediction models are available at https://sjshin.shinyapps.io/mins_occur_prediction/. The estimated thresholds were 0.47 in 12-variable models and 0.53 in 6-variable models. The areas under the receiver operating characteristic curves are 0.78 (95% confidence interval [CI] 0.77–0.78) and 0.77 (95% CI 0.77–0.78), respectively, with an accuracy of 0.97 for both models. Using machine learning techniques, we demonstrated prediction models for MINS. These models require further verification in other populations.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Prediction model for myocardial injury after non-cardiac surgery using machine learning

Park

Shin

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Major elective surgery: Mortality was assessed postoperatively 27–36,100 in the surgical intensive care unit 35 or in the hospital. 28,29,32,36,100 One study predicted 30-day mortality risk related to myocardial injury in noncardiac surgery patients, 37 while another developed a natural language processing model using deep learning to analyze medical records and obtain diagnoses directly from notes written by a physician. 27–35,38,100…”

Section: Resultsmentioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

View full text Add to dashboard Cite

Background The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

show abstract

“…Machine learning with gradient boosting decision tree, which was used in this study, has been employed in previous studies and has shown high prediction performance (23) , (25) , (26) , (27) .…”

Section: Discussionmentioning

confidence: 99%

Short-term All-cause In-hospital Mortality Prediction by Machine Learning Using Numeric Laboratory Results

2023

JMA J

View full text Add to dashboard Cite

Introduction: A critical value (or panic value) is a laboratory test result that significantly deviates from the normal value and represents a potentially life-threatening condition requiring immediate action. Although notification of critical values by critical value list (CVL) is a well-established method, their contribution to mortality prediction is unclear. Methods: A total of 335,430 clinical laboratory results from 92,673 patients from July 2018 to December 2019 were used. Data in the first 12 months were divided into two datasets at a ratio of 70:30, and a 7-day mortality prediction model by machine learning (eXtreme Gradient Boosting [XGB] decision tree) was created using stratified random undersampling data of the 70% dataset. Mortality predictions by the CVL and XGB model were validated using the remaining 30% of the data, as well as different 6-month datasets from July to December 2019. Results: The true results which were the sum of correct predictions by the XGB model and CVL using the remaining 30% data were 61,535 and 61,024 tests, and the false results which were the sum of incorrect predictions were 5,492 and 6,003, respectively. Furthermore, the true results with the different datasets were 105,956 and 102,061 tests, and the false results were 6,052 and 9,947, respectively. The XGB model was significantly better than CVL ( p < 0.001) in both datasets. The receiver operating characteristic-area under the curve values for the 30% and validation data by XGB were 0.9807 and 0.9646, respectively, which were significantly higher than those by CVL (0.7549 and 0.7172, respectively). Conclusions: Mortality prediction within 7 days by machine learning using numeric laboratory results was significantly better than that by conventional CVL. The results indicate that machine learning enables timely notification to healthcare providers and may be safer than prediction by conventional CVL.

show abstract

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Cited by 8 publications

References 20 publications

Prediction model for myocardial injury after non-cardiac surgery using machine learning

Prediction model for myocardial injury after non-cardiac surgery using machine learning

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Short-term All-cause In-hospital Mortality Prediction by Machine Learning Using Numeric Laboratory Results

Contact Info

Product

Resources

About