Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes

Wu, Ting; Lin, Xiu Quan; Mu, Yan; Li, Hong; Guo, Yuan-Lin

doi:10.1002/clc.23541

Cited by 28 publications

(31 citation statements)

References 30 publications

(34 reference statements)

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“…They found that the decision tree showed higher classification results (AUC = 0.96) [ 32 ]. Moreover, Wu et al found that the XGB algorithm was the most accurate compared to the other seven algorithms and showed promising discrimination for detecting in-hospital cardiac arrest, reporting accuracy of 88.9%, sensitivity of 73%, and F1 score of 80% [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the adult population with HF, machine learning algorithms create risk scores estimating the likelihood of a heart failure diagnosis and the probability of outcomes such as all-cause mortality, cardiac death, and hospitalization [ 4 , 5 , 6 , 7 ]. MLTs are also increasingly used for hard outcome prediction in the clinical setting (e.g., in-hospital cardiac arrest) since they present several advantages over traditional methods and show promising performance and better power than existing prediction systems [ 8 , 9 ]. They help extricate complex relationships between covariates and outcome of interest, even though a low number of events have occurred with respect to the many variables to be tested.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

et al. 2021

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The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770–0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

et al. 2021

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“…We observed 26 studies that fit into this category [14,. Of these 26 studies, 11 (42%) used ML models [14,18,19,23,25,27,30,32,[34][35][36] and 3 (12%) used DL algorithms [20,31,38]. We observed that 12 studies incorporated both ML and DL models to analyze and validate different parameters [21,22,24,26,28,29,33,37,[39][40][41][42].…”

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

“…Random forest (RF) [14,21,23,[28][29][30]32,[35][36][37][39][40][41] and support vector machine (SVM) [18,22,24,26,28,34,[40][41][42] were the most used ML models observed in these studies, followed by decision tree (DT) [22,29,30,[40][41][42], logistic regression (LR) [28][29][30]40], Naive Bayes [19,28,29,41] [27,28], extreme gradient boosting [27,29], LogitBoost [21], AdaBoost [29], TreeBagger [34], and sequential feature selection [24]. The most used DL-based algorithm in the studies was k-nearest neighbors (KNN) [20,22,26,29,33,…”

Section: Analysis Of Variables and Parametersmentioning

confidence: 99%

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

Alamgir¹,

Mousa²,

Shah³

2021

JMIR Med Inform

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Background Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

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“…Recently, machine learning has emerged as an effective approach to integrate multiple quantitative variables to improve accuracy of incidence predictions in medicine, with the potential to dramatically improve health care delivery (22)(23)(24)(25)(26). Speci cally, in the research of anesthesiology and cardiac arrest, it has shown in very recent years ML is a promising method for a more comprehensive understanding of the risk factors and a supporting tool for care improvement (27)(28)(29)(30)(31).…”

Section: Introductionmentioning

confidence: 99%

A Retrospective Study of Mortality for Perioperative Cardiac Arrests Towards Personalized Treatment

Shang

Chu

Zheng

et al. 2022

Preprint

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Background Perioperative cardiac arrest (POCA) is associated with a high mortality rate. This work aims to study its prognostic factors for risk mitigation by means of care management and planning. Methods A database of 380,919 surgeries was reviewed and 150 POCAs were curated. The main outcome is the mortality prior to hospital discharge. Patient demographics, medical history, and clinical characteristics (anesthesia and surgery) were main features. Six ML algorithms were explored: LR, SVC, RF, GBM, AdaBoost, and VotingClassifier. The last one is an ensemble of the beginning five ones. k-fold cross-validation and bootstrapping minimized the prediction bias and variance, respectively. Explainers (SHAP and LIME) were used to interpret the predictions. Results The ensemble provided the most accurate and robust predictions (AUC=0.90 [95% CI, 0.78-0.98]) across various age groups. The risk factors were identified by order of importance. Surprisingly, the comorbidity of hypertension was found to have a protective effect on survival, which was reported by a recent study (Alnabelsi T. et. al. 2020) for the first time to our knowledge. Conclusions The validated ensemble classifier in aid of the explainers improved the predictive differentiation, deepening our understanding of POCA prognostication. It offers a holistic model-based approach for personalized anesthesia and surgical treatment.

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Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes

Cited by 28 publications

References 30 publications

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Artificial Intelligence in Predicting Cardiac Arrest: Scoping Review

A Retrospective Study of Mortality for Perioperative Cardiac Arrests Towards Personalized Treatment

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