Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction

Zhu, Xiaoli; Xie, Bojian; Chen, Yijun; Zeng, Hanqian; Hu, Jinxi

doi:10.1016/j.cca.2024.117776

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Studies examining ischemic coronary artery disease [ 11 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 ] use AI both in predicting the disease [ 180 , 181 , 191 ] and in its diagnosis [ 179 , 183 , 184 ] or prognosis [ 41 , 186 , 187 , 188 , 189 , 190 ]. Other studies use machine learning models in patients undergoing coronary artery bypass graft (CABG) surgery to create predictive models of the risk of continuous renal replacement therapy (CRRT) after surgery [ 267 ].…”

Section: Discussionmentioning

confidence: 99%

“…When it comes to ischemic heart disease, artificial intelligence can be useful both in the early diagnosis of ischemic heart disease [ 175 , 176 , 177 , 178 , 184 , 185 ] and in the prediction of complications after an acute ischemic event [ 186 , 187 , 188 , 189 , 190 ] and coronary artery disease [ 179 , 180 , 181 , 191 ]; AI can also be used in coronary artery disease prevention [ 11 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model

Zhang,

Yu,

Chen

et al. 2024

Computers in Biology and Medicine

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Development and Validation of a Nomogram Model for Predicting in-Hospital Mortality in non-Diabetic Patients with non-ST-Segment Elevation Acute Myocardial Infarction

Li,

Yao,

et al. 2024

Clin Appl Thromb Hemost

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Non-ST-segment elevation acute myocardial infarction (NSTEMI) is a life-threatening clinical emergency with a poor prognosis. However, there are no individualized nomogram models to identify patients at high risk of NSTEMI who may undergo death. The aim of this study was to develop a nomogram for in-hospital mortality in patients with NSTEMI to facilitate rapid risk stratification of patients. A total of 774 non-diabetic patients with NSTEMI were included in this study. Least Absolute Shrinkage and Selection Operator regression was used to initially screen potential predictors. Univariate and multivariate logistic regression (backward stepwise selection) analyses were performed to identify the optimal predictors for the prediction model. The corresponding nomogram was constructed based on those predictors. The receiver operating characteristic curve, GiViTI calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. The nomogram model consisting of six predictors: age (OR = 1.10; 95% CI: 1.05-1.15), blood urea nitrogen (OR = 1.06; 95% CI: 1.00-1.12), albumin (OR = 0.93; 95% CI: 0.87-1.00), triglyceride (OR = 1.41; 95% CI: 1.09-2.00), D-dimer (OR = 1.39; 95% CI: 1.06-1.80), and aspirin (OR = 0.16; 95% CI: 0.06-0.42). The nomogram had good discrimination (area under the curve (AUC) = 0.89, 95% CI: 0.84-0.94), calibration, and clinical usefulness. In this study, we developed a nomogram model to predict in-hospital mortality in patients with NSTEMI based on common clinical indicators. The proposed nomogram has good performance, allowing rapid risk stratification of patients with NSTEMI.

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Machine learning in the prediction of in-hospital mortality in patients with first acute myocardial infarction

Cited by 5 publications

References 28 publications

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model

Development and Validation of a Nomogram Model for Predicting in-Hospital Mortality in non-Diabetic Patients with non-ST-Segment Elevation Acute Myocardial Infarction

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