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P5581GRACE and TIMI risk scores in hospitalized patients with ST-elevation acute myocardial infarction undergoing pharmaco invasive therapy: are they similar?

P.I.M. Moraes, A.M. Nicolau, C.M.R. Alves, A.H.P. Barbosa, I. Goncalves Jr, J.M.A. Sousa, J.M. Orlando, A.C. Moreno, L.P.M. Machado, H.C. Orellana, J.C. Nicolau, A.C. Carvalho
2017 European Heart Journal  
The long-term mortality also had the higher rates at lower PNI level and that had 6.4-times higher mortality rates (95% CI: 4.4-12.4) than higher PNI level.  ...  Latest on STEMI 1199 had 7.9-times higher mortality rates (95% CI: 5.0-15.8) than higher PNI level.  ...  P5582 | BEDSIDE Simple risk prediction model to assess hospital mortality in Chinese patients with ST elevation myocardial infarction based on a machine learning approach: from China acute myocardial infarction  ... 
doi:10.1093/eurheartj/ehx493.p5581 fatcat:mtg5dzledfgidhdvzh2zq3muwy

P5579A novel and useful predictive indicator of prognosis in ST segment elevation myocardial infarction; prognostic nutritional index

M. Keskin, M.I. Hayiroglu, T. Keskin, A. Kaya, M.A. Tatlisu, S. Altay, A.O. Uzun, E.B. Borklu, T.S. Guvenc, I.I. Avci, O. Kozan
2017 European Heart Journal  
Objective: We sought to assess the effect of hemoglobin A1c (HbA1c) on the outcomes of primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI).  ...  Background: Myocardial deformation analysis allows prediction of prognosis in patients with an acute myocardial infarction (AMI) as well as in patients with a chronic coronary artery disease (CAD).  ...  P5582 | BEDSIDE Simple risk prediction model to assess hospital mortality in Chinese patients with ST elevation myocardial infarction based on a machine learning approach: from China acute myocardial infarction  ... 
doi:10.1093/eurheartj/ehx493.p5579 fatcat:7h57hqyloze7rdgsmicfpgojqi

Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction

Joon-myoung Kwon, Ki-Hyun Jeon, Hyue Mee Kim, Min Jeong Kim, Sungmin Lim, Kyung-Hee Kim, Pil Sang Song, Jinsik Park, Rak Kyeong Choi, Byung-Hee Oh, Pablo Salinas
2019 PLoS ONE  
Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations.  ...  This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI).  ...  [16] A previous study attempted to predict a 30-day mortality after ST-elevation myocardial infarction using conventional machine-learning methods including LR and RF and confirmed that RF performed  ... 
doi:10.1371/journal.pone.0224502 pmid:31671144 pmcid:PMC6822714 fatcat:3k3ilaagxbhslcghy7luzl3oxe

Optimized Machine Learning Models to Predict In-Hospital Mortality for Patients with ST-Segment Elevation Myocardial Infarction

Jia Zhao, Pengyu Zhao, Chunjie Li, Yonghong Hou
2021 Therapeutics and Clinical Risk Management  
This study aimed to optimize machine learning (ML) models for predicting in-hospital mortality in patients with ST-segment elevation acute myocardial infarction (STEMI).  ...  For models trained with the simplified set, the SVM achieved 83.48% G-mean, which was comparable to the models trained using the full set.  ...  Besides, we thank professor Li Li for her consultation on data collection. Disclosure The authors report no conflicts of interest in this work.  ... 
doi:10.2147/tcrm.s321799 pmid:34511920 pmcid:PMC8427294 fatcat:rm2xlinrznhunnc4bwof5axelq

Data mining approach for in-hospital treatment outcome in patients with acute coronary syndrome

Miroslava Sladojevic, Milenko Cankovic, Snezana Cemerlic, Bojan Mihajlovic, Filip Adjic, Milana Jarakovic
2015 Medicinski pregled  
The best prediction results were achieved using Alternating Decision Tree classifier, which was able to predict in-hospital mortality with 89% accuracy, and preserved good performance on validation cohort  ...  This study was aimed at developing an outcome prediction model for patients with acute coronary syndrome submitted to percutaneus coronary intervention using data mining approach.  ...  All patients underwent an invasive strategy {primary PCI for STEMI (ST elevation myocardial infarc-tion)/urgent PCI for NSTEMI (non-ST elevation myocardial infarction) and UA}, within two hours upon admission  ... 
doi:10.2298/mpns1506157s pmid:26234022 fatcat:byz2kvih65fthn3pcwjwwbntuy

Machine Learning to Predict the 1-Year Mortality Rate After Acute Anterior Myocardial Infarction in Chinese Patients

Yi-ming Li, Li-cheng Jiang, Jing-jing He, Kai-yu Jia, Yong Peng, Mao Chen
2020 Therapeutics and Clinical Risk Management  
In this study, we sought to evaluate the performance of different machine learning models in predicting the 1-year mortality rate of anterior ST-segment elevation myocardial infarction (STEMI) patients  ...  A formal risk assessment for identifying high-risk patients is essential in clinical practice and promoted in guidelines for the management of anterior acute myocardial infarction.  ...  Zhuo-lun Li from Department of Computer Science and Engineering, Tandon School of Engineering, New York University, New York, US, for providing the assistance of statistics and debugging in Python work  ... 
doi:10.2147/tcrm.s236498 pmid:32021220 pmcid:PMC6957091 fatcat:v5bu6y5jujbbfjwho445i3ip3i

Risk Scores in Acute Coronary Syndrome: Current Applications and Future Perspectives

Pedro G. M. de Barros e Silva, Renato D. Lopes
2022 International Journal of Cardiovascular Sciences  
below 1%), until patients with ST-elevation myocardial infarction (STEMI) and cardiogenic shock (30-day mortality around 50%).  ...  Multivariable prediction models have been developed to classify short-term and long-term risk of these patients (Table 1 ).  ...  ); also validated in patients with chest pain (with a lower Main outcomes predicted by the GRACE score are in-hospital mortality and six-month mortality + myocardial infarction performance compared to  ... 
doi:10.36660/ijcs.20220006 fatcat:ebabn6zwkvcuhcc4y4resuzody

Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods

Yong Li, Michael Spartalis
2022 Cardiology Research and Practice  
Preventing in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is a crucial step. Objectives.  ...  The objective of our research was to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods. Methods.  ...  Xiang Li used the machine learning method to make a prediction model of in-hospital mortality for STEMI patients [6] .  ... 
doi:10.1155/2022/8758617 fatcat:34iyhveatzcqrery2r7ut4kw4m

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Firdaus Aziz, Sorayya Malek, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff, Wan Azman Wan Ahmad, Rosli Mohd Ali, Kien Ting Liu, Gunavathy Selvaraj, Sazzli Kasim, Yoshihiro Fukumoto
2021 PLoS ONE  
Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score.  ...  Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.  ...  Acknowledgments We thank National Heart Association Malaysia for providing us with the data for this study. Author Contributions  ... 
doi:10.1371/journal.pone.0254894 pmid:34339432 pmcid:PMC8328310 fatcat:35r5ftd7gfd4lnmowuq6lty3nq

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Woojoo Lee, Joongyub Lee, Seoung-Il Woo, Seong Huan Choi, Jang-Whan Bae, Seungpil Jung, Myung Ho Jeong, Won Kyung Lee
2021 Scientific Reports  
The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs.  ...  Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs  ...  AMI, Acute Myocardial Infarction; STEMI, ST-segment elevation Myocardial Infarction; NSTEMI, Non-ST-segment elevation Myocardial Infarction.  ... 
doi:10.1038/s41598-021-92362-1 pmid:34145358 fatcat:lijxgh3odfec3obab76swnx5aa

Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry [article]

Rohan Khera, Julian Haimovich, Nate Hurley, Robert McNamara, John A Spertus, Nihar Desai, Frederick A Masoudi, Chenxi Huang, Sharon-Lise Normand, Bobak Jack Mortazavi, Harlan M Krumholz
2019 bioRxiv   pre-print
We developed and validated three machine learning models to predict in-hospital mortality and compared the performance characteristics with a logistic regression model.  ...  Conclusions: Machine-learning methods improved the prediction of in-hospital mortality for AMI compared with logistic regression.  ...  METHODS Chest pain-MI Registry The CP-MI Registry is a voluntary registry that collects data from participating hospitals on patients admitted with AMI, defined as either ST-elevation myocardial infarction  ... 
doi:10.1101/540369 fatcat:4psxwuywv5du3fj3bahtmuqzym

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

Syed Waseem Abbas Sherazi, Jang-Whan Bae, Jong Yun Lee, Saurav Chatterjee
2021 PLoS ONE  
It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (  ...  This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.  ...  Acknowledgments The authors would like to thank to Korea Acute Myocardial Infarction Registry (KAMIR); a nationwide, multicenter data collection registry; to provide us multicenter data for our experiments  ... 
doi:10.1371/journal.pone.0249338 pmid:34115750 fatcat:neze6q2ulzgxtngvv4ahgxrk4a

The relationship between preinfarction angina and serum sphingosine 1 phosphate levels

E. Kiziltunc, A. Abaci, S. Ozkan, Y. Alsancak, S. Unlu, E. S. Simsek, S. Elbeg, M. Cemri
2013 European Heart Journal  
innovative approach of using Data Mining techniques for developing an in-hospital mortality prediction model for patients presented with ST-segment elevation myocardial infarction (STEMI) submitted to  ...  We aimed to investigate if EAT thickness might be used to predict acute myocardial infarction.  ... 
doi:10.1093/eurheartj/eht307.p477 fatcat:y7k6ncultrdzdjjmg2pbwu4hu4

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

Sandeep Chandra Bollepalli, Ashish Kumar Sahani, Naved Aslam, Bishav Mohan, Kanchan Kulkarni, Abhishek Goyal, Bhupinder Singh, Gurbhej Singh, Ankit Mittal, Rohit Tandon, Shibba Takkar Chhabra, Gurpreet S. Wander (+1 others)
2022 Diagnostics  
In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method.  ...  Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI):  ...  In the present work, we used a machine learning model to predict in-hospital mortality, heart failure, ST-segment elevation myocardial infarction (STEMI), pulmonary embolism, and duration of stay using  ... 
doi:10.3390/diagnostics12020241 pmid:35204333 pmcid:PMC8871182 fatcat:jpoyeyt67rfkdbxsr5mw6c5nc4

Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers

Muhammad Hazrani Abdul Halim, Yumn Suhaylah Yusoff, Mazlynda Md Yusuf
2018 International Journal of Engineering & Technology  
The dataset used for this study is provided by National Cardiovascular Disease Database (NCVD) which consist of 2840 MI patients from hospitals in Malaysia.  ...  This paper study the ability of k-Nearest Neighbors (kNN) and Naïve Bayes algorithms to predict the 30-day mortality of MI patients, using.  ...  The authors would also like to express their gratitude to National Cardiovascular Disease Database (NCVD) for providing the data used in this research.  ... 
doi:10.14419/ijet.v7i4.15.21360 fatcat:t3klh5ko6vcrpookqf45usr7mi
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