scholarly journals Comparison of Traditional Risk Factors, Angiographic Findings, and In-Hospital Mortality between Smoking and Nonsmoking Turkish Men and Women With Acute Myocardial Infarction

2010 ◽  
Vol 33 (6) ◽  
pp. E49-E54 ◽  
Author(s):  
Nazif Aygul ◽  
Kurtulus Ozdemir ◽  
Adnan Abaci ◽  
Meryem Ulku Aygul ◽  
Mehmet Akif Duzenli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Mortality ◽  
Men And Women ◽  
Traditional Risk Factors ◽  
Angiographic Findings

Coronary Angiographic Findings and Risk Factors in Acute Myocardial Infarction Patients before after Age 50

1994 ◽  
Vol 24 (6) ◽  
pp. 809
Author(s):  
Hui Nam Park ◽  
Sang Chil Lee ◽  
Chang Kyu Park ◽  
Young Hoon Kim ◽  
Wan Joo Shim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Angiographic Findings

P1755Plasma proteomics in healthy individuals predict myocardial infarction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Hoogeveen ◽  
J P Belo Pereira ◽  
V Zampoleri ◽  
M J Bom ◽  
W Koenig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Plasma Proteins ◽  
Risk Model ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Traditional Risk Factors

Abstract Background Currently used models to predict cardiovascular event risk have limited value. It has been shown repetitively that the addition of single biomarkers has modest impact. Recently we observed that a model consisting of a larger array of plasma proteins performed very well in predicting the presence of vulnerable plaques in primary prevention patients. However, the validation of this protein panel in predicting cardiovascular outcomes remains to be established. Purpose This study investigated the ability of a 384 preselected protein biomarkers to predict acute myocardial infarction, using state-of-the-art machine learning techniques. Secondly, we compared the performance of this multi-protein risk model to traditional risk engines. Methods We selected 822 subjects from the EPIC-Norfolk prospective cohort study, of whom 411 suffered a myocardial infarction during follow-up (median 15 years) compared to 411 controls who remained event-free (median follow-up 20 years). The 384 proteins were measured using proximity extension assay technology. Machine learning algorithms (random forests) were used for the prediction of acute myocardial infarction (ICD code I21–22). Performance of the model was tested against and on top of traditional risk factors for cardiovascular disease (refit Framingham). All performance measurements were averaged over several stability selection routines. Results Prediction of myocardial infarction using a machine-learning model consisting of 50 plasma proteins resulted in a ROC AUC of 0.74±0.14, in comparison to 0.69±0.17 using traditional risk factors (refit Framingham. Combining the proteins and refit Framingham resulted in a ROC AUC of 0.74±0.15. Focussing on events occurring within 3 years after baseline blood withdrawal, the ROC AUC increased to 0.80±0.09 using 50 plasma proteins, as opposed to 0.67±0.22 using refit Framingham (figure). Combining the protein model with refit Framingham resulted in a ROC AUC of 0.82±0.11 for these events. Diagnostic performance events <3yrs Conclusion High-throughput proteomics outperforms traditional risk factors in prediction of acute myocardial infarction. Prediction of myocardial infarction occurring within 3 years after inclusion showed highest performance. Availability of affordable proteomic approaches and developed machine learning pave the path for clinical implementation of these models in cardiovascular risk prediction. Acknowledgement/Funding This study was funded by an ERA-CVD grant (JTC2017) and EU Horizon 2020 grant (REPROGRAM, 667837)

Impact of history of diabetes mellitus on hospital mortality in men and women with first acute myocardial infarction

2000 ◽  
Vol 85 (12) ◽  
pp. 1486-1489 ◽  
Author(s):  
Viola Vaccarino ◽  
Lori Parsons ◽  
Nathan R. Every ◽  
Hal V. Barron ◽  
Harlan M. Krumholz
Keyword(s):  
Diabetes Mellitus ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Mortality ◽  
Men And Women ◽  
History Of

scholarly journals Risk factors for in-hospital mortality in patients with acute myocardial infarction during the COVID-19 outbreak

2020 ◽  
Vol 73 (12) ◽  
pp. 985-993
Author(s):  
Jorge Solano-López ◽  
José Luis Zamorano ◽  
Ana Pardo Sanz ◽  
Ignacio Amat-Santos ◽  
Fernando Sarnago ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Mortality

scholarly journals Factors associated with acute myocardial infarction in older patients after hospitalization with community-acquired pneumonia: a cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Kang ◽  
Xiang-Yang Fang ◽  
Dong Wang ◽  
Xiao-Juan Wang
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Coronary Artery Disease ◽  
Acute Myocardial Infarction ◽  
Respiratory Failure ◽  
Hospital Mortality ◽  
Older Patients ◽  
Geriatric Patients ◽  
Community Acquired Pneumonia ◽  
Artery Disease

Abstract Background Community-acquired pneumonia (CAP) and acute myocardial infarction cardiovascular (AMI) are two important health issues in older patients. Little is known regarding characteristics of AMI in older patients hospitalized for CAP. Therefore, we investigated the prevalence, characteristics compared with younger patients, impact on clinical outcomes and risk factors of AMI during hospitalization for CAP in geriatric patients. Methods Eleven thousand nine adult inpatients consisted of 5111 patients≥65 years and 5898 patients< 65 years in respiratory ward diagnosed with CAP were retrospectively analyzed by electronic medical records. Results 159 (3.1%) older patients in respiratory ward experienced AMI during hospitalization for CAP. AMI were more frequently seen in patients≥65 years compared with patients< 65 years (3.1% vs. 1.0%). Patients≥65 years who experienced AMI during hospitalization for CAP had higher percentage of respiratory failure (P = 0.001), hypertension (P = 0.008), dyspnea (P = 0.046), blood urea nitrogen (BUN) ≥7 mmol/L (P < 0.001), serum sodium< 130 mmol/L (P = 0.005) and had higher in-hospital mortality compared to patients< 65 years (10.1% vs. 6.6%). AMI was associated with increased in-hospital mortality (odds ratio, OR, with 95% confidence interval: 1.49 [1.24–1.82]; P < 0.01). Respiratory failure (OR, 1.34 [1.15–1.54]; P < 0.01), preexisting coronary artery disease (OR, 1.31[1.07–1.59]; P = 0.02), diabetes (OR, 1.26 [1.11–1.42]; P = 0.02) and BUN (OR, 1.23 [1.01–1.49]; P = 0.04) were correlated with the occurrence of AMI in the older patients after hospitalization with CAP. Conclusions The incidence of AMI during CAP hospitalization in geriatric patients is notable and have an impact on in-hospital mortality. Respiratory failure, preexisting coronary artery disease, diabetes and BUN was associated with the occurrence of AMI in the older patients after hospitalization with CAP. Particular attention should be paid to older patients with risk factors for AMI.

scholarly journals Risks of Recurrent Cardiovascular Events and Mortality in 1-Year Survivors of Acute Myocardial Infarction Implanted with Newer-Generation Drug-Eluting Stents

2021 ◽  
Vol 10 (16) ◽  
pp. 3642
Author(s):  
Sungmin Lim ◽  
Eun Ho Choo ◽  
Ik Jun Choi ◽  
Kwan Yong Lee ◽  
Su Nam Lee ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Clinical Outcomes ◽  
Cardiovascular Events ◽  
Drug Eluting Stents ◽  
Index Procedure ◽  
Drug Eluting ◽  
Traditional Risk Factors

Current treatments for acute myocardial infarction (AMI) have dramatically improved clinical outcomes during the first year after AMI. Less is known, however, about the subsequent risks of recurrent cardiovascular events and mortality in patients who survive 1 year after AMI. The purpose of the present study is to evaluate long-term clinical outcomes in 1-year AMI survivors who were implanted with newer-generation drug-eluting stents (DESs) since 2010. The COREA-AMI (CardiOvascular Risk and idEntificAtion of potential high-risk population in AMI) registry consecutively enrolled AMI patients who underwent percutaneous coronary intervention (PCI), and patients who received newer-generation DESs since 2010 were analyzed. The primary endpoint was major adverse cardiovascular events (MACEs), and secondary endpoint was all-cause mortality. Of 6242 AMI patients, 5397 were alive 1 year after the index procedure. The cumulative incidence of MACEs and all-cause death 1 to 7 years after AMI were 28.4% (annually 4–6%) and 20.2% (annually 3–4%), respectively. Multivariate analysis showed that uncontrolled systolic blood pressure (SBP) and serum low-density lipoprotein cholesterol (LDL-C) concentration, as well as traditional risk factors, were associated with MACEs and all-cause death. Recurrent non-fatal myocardial infarction, ischemic stroke, and bleeding events within 1 year were significantly associated with all-cause death. The risks of adverse cardiovascular events and death remain high in AMI patients more than 1 year after the index PCI with newer-generation DESs. Traditional risk factors, uncontrolled SBP and LDL-C, and non-fatal adverse events within 1 year after the index procedure strongly influence long-term clinical outcomes.

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