scholarly journals Relative value of clinical variables, bicycle ergometry, rest radionuclide ventriculography and 24 hour ambulatory electrocardiographic monitoring at discharge to predict 1 year survival after myocardial infarction

1986 ◽  
Vol 8 (1) ◽  
pp. 40-49 ◽  
Author(s):  
Paolo Fioretti ◽  
Ronald W. Brower ◽  
Maarten L. Simoons ◽  
Harald ten Katen ◽  
Anita Beelen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Radionuclide Ventriculography ◽  
Bicycle Ergometry ◽  
Clinical Variables ◽  
Electrocardiographic Monitoring ◽  
Relative Value

scholarly journals Association of atrial arrhythmias with thrombospondin-1 in patients with acute myocardial infarction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenkai Liao ◽  
Li Xu ◽  
Yuxia Pan ◽  
Jie Wei ◽  
Peijia Wang ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Heart Function ◽  
Coronary Intervention ◽  
Atrial Remodeling ◽  
Atrial Arrhythmias ◽  
Electrocardiographic Monitoring ◽  
Thrombospondin 1 ◽  
Independent Risk Factors ◽  
Hs Crp

Abstract Objectives Atrial remodeling is the main developmental cause of atrial arrhythmias (AA), which may induce atrial fibrillation, atrial flutter, atrial tachycardia, and frequent premature atrial beats in acute myocardial infarction (AMI) patients. Thrombospondin-1 (TSP-1) has been shown to play an important role in inflammatory and fibrotic processes, but its role in atrial arrhythmias is not well described. The purpose of this study was to investigate the role of TSP-1 in AMI patients with atrial arrhythmias. Methods A total of 219 patients with AMI who underwent percutaneous coronary intervention and with no previous arrhythmias were included. TSP-1 were analyzed in plasma samples. Patients were classified into 2 groups, namely, with and without AA during the acute phase of MI. Continuous electrocardiographic monitoring was used for AA diagnosis in hospital. Results Twenty-four patients developed AA. Patients with AA had higher TSP-1 levels (29.01 ± 25.87 μg/mL vs 18.36 ± 10.89 μg/mL, p < 0.001) than those without AA. AA patients also tended to be elderly (65.25 ± 9.98 years vs 57.47 ± 10.78 years, p < 0.001), had higher Hs-CRP (39.74 ± 43.50 mg/L vs 12.22 ± 19.25 mg/L, p < 0.001) and worse heart function. TSP-1 (OR 1.033; 95% CI 1.003–1.065, p = 0.034), Hs-CRP (OR 1.023; 95% CI 1.006–1.041, p = 0.008), age (OR 1.067; 95% CI 1.004–1.135, p = 0.038) and LVDd (OR 1.142; 95% CI 1.018–1.282, p = 0.024) emerged as independent risk factors for AA in AMI patients. Conclusion TSP-1 is a potential novel indicator of atrial arrhythmias during AMI. Graphical abstract

Abstract P041: Cumulative Exposure to Air Pollution, Socioeconomic Status and Post-Myocardial Infarction Outcomes in Central Israel. A Cohort Study

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Noa Molshatzki ◽  
David Broday ◽  
Silvia Koton ◽  
Yuval . ◽  
Vicki Myers ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Socioeconomic Status ◽  
Cardiac Death ◽  
Fine Particles ◽  
Adverse Outcomes ◽  
Cumulative Exposure ◽  
Exposure Misclassification ◽  
Cox Models ◽  
Clinical Variables

Objective: In a community-based cohort of myocardial infarction (MI) survivors, we examined whether cumulative residential exposure to air pollutants constitutes a risk factor for adverse outcomes. Methods: Patients aged ≤65 years (n=1,428), admitted with first MI in 1992-1993 to the 8 hospitals serving the population of central Israel, were followed up through 2005. Extensive data were collected on socio-demographic, clinical, and environmental factors. Daily measures of nitrogen dioxide (NO 2 ), nitric oxide (NO) and fine particles (PM 2.5 ) recorded at air quality monitoring stations were summarized and cumulative exposure was estimated for each patient based on geo-coded residential location at study entry. Cox models were used to assess the hazard ratios (HRs) for all-cause death, cardiac death, recurrent MI, heart failure and stroke associated with a 10 μ g/m 3 increase in pollutant exposure. Results: Patients residing in more polluted areas had better socioeconomic status at both the individual and neighborhood levels. Exposure to pollutants was inversely associated with outcomes. However, these associations were either removed or reversed upon multivariable adjustment for socioeconomic and clinical variables (HRs [95% CIs] of PM 2.5 are reported in the Table). Conclusions: In this unique setting and in contrast to other reports, better socioeconomic status was associated with higher exposure to pollution. In multivariable models accounting for socioeconomic and clinical variables, we observed a weak positive association between PM 2.5 exposure and post-MI outcomes, consistent with findings in the general population. Considering exposure misclassification, the true association is likely stronger. Unadjusted Multivariable-Adjusted All-Cause Death 0.93 (0.53-1.62) 1.51 (0.85-2.67) Cardiac Death 0.74 (0.38-1.44) 1.37 (0.70-2.68) Recurrent MI 1.06 (0.62-1.81) 1.67 (0.96-2.92) Heart Failure 0.60 (0.32-1.12) 1.39 (0.73-2.62) Stroke 0.64 (0.29-1.40) 1.04 (0.46-2.33)

scholarly journals Correlation Between Physical Activity and Clinical Variables in Patients with Acute Myocardial Infarction

2017 ◽  
Author(s):  
Ana Teresa Glaser Carvalho ◽  
Thays Fraga Duarte ◽  
Andressa Sarda Maiochi ◽  
Roberto Leo da Silva ◽  
Tammuz Fattah ◽  
...  
Keyword(s):  
Physical Activity ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Clinical Variables

P1218Deep learning survival analysis enhances the value of hybrid PET/CT for long-term cardiovascular event prediction

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
L E Juarez-Orozco ◽  
J W Benjamins ◽  
T Maaniitty ◽  
A Saraste ◽  
P Van Der Harst ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Survival Analysis ◽  
Deep Learning ◽  
Ct Angiography ◽  
Time To Event ◽  
Clinical Variables ◽  
Pet Ct ◽  
Early Revascularization

Abstract Background Deep Learning (DL) is revolutionizing cardiovascular medicine through complex data-pattern recognition. In spite of its success in the diagnosis of coronary artery disease (CAD), DL implementation for prognostic evaluation of cardiovascular events is still limited. Traditional survival models (e.g.Cox) notably incorporate the effect of time-to-event but are unable to exploit complex non-liner dependencies between large numbers of predictors. On the other hand, DL hasn't systematically incorporated time-to-event for prognostic evaluations. Long-term registries of hybrid PET/CT imaging represent a suitable substrate for DL-based survival analysis due the large amount of time-dependent structured variables that they convey. Therefore, we sought to evaluate the feasibility and performance of DL Survival Analysis in predicting the occurrence of myocardial infarction (MI) and death in a long-term registry of cardiac hybrid PET/CT. Methods Data from our PET/CT registry of symptomatic patients with intermediate CAD risk who underwent sequential CT angiography and 15O-water PET for suspected ischemia, was analyzed. The sample has been followed for a 6-year average for MI or death. Ten clinical variables were extracted from electronic records including cardiovascular risk factors, dyspnea and early revascularization. CT angiography images were evaluated segmentally for: presence of plaque, % of luminal stenosis and calcification (58 variables). Absolute stress PET myocardial perfusion data was evaluated globally and regionally across vascular territories (4 variables). Cox-Nnet (a deep survival neural network) was implemented in a 5-fold cross-validated 80:20 split for training and testing. Resulting DL-hazard ratios were operationalized and compared to the observed events developed during follow-up. The performance of Cox-Nnet evaluating structured CT, PET/CT, and PET/CT+clinical variables was compared to expert interpretation (operationalized as: normal coronaries, non-obstructive CAD, obstructive CAD) and to Calcium Score (CaSc), through the concordance (c)-index. Results There were 426 men and 525 women with a mean age of 61±9 years-old. Twenty-four MI and 49 deaths occurred during follow-up (1 month–9.6 years), while 11.5% patients underwent early revascularization. Cox-Nnet evaluation of PET/CT data (c-index=0.75) outperformed categorical expert interpretation (c-index=0.54) and CaSc (c-index=0.65), while hybrid PET/CT and PET/CT+clinical (c-index=0.75) variables demonstrated incremental performance overall independent from early revascularization. Conclusion Deep Learning Survival Analysis is feasible in the evaluation of cardiovascular prognostic data. It might enhance the value of cardiac hybrid PET/CT imaging data for predicting the long-term development of myocardial infarction and death. Further research into the implementation of Deep Learning for prognostic analyses in CAD is warranted.

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