Why people experiencing acute myocardial infarction delay seeking medical assistance

Background: Delay time from onset of symptoms of myocardial infarction to seeking medical assistance can have life- 31 threatening consequences. A number of factors have been associated with delay, but there is little evidence regarding the predictive 32 value of these indices. Aim: To explore potential predictors of patient delay from onset of symptoms to time medical assistance 33 was sought in a consecutive sample of patients admitted to CCU with acute myocardial infarction. Methods: The Cardiac Denial 34 of Impact Scale, Health Locus of Control Scale, Health Value Scale and Pennebaker Inventory of Limbic Languidness were 35 administered to 62 patients between 3 and 6 days after admission. Results: Attribution of symptoms to heart disease and health 36 locus of control had a significant predictive effect on patients seeking help within 60 min, while previous experience of heart 37 disease did not. Conclusion: Assisting individuals to recognise the potential for symptoms to have a cardiac origin is an important 38 objective. Interventions should take into account the variety of cognitive and behavioural factors involved in decision making.

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Novel biomarkers in early diagnosis of acute myocardial infarction compared with cardiac troponin T

Aims: To evaluate the role of novel biomarkers in early detection of acute myocardial infarction (MI) in patients admitted with acute chest pain.
Methods and results: A prospective study of 664 patients presenting to two coronary care units with chest pain was conducted over 3 years from 2003. Patients were assessed on admission: clinical characteristics, ECG (electrocardiogram), renal function, cardiac troponin T (cTnT), heart fatty acid binding protein (H-FABP), glycogen phosphorylase-BB, NT-pro-brain natriuretic peptide, D-dimer, hsCRP (high sensitivity C-reactive protein), myeloperoxidase, matrix metalloproteinase-9, pregnancy associated plasma protein-A, soluble CD40 ligand. A =12 h cTnT sample was also obtained. MI was defined as cTnT = 0.03 µg/L. In patients presenting <4 h of symptom onset, sensitivity of H-FABP for MI was significantly higher than admission cTnT (73 vs. 55%; P = 0.043). Specificity of H-FABP was 71%. None of the other biomarkers challenged cTnT. Combined use of H-FABP and cTnT (either one elevated initially) significantly improved the sensitivities of H-FABP or cTnT (85%; P = 0.004). This combined approach also improved the negative predictive value, negative likelihood ratio, and the risk ratio.
Conclusion: Assessment of H-FABP within the first 4 h of symptoms is superior to cTnT for detection of MI, and is a useful additional biomarker for patients with acute chest pain.

Improved detection of acute myocardial infarction in patients with chest pain and significant left main stem coronary stenosis.

Background: Non-invasive diagnosis of acute myocardial infarction (AMI) associated with significant left main stem (LMS) stenosis remains challenging.

Methods: Consecutive patients presenting with acute ischaemic-type chest pain from 2000 to 2010 were analysed. Entry criteria: 12-lead ECG and Body Surface Potential Map (BSPM) at presentation, cardiac troponin T (cTnT) =12?h and coronary angiography during admission. cTnT =0.03?µg/l defined AMI. ECG abnormalities assessed: STEMI by Minnesota criteria; ST elevation (STE) aVR =0.5?mm; ST depression (STD) =0.5?mm in =2 contiguous leads (CL); T-wave inversion (TWI) =1?mm in =2 CL. BSPM STE was =2?mm in anterior, =1?mm in lateral, inferior, right ventricular or high right anterior and =0.5?mm in posterior territories. Significant LMS stenosis was =70%.

Results: Enrolled were 2810 patients (aged 60?±?12 years; 71% male). Of these, 116 (4.1%) had significant LMS stenosis with AMI occurring in 92 (79%). STEMI by Minnesota criteria occurred in 13 (11%) (sensitivity 12%, specificity 92%), STE in lead aVR in 23 (20%) (sensitivity 23%, specificity 92%), TWI in 38 (33%) (sensitivity 34%, specificity 71%) and STD in 51 (44%) (sensitivity 49%, specificity 75%). BSPM STE occurred in 85 (73%): sensitivity 88%, specificity 83%, positive predictive value 95% and negative predictive value 65%. Of those with AMI, 74% had STE in either the high right anterior or right ventricular territories not identified by the 12-lead ECG. C-Statistic for AMI diagnosis using BSPM STE was 0.800 (P?<?0.001).

Conclusion: In patients with significant LMS stenosis presenting with chest pain, BSPM STE has improved sensitivity (88%), with specificity 83%, over 12-lead ECG in the diagnosis of AMI.

Lack of Predictability at Work and Risk of Acute Myocardial Infarction: An 18-Year Prospective Study of Industrial Employees

Objectives. We examined whether the distinctive components of job control-decision authority, skill discretion, and predictability-were related to subsequent acute myocardial infarction (MI) events in a large population of initially heart disease-free industrial employees.

Myocardial infarction in the C57BL/6J mouse - A quantifiable and highly reproducible experimental model

Introduction: The laboratory mouse is a powerful tool in cardiovascular research. In this report, we describe a method for a reproducible mouse myocardial infarction model that would allow subsequent comparative and quantitative studies on molecular and pathophysiological variables. Methods: (A) The distribution of the major coronary arteries including the septal artery in the left ventricle of the C57BL/6J mice (n=20) was mapped by perfusion of latex dye or fluorescent beads through the aorta. (B) The territory of myocardial infarction after the ligation of the most proximal aspect of the left anterior descending (LAD) coronary artery was quantified. (C) The consistency in the histological changes parallel to the infarction at different time points was analyzed. Results: (A) The coronary artery tree of the mouse is different from human and, particularly, in regard to the blood supply of the septum. (B) Contrary to previous belief, the septal coronary artery in the mouse is variable in origin. (C) A constant ligation of the LAD immediately below the left auricular level ensures a statistically significant reproducible infarct size. (D) The ischemic changes can be monitored at a histological level in a way similar to what is described in the human. Conclusion: We illustrate a method for maximal reproducibility of experimental acute myocardial infarction in the mouse model, due to a consistent loss of perfusion in the lower half of the left ventricle. This will allow the study of molecular and physiological variables in a controlled and quantifiable experimental model environment. (C) 2004 Elsevier Inc. All rights reserved.