Clinical diagnosis of acute coronary syndrome in patients with chest pain and a normal or non-diagnostic electrocardiogram

A Comparative Study of Ischemia-Modified Albumin: A Promising Biomarker for Early Detection of Acute Coronary Syndrome (ACS)

Introduction The second most common cause of emergency department (ED) visits is chest pain and discomfort. Timely identification or threat stratification is crucial for identifying high-risk individuals who benefit from sophisticated diagnostic investigations (including cardiac biomarkers) and early relevant therapies. We aimed to assess the levels of ischemia-modified albumin (IMA) and also to study its sensitivity and specificity in comparison with cardiac troponin T/troponin I and electrocardiogram (ECG) (alone and in combination) in the diagnosis of acute myocardial infarction. Methods Adults (either gender) presented at the ED of a tertiary care centre with classical chest pain suggestive of angina pectoris or angina-like chest pain and ECG changes suggestive of ACS, ST-elevation myocardial infarction (STEMI), non-ST elevation myocardial MI (NSTEMI), and unstable angina, within three hours of onset were enrolled. Demographic and clinical information was recorded. ECG, haematological investigations like complete blood count, blood sugar level, lipid profile, IMA, troponin I, and creatinine kinase-MB (CK-MB), and radiological investigations like 2D-echocardiography (2D-ECHO) and coronary angiography were performed. Results A total of 100 subjects were enrolled in the study out of which 50 were cases and 50 were controls. Cases were older as compared to controls (mean age 60.5 versus 46.0 years). Of the 50 cases, 33 (66%) were males. There were equal numbers of males (33 each) and females (17 each) subjects in both the groups. Typical chest pain, risk factors, and history of coronary artery disease (CAD) were higher in cases. ECG diagnosis revealed the presence of STEMI (52%) and coronary angiography revealed the presence of double vessel CAD (60%) in cases. Among controls, gastroesophageal reflux disorder was the most common cause of chest pain followed by costochondritis and pneumonia. Glucose (fasting and postprandial), all lipid profile parameters (except high-density lipoprotein) and IMA values were significantly higher in cases as compared to controls. A combination of ECG+IMA has the highest sensitivity (90%) with 79% PPV in the diagnosis of ACS within three hours of the onset of chest pain, and ECG+IMA+2D-ECHO had similar results. However, ECG is equally sensitive. IMA alone has 64% sensitivity with 82% diagnostic accuracy which was higher than other biomarkers (CK-MB, cardiac troponin I). Conclusions As found in our study, among the biomarkers used, the diagnostic accuracy of IMA was the highest and better than that of cardiac troponin I and CK-MB. Although ECG is the preferred diagnostic tool for diagnosing ACS (STEMI, NSTEMI, and unstable angina) in patients presenting within three hours of the onset of chest pain, a confirmation can be done with the help of other diagnostic tests and investigations like serum IMA levels and further treatment can be initiated.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Particulate Matter-Induced Acute Coronary Syndrome: MicroRNAs as Microregulators for Inflammatory Factors

The most prevalent cause of mortality and morbidity worldwide is acute coronary syndrome (ACS) and its consequences. Exposure to particulate matter (PM) from air pollution has been shown to impair both. Various plausible pathogenic mechanisms have been identified, including microRNAs (miRNAs), an epigenetic regulator for gene expression. Endogenous miRNAs, average 22-nucleotide RNAs (ribonucleic acid), regulate gene expression through mRNA cleavage or translation repression and can influence proinflammatory gene expression posttranscriptionally. However, little is known about miRNA responses to fine PM (PM_2.5, PM₁₀, ultrafine particles, black carbon, and polycyclic aromatic hydrocarbon) from air pollution and their potential contribution to cardiovascular consequences, including systemic inflammation regulation. For the past decades, microRNAs (miRNAs) have emerged as novel, prospective diagnostic and prognostic biomarkers in various illnesses, including ACS. We wanted to outline some of the most important studies in the field and address the possible utility of miRNAs in regulating particulate matter-induced ACS (PMIA) on inflammatory factors in this review.

The Serum Metabolic Biomarkers in Early Diagnosis and Risk Stratification of Acute Coronary Syndrome

Despite advances in the treatment of coronary diseases, acute coronary syndrome (ACS) remains the leading cause of death worldwide. ACS is associated with metabolic abnormalities of lipid oxidation stress. In this study, based on liquid chromatograph mass spectrometry technique, we conducted the metabolic profiling analysis of serum samples from stable plaques (SPs) and vulnerable plaques (VPs) in ACS patients for exploring the potential biomarkers of plaque stability. The results showed that four differential metabolites were identified between the SPs and VPs, including betaine, acetylcarnitine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, and isoundecylic acid. Meanwhile, the diagnostic model was identified using stepwise logistic regression and internally validated with 10-fold cross-validation. We analyzed the correlations between serum metabolic perturbations and plaque stability, and the serum betaine and ejection fraction-based model was established with a good diagnostic efficacy [area under the curve (AUC) = 0.808, sensitivity = 70.6%, and specificity = 80.0%]. In summary, we firstly illustrate the comprehensive serum metabolic profiles in ACS patients, suggesting that the combined model of serum betaine and ejection fraction seems to be used as the potential diagnostic biomarker for the vulnerability of plaque stability.

Presenting Symptoms in Men and Women Diagnosed With Myocardial Infarction Using Sex-Specific Criteria

Background Sex-specific criteria are recommended for the diagnosis of myocardial infarction, but the impact of these on presenting characteristics is unknown. Methods and Results We evaluated patient-reported symptoms in 1941 patients (39% women) with suspected acute coronary syndrome attending the emergency department in a substudy of a prospective trial. Standardized criteria defined typical and atypical presentations based on pain nature, location, radiation, and additional symptoms. Diagnosis of myocardial infarction was adjudicated using a high-sensitivity cardiac troponin I assay with sex-specific thresholds (>16 ng/L women, >34 ng/L men). Patients identified who were missed by the contemporary assay with a uniform threshold (≥50 ng/L) were reclassified by this approach. Type 1 myocardial infarction was diagnosed in 16% (184/1185) of men and 12% (90/756) of women, with 9 (5%) men and 27 (30%) women reclassified using high-sensitivity cardiac troponin I and sex-specific thresholds. Chest pain was the presenting symptom in 91% (1081/1185) of men and 92% (698/756) of women. Typical symptoms were more common in women than in men with myocardial infarction (77% [69/90] versus 59% [109/184]; P=0.007), and differences were similar in those reclassified (74% [20/27] versus 44% [4/9]; P=0.22). The presence of ≥3 typical features was associated with a positive likelihood ratio for the diagnosis of myocardial infarction in women (positive likelihood ratio, 1.18; 95% CI, 1.03-1.31) but not in men (positive likelihood ratio 1.09; 95% CI, 0.96-1.24). Conclusions Typical symptoms are more common and have greater predictive value in women than in men with myocardial infarction whether or not they are diagnosed using sex-specific criteria. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier NCT01852123.

Symptoms Predictive of Acute Myocardial Infarction in the Troponin Era: Analysis From the TRAPID-AMI Study

The TRAPID-AMI (High Sensitivity Cardiac Troponin T assay for rapid Rule-out of Acute Myocardial Infarction) study evaluated a rapid "rule-out" acute myocardial infarction (AMI). We evaluated what symptoms were associated with AMI as part of a substudy of TRAPID-AMI. There were 1282 patients evaluated from 12 centers in Europe, the United States of America, and Australia from 2011 to 2013. Multiple symptom variables were prospectively obtained and evaluated for association with the final diagnosis of AMI. Multivariate logistic regression analysis was done, and odds ratios (OR) were calculated. There were 213/1282 (17%) AMIs. Four independent predictors for the diagnosis of AMI were identified: radiation to right arm or shoulder [OR = 3.0; confidence interval (CI): 1.8-5.0], chest pressure (OR = 2.5; CI: 1.3-4.6), worsened by physical activity (OR = 1.7; CI: 1.2-2.5), and radiation to left arm or shoulder (OR = 1.7; CI: 1.1-2.4). In the entire group, 131 (10%) had radiation to right arm or shoulder, 897 (70%) had chest pressure, 385 (30%) worsened with physical activity, and 448 (35%) had radiation to left arm or shoulder. Duration of symptoms was not predictive of AMI. There were no symptoms predictive of non-AMI. Relationship between AMI size and symptoms was also studied. For 213 AMI patients, cardiac troponins I values were divided into 4 quartiles. Symptoms including pulling chest pain, supramammillary right location, and right arm/shoulder radiation were significantly more likely to occur in patients with larger AMIs. In a large multicenter trial, only 4 symptoms were associated with the diagnosis of AMI, and no symptoms that were associated with a non-AMI diagnosis.

A randomized trial of a 1-hour troponin T protocol in suspected acute coronary syndromes: Design of the Rapid Assessment of Possible ACS In the emergency Department with high sensitivity Troponin T (RAPID-TnT) study

BACKGROUND

Protocols incorporating high-sensitivity troponin to guide decision making in the disposition of patients with suspected acute coronary syndromes (ACS) in the emergency department have received a lot of attention. Traditionally, patients with chest pain have required long periods of observation in emergency department before being deemed safe for discharge. In an era of limited health service resources, a protocol that could discharge patients safely within an hour of presentation is extremely attractive. Unfortunately, despite incorporation into some guidelines, these protocols have not been subjected to randomized comparisons evaluating safety, effectiveness, and cost-effectiveness.

OBJECTIVE

This study is designed to provide the evidence required to allow key decision makers to implement these protocols: specifically, to provide evidence that a decision rule based on 0- and 1-hour high-sensitivity troponin T (hs-TnT) is safe, provides noninferior outcomes in all patients with suspected ACS, and that implementation of a rapid troponin protocol leads to efficient care.

DESIGN

This prospective pragmatic trial (n=5,400, 5 hospitals) randomly allocates patients with suspected ACS to either a 0/1-hour hs-TnT protocol as advocated in clinical guidelines, versus usual care of standard troponin reporting evaluated at 3 and 6hours. The primary effectiveness composite end points of this study are all-cause death and new/recurrent ACS within 30days. To evaluate cost-effectiveness, follow-up will determine clinical events, quality of life, and resource utilization within 12 months.

SUMMARY

Demonstrating that a 0/1-hour hs-TnT protocol improves the effectiveness and efficiency of care within a robust comparative study will fill an evidence gap that currently limits the translation of more precise hs-TnT testing into better patient and health service outcomes.

'Chest pain typicality' in suspected acute coronary syndromes and the impact of clinical experience

BACKGROUND

Physicians rely upon chest pain history to make management decisions in patients with suspected acute coronary syndromes, particularly where the diagnosis is not immediately apparent through electrocardiography and troponin testing. The objective of this study was to establish the discriminatory value of "typicality of chest pain" and the effect of clinician experience, for the prediction of acute myocardial infarction and presence of significant coronary artery disease.

METHODS

This prospective single-center observational study was undertaken in a UK General Hospital emergency department. We recruited consecutive adults with chest pain and a nondiagnostic electrocardiogram, for whom the treating physician determined that delayed troponin testing was necessary. Using their own clinical judgment, physicians recorded whether the chest pain described was typical or atypical for acute coronary syndrome. Physicians were defined as "experienced" or "novice" according to postgraduate experience. Acute myocardial infarction was adjudicated using a high-sensitivity troponin (hs-cTn) assay, whereas coronary artery disease was adjudicated angiographically.

RESULTS

Overall, 912 patients had typicality of chest pain assessed, of whom 114/912 (12.5%) had an acute myocardial infarction and 157/912 (17.2%) underwent angiography. In patients undergoing angiography, 90/157 (57.3%) had hs-cTn elevation, of whom 60 (66.7%) had significant coronary artery disease. Sixty-seven of 157 (42.7%) patients had angiography without hs-cTn elevation; of these, 31 (46.2%) had significant coronary artery disease. For the diagnosis of acute myocardial infarction, chest pain typicality had an area under the curve (AUC) of 0.54 (95% confidence interval [CI], 0.49-0.60). For the prediction of significant coronary artery disease with hs-cTn elevation AUC: 0.54 (95% CI, 0.40-0.67), and without hs-cTn elevation AUC: 0.45 (95% CI, 0.31-0.59). When assessed by experienced physicians, specificity for the diagnosis of acute myocardial infarction was higher at 65.8% (95% CI, 63.1%-68.7%) vs 55.4% (95% CI, 53.9%-56.8%) for novices.

CONCLUSIONS

Subjective interpretation of "typicality of chest pain" is of limited discriminatory value in the assessment of suspected acute coronary syndromes, in the context of a nondiagnostic electrocardiogram. Greater clinical experience improves accuracy as a rule-in tool but does not improve overall discriminatory ability.

Management of non-traumatic chest pain by the French Emergency Medical System: Insights from the DOLORES registry

BACKGROUND

The early recognition of acute coronary syndromes is a priority in health care systems, to reduce revascularization delays. In France, patients are encouraged to call emergency numbers (15, 112), which are routed to a Medical Dispatch Centre where physicians conduct an interview and decide on the appropriate response. However, the effectiveness of this system has not yet been assessed.

AIM

To describe and analyse the response of emergency physicians receiving calls for chest pain in the French Emergency Medical System.

METHODS

From 16 November to 13 December 2009, calls to the Medical Dispatch Centre for non-traumatic chest pain were included prospectively in a multicentre observational study. Clinical characteristics and triage decisions were collected.

RESULTS

A total of 1647 patients were included in the study. An interview was conducted with the patient in only 30.5% of cases, and with relatives, bystanders or physicians in the other cases. A Mobile Intensive Care Unit was dispatched to 854 patients (51.9%) presenting with typical angina chest pains and a high risk of cardiovascular disease. Paramedics were sent to 516 patients (31.3%) and a general practitioner was sent to 169 patients (10.3%). Patients were given medical advice only by telephone in 108 cases (6.6%).

CONCLUSIONS

Emergency physicians in the Medical Dispatch Centre sent an effecter to the majority of patients who called the Emergency Medical System for chest pain. The response level was based on the characteristics of the chest pain and the patient's risk profile.

Chest pain in an out-of-hospital emergency setting: no relationship between pain severity and diagnosis of acute myocardial infarction

BACKGROUND

Chest pain frequently prompts emergency medical services (EMS) call-outs. Early management of acute coronary syndrome (ACS) cases is crucial, but there is still controversy over the relevance of pain severity as a diagnostic criterion.

STUDY OBJECTIVE

The aim of this study was to determine whether there is a relationship between the severity of chest pain at the time of out-of-hospital emergency care and diagnosis of acute myocardial infarction (AMI).

METHODS

This was a subsidiary analysis of prehospital data collated prospectively by EMS in a large suburb. It concerned patients with chest pain taken to hospital by a mobile intensive care unit. Pain was rated on EMS arrival using a visual analog, numeric or verbal rating scale and classified on severe or not severe according to the pain score. A diagnosis of AMI was confirmed or ruled out on the basis of 2 plasma troponin measurements and/or coronary angiography results.

RESULTS

Among the cohort of 2,279 patients included, 234 were suitable for analysis, of which 109 (47%) were diagnosed with AMI. The rate of severe pain on EMS arrival was not significantly different between AMI patients and no myocardial infarction patients (49% [95% CI 40 to 58] and 43% [34 to 52], respectively; P = 0.3; odds ratio 1.3 [0.8 - 2.3] after adjustment for age and gender).

CONCLUSION

In our out-of-hospital emergency setting, the severity of chest pain was not a useful diagnostic criterion for AMI.

Application of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling

OBJECTIVE

The classification of Acute Coronary Syndrome (ACS), using artificial intelligence (AI), has recently drawn the attention of the medical researchers. Using this approach, patients with myocardial infarction can be differentiated from those with unstable angina. The present study aims to develop an integrated model, based on the feature selection and classification, for the automatic classification of ACS.

METHODS

A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.

RESULTS

The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.

CONCLUSION

The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.

A clinical scoring system in undifferentiated chest pain predicting undetectable troponin concentration

BACKGROUND

Chest pain is the most common reason for emergency admission to hospital, but the majority of these are due to non-cardiac pain. We sought to determine which combination of clinical features is more likely to predict an undetectable troponin level in patients presenting with chest pain.

METHODS

We collected data over a two-month period on consecutive patients presenting acutely to hospital with chest pain and who had a troponin I measured. We recorded basic demographics, risk factors, pain distribution, associated symptoms, physical findings and ECG changes. The parameters significantly associated with troponin positivity were entered into a stepwise logistic regression analysis and the resulting model's coefficients were used to construct a simple clinical score to categorise patients into low, medium or high probability of having a positive troponin.

RESULTS

26 of 157 (16.6%) patients had a positive troponin. The variables retained in the regression model were: age >65, heart rate >80, previous myocardial infarction, diabetes and pain radiating to either arm. The model showed good discrimination (area under ROC curve 0.869, 95% CI 0.806 - 0.917). Using the regression model's coefficients, patients were grouped into low, intermediate or high probability groups. Being in the low probability group had a negative predictive value of 97.8% and being in the high probability group had a positive predictive value of 65.2%. The majority (73.9%) of patients could be categorised as either low or high probability.

DISCUSSION

This simple scoring system, if prospectively validated, may be useful in identifying low risk patients with chest pain who are unlikely to have elevation of serum troponin concentration.

Scuba diving, acute left anterior descending artery occlusion and normal ECG

We report the case of an acute proximal occlusion of the left anterior descending coronary (LAD) artery following a scuba diving decompression accident and associated with normal ECG. Following uneventful thromboaspiration and coronary stenting, the patient was discharged on day 4 with secondary preventative therapies. A transthoracic echocardiography performed at this point showed a complete recovery compared with an initial localised akinesia involving the anterior and apical portion of the left ventricle upon admission. This case highlights that significant acute coronary lesions involving the LAD can occur without any ECG anomaly. The presence of acute and persistent angina associated with troponin elevation should prompt physicians to consider coronary angiography without delay, independently of the ECG results.