Short- and long-term prognostic performance of exercise ECG and myocardial perfusion SPECT.

BACKGROUND: Myocardial perfusion SPECT (MPS) and exercise electrocardiography (Ex-ECG) results are of prognostic importance for short-term follow up duration. However, the value of MPS or Ex-ECG findings for long-term risk assessment is less evident as underlying risk factors for ischemic heart disease (IHD) gain in importance. OBJECTIVES: To assess the short- and long-term prognostic value of MPS and Ex-ECG in relation to known risk factors. METHODS AND MATERIALS: An observational study of 908 patients (age 63 years, 49% male, 45% prior IHD) referred for MPS and Ex-ECG. Follow-up was divided into two periods (short-term: <5 years and long-term: >5 years). Cardiac events were defined as a composite of acute myocardial infarction, unstable angina, unplanned revascularization and cardiovascular death. RESULTS: The composite endpoint occurred in 95 patients (short-term follow up) and in 94 patients (long-term follow up). In multivariable models stress testing had a strong predictive value for short-term follow up (HR for MPS = 2.9, CI = 1.9-4.5, p < 0.001 and HR for Ex-ECG = 2.1, CI 1.3-3.3, p = 0.002), but no predictive value for long-term follow up (HR for MPS = 0.9, CI = 0.5-1.5, p = 0.70 and HR for Ex-ECG = 1.0, CI = 0.6-1.6, p = 0.92). Male sex and prior IHD were significant predictors regardless of follow up duration. Age, diabetes and decreased exercise capacity were risk factors for long-term follow up. CONCLUSIONS: The prognostic value of MPS and Ex-ECG results are strong for short-term follow up but diminish over time and do not contribute significantly in multivariable models after 5 years. Long-term prognosis is primarily governed by underlying risk factors and exercise capacity.

Standards for the function of an academic 12-lead electrocardiographic core laboratory.

An academic 12-lead electrocardiogram (ECG) core laboratory aims to provide the highest possible quality ECG recording, measurement, and storage to aid clinicians in research into important cardiovascular outcomes and to maximize the credibility of scientific results based solely, or in part, on ECG data. This position paper presents a guide for the structure and function of an academic ECG core laboratory. The key functional aspects are: 1) Data collection, 2) Staff composition, 3) Diagnostic measurement and definition standards, 4) Data management, 5) Academic considerations, 6) Economic consideration, and 7) Accreditation. An ECG Core Laboratory has the responsibility for rapid and accurate analysis and responsible management of the electrocardiographic data in multicenter clinical trials. Academic Laboratories, in addition, provide leadership in research protocol generation and production of research manuscripts for submission to the appropriate peer-review journals.

Outcome after cup hemiarthroplasty in the rheumatoid shoulder: a retrospective evaluation of 39 patients followed for 2-6 years.

33 rheumatoid patients, treated with hemispherical cup resurfacing hemiarthroplasty of the shoulder without medullary fixation (6 bilaterally), were reviewed after mean 4.4 (2-6) years. The median Constant score was 30 (15-79), mean proximal migration of the humerus 55 (SD 5.2) mm and mean glenoid erosion 2.6 (SD 1.7) mm. Proximal migration and glenoid erosion did not correlate with shoulder function or pain. Radiographic signs of loosening (changes in cup inclination combined with changes in cup distance above the greater tuberosity) occurred in one quarter of the shoulders. At follow-up, 26 patients were satisfied with the procedure, despite poor shoulder function and radiographic deterioration.

The correlation between presenting ST-segment depression and the final size of acute myocardial infarcts in patients with acute coronary syndromes.

The use of reperfusion therapy in patients with ST elevation acute coronary syndromes had been established. However, reperfusion therapy is usually considered contra-indicated in those with ST depression, despite the knowledge that regional posterior infarction is typically indicated by ST depression maximal in leads V1 to V3 and nonregional subendocardial infarction is typically indicated by marked ST depression maximal in other leads. This study of patients with non-ST-elevation acute coronary syndromes investigates the quantitative relationship between presenting ST depression and final QRS changes in both of these subgroups. The final QRS score was significantly higher (2.44 points) than that of a control group with not ST depression, (1.55 points) in the group with maximal ST depression in V1 to V3 (P = 0.04). However, in the entire population, there was a highly significant correlation (P = .003) between the sum of the presenting ST depression and the final QRS score. Trials of reperfusion therapy will be required to determine if such evolution to electrocardiogram documented acute myocardial infarction can be prevented in patient with marked ST depression acute coronary syndromes.

Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks.

BACKGROUND: The 12-lead ECG, together with patient history and clinical findings, remains the most important method for early diagnosis of acute myocardial infarction. Automated interpretation of ECG is widely used as decision support for less experienced physicians. Recent reports have demonstrated that artificial neural networks can be used to improve selected aspects of conventional rule-based interpretation programs. The purpose of this study was to detect acute myocardial infarction in the 12-lead ECG with artificial neural networks. METHODS AND RESULTS: A total of 1120 ECGs from patients with acute myocardial infarction and 10,452 control ECGs, recorded at an emergency department with computerized ECGs, were studied. Artificial neural networks were trained to detect acute myocardial infarction by use of measurements from the 12 ST-T segments of each ECG, together with the correct diagnosis. After this training process, the performance of the neural networks was compared with that of a widely used ECG interpretation program and the classification of an experienced cardiologist. The neural networks showed higher sensitivities and discriminant power than both the interpretation program and cardiologist. The sensitivity of the neural networks was 15.5% (95% confidence interval [CI], 12.4 to 18.6) higher than that of the interpretation program compared at a specificity of 95.4% (P<.00001) and 10.5% (95% CI, 7.2 to 13.6) higher than the cardiologist at a specificity of 86.3% (P<.00001). CONCLUSIONS: Artificial neural networks can be used to improve automated ECG interpretation for acute myocardial infarction. The networks may be useful as decision support even for the experienced ECG readers.

Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction.

OBJECTIVES: The purpose of this study was to compare the diagnoses of healed myocardial infarction made from the 12-lead electrocardiogram (ECG) by artificial neural networks and an experienced electrocardiographer. BACKGROUND: Artificial neural networks have proved of value in pattern recognition tasks. Studies of their utility in ECG interpretation have shown performance exceeding that of conventional ECG interpretation programs. The latter present verbal statements, often with an indication of the likelihood for a certain diagnosis, such as "possible left ventricular hypertrophy." A neural network presents its output as a numeric value between 0 and 1; however, these values can be interpreted as Bayesian probabilities. METHODS: The study was based on 351 healthy volunteers and 1,313 patients with a history of chest pain who had undergone diagnostic cardiac catheterization. A 12-lead ECG was recorded in each subject. An expert electrocardiographer classified the ECGs in five different groups by estimating the probability of anterior myocardial infarction. Artificial neural networks were trained and tested to diagnose anterior myocardial infarction. The network outputs were divided into five groups by using the output values and four thresholds between 0 and 1. RESULTS: The neural networks diagnosed healed anterior myocardial infarctions at high levels of sensitivity and specificity. The network outputs were transformed to verbal statements, and the agreement between these probability estimates and those of an expert electrocardiographer was high. CONCLUSIONS: Artificial neural networks can be of value in automated interpretation of ECGs in the near future.

Detection of frequently overlooked electrocardiographic lead reversals using artificial neural networks.

Artificial neural networks can be used to recognize lead reversals in the 12-lead electrocardiogram at very high specificity, and the sensitivity is much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of lead reversals, in combination with an algorithm for the right arm/right foot lead reversal, would recognize approximately 75% of lead reversals encountered in clinical practice.

Artificial neural networks for recognition of electrocardiographic lead reversal.

Misplacement of electrodes during the recording of an electrocardiogram (ECG) can cause an incorrect interpretation, misdiagnosis, and subsequent lack of proper treatment. The purpose of this study was twofold: (1) to develop artificial neural networks that yield peak sensitivity for the recognition of right/left arm lead reversal at a very high specificity; and (2) to compare the performances of the networks with those of 2 widely used rule-based interpretation programs. The study was based on 11,009 ECGs recorded in patients at an emergency department using computerized electrocardiographs. Each of the ECGs was used to computationally generate an ECG with right/left arm lead reversal. Neural networks were trained to detect ECGs with right/left arm lead reversal. Different networks and rule-based criteria were used depending on the presence or absence of P waves. The networks and the criteria all showed a very high specificity (99.87% to 100%). The neural networks performed better than the rule-based criteria, both when P waves were present (sensitivity 99.1%) or absent (sensitivity 94.5%). The corresponding sensitivities for the best criteria were 93.9% and 39.3%, respectively. An estimated 300 million ECGs are recorded annually in the world. The majority of these recordings are performed using computerized electrocardiographs, which include algorithms for detection of right/left arm lead reversals. In this study, neural networks performed better than conventional algorithms and the differences in sensitivity could result in 100,000 to 400,000 right/left arm lead reversals being detected by networks but not by conventional interpretation programs.

Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction.

Artificial neural networks are computer-based expert systems that learn by example, in contrast to the currently used rule-based electrocardiographic interpretation programs. For the purpose of this study, 1,107 electrocardiograms (ECGs) from patients who had undergone cardiac catheterization were used to train and test neural networks for the diagnosis of myocardial infarction. Different combinations of QRS and ST-T measurements were used as input to the neural networks. In a learning process, the networks automatically adjusted their characteristics to correctly diagnose anterior or inferior wall myocardial infarction from the ECG. Two thirds of the ECGs were used in this process. Thereafter, the performance of the networks was studied in a separate test set, using the remaining third of the ECGs. The results from the networks were also compared with that of conventional electrocardiographic criteria. The sensitivity for the diagnosis of anterior myocardial infarction was 81% for the best network and 68% for the conventional criteria (p < 0.01), both having a specificity of 97.5%. The corresponding sensitivities of the network and the criteria for the diagnosis of inferior myocardial infarction were 78% and 65.5% (p < 0.01), respectively, compared at a specificity of 95%. The results indicate that artificial neural networks may be of interest in the attempt to improve computer-based electrocardiographic interpretation programs.