PubMed · 6520609
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J I Martinez-Lopez. 1984. Copy cats.. https://pubmed.ncbi.nlm.nih.gov/6520609/
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Rhythm analysis by commercial systems does not meet clinical needs well, because (1) differential diagnosis of complex rhythms is not performed, (2) common rhythms are often misdiagnosed, and (3) transitions between rhythms are not described. We have developed a model-based diagnostic software system named EINTHOVEN that is designed to address the above limitations. A demonstration is available on the World Wide Web at http:@einthoven.uokhsc.edu. The system has been validated using simple rhythms from introductory electrocardiogram (ECG) textbooks. We present here the results of evaluation with more complex rhythm strips taken from clinical records and intermediate-level ECG textbooks. Rhythm strips were described by the onset and offset of each electrical event (P wave, QRS complex, and T wave) and by a morphology classification for each event. The rhythms included a variety of supraventricular and ventricular rhythms. The analysis was considered correct if it named all correct diagnoses in a rhythm strip, incorrect if it completed the analysis and failed to name the correct diagnoses, and indeterminate if it failed to complete the analysis. The system was designed not to complete an analysis if it could not explain an entire rhythm by at least 1 pathophysiological model. The test rhythms were not used to develop the system. Forty-six of 56 test rhythms were diagnosed correctly, and 8 were not analyzed completely. The 2 incorrect diagnoses were atrial tachycardia with variable conduction (diagnosed as intermittent complete heart block) and atrial fibrillation (diagnosed as irregular junctional tachycardia). All 56 rhythms were diagnosed correctly after minor technical improvements to the system. The processing time of the system was 7.6-fold (range 1.5-to 16.9-fold) faster than the elapsed time of the individual records. These preliminary results suggest (1) that computer-based interpretation of complex rhythms is possible, (2) that further software development is necessary to reach a clinical level of accuracy, and (3) that there are no theoretical obstacles to achieving this goal.
The aim of this study was to extract and combine non-invasive risk parameters from the signal-averaged electrocardiogram (SAECG) and heart rate variability (HRV) based on 24-hour ambulatory electrocardiography to optimize the prognostic value for arrhythmic events after acute myocardial infarction. A prospective series of 553 men < 66 years of age enrolled in the Post-Infarction Late Potential study were analyzed. Within 2 to 4 weeks after acute myocardial infarction, all patients underwent SAECG and 24-hour ambulatory electrocardiography before hospital discharge. During 6 months of followup, 25 patients (4.5%) experienced arrhythmic events (sustained ventricular tachycardia, n = 11; ventricular fibrillation, n = 7; sudden cardiac death, n = 7). The predictive power of SAECG and HRV parameters was assessed using a Cox proportional-hazards model. In HRV analysis, the most significant differences between patients with and without arrhythmic events were observed for the beat-to-beat parameter root-meansquare of successive RR differences [RMSSD]): 25.7 +/- 16.9 ms in patients with arrhythmic events versus 34.1 +/- 18.6 ms in patients free of arrhythmic events (p = 0.004). Time domain analysis of the SAECG showed the QRS duration to be most significantly different in both patient groups: 106.4 +/- 18.7 ms (arrhythmic events) versus 95.3 +/- 18.7 ms (no arrhythmic events) (p = 0.001). Based on the Cox regression model, RMSSD and QRS duration were demonstrated to be independent significant risk factors (regression coefficient for QRS duration: cq = 0.014 +/- 0.006 ms(-1), p = 0.014; for RMSSD: cr = -0.041 +/- 0.016 ms(-1), p = 0.009). Based on the regression coefficients, an analytic risk model was developed describing the arrhythmic risk as a function of QRS duration, RMSSD, and time after infarction. We conclude that the combination of beat-to-beat changes of heart rate measured by RMSSD and QRS duration from the SAECG enhances noninvasive risk stratification after myocardial infarction.
As soon as it was established that the implantable cardioverter-defibrillator (ICD) effectively prevents sudden death, it became axiomatic that whether the ICD will prolong overall survival depends entirely on the population of patients to which it is applied. This axiom of overall ICD survival immediately reveals the only vital question that remains regarding usage of the ICD; namely, How does one select those patients in whom prevention of sudden death by the ICD will also prolong life? This axiom also reveals the essential futility of randomized trials now being conducted for the purpose of discerning the true efficacy of the ICD. Claims to the scientific high ground notwithstanding, if a study asks the wrong question from the beginning, then the design of that study (including whether the study is randomized or nonrandomized), is completely irrelevant. Ideally, funds now being spent on these randomized trials should be diverted to the design and initiation of more appropriate trials, trials that will teach us to select patients for the ICD more effectively. At the very least, however, we should recognize the problems inherent in the ongoing trials, so that when their results are finally published (and are loudly touted by whichever faction feels vindicated by them), we will not be carried away into inappropriate clinical behavior.