Ian Rowlandson

Test of the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) for Prehospital Use

STUDY OBJECTIVES To test diagnostic performance for acute cardiac ischemia (ACI) in a manually calculated and in a computerized, ECG-calculated ACI time-insensitive predictive instrument (ACI-TIPI) in prehospital chest pain patients. METHODS We carried out prospective inclusion and data acquisition with retrospective analysis. Over a 6-month period, 439 adult emergency medical services patients with chest pain underwent prehospital electrocardiography. Because of incomplete data, 77 cases were excluded, leaving a study sample of 362 patients. Excluded patients did not differ significantly with respect to age, sex, final diagnosis, or history of myocardial infarction, heart surgery, diabetes, or stroke. ACI-TIPI probabilities of ACI were computed on the basis of the prehospital ECGs as interpreted retrospectively and independently by two study investigators blinded to patient outcome, with a specially programmed electrocardiograph, and with a computer algorithm further modified by logistic-regression analysis. RESULTS Diagnostic performance on the basis of receiver operating characteristic (ROC) curve areas of the ACI-TIPI was scored, by the two physician readers, .73 and .74; and by ECG, .75. Patients with low ACI-TIPI probability (0% to 9%) had no acute myocardial infarctions, a 2.3% incidence of angina, and no prehospital life-threatening events. CONCLUSION ACI-TIPI probabilities of ACI as generated by a specially programmed electrocardiograph are comparable to those based on physician ECG interpretations and may be useful in the prehospital evaluation of chest pain.

Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs

BACKGROUND AND PURPOSE Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs. METHODS Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS). RESULTS Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS. CONCLUSIONS Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs.

Computerized STEMI recognition: an example of the art and science of building ECG algorithms

With the advent of thrombolytics, guidelines for ST-elevated myocardial infarction (STEMI) recognition were presented in terms of an ST segment exceeding a particular level (1 or 2 mm) in 2 contiguous leads. However, more than half of prehospital electrocardiograms that exceed these ST criteria are from patients not having an acute myocardial infarction. In contrast, expert physicians (EXMD) maintain a high specificity (>95%) for the recognition of STEMI. Likewise, in terms of increasing sensitivity, it has been found that the EXMD will classify STEMI at lower levels than specified in the guideline. Thus, the EXMD uses additional electrocardiogram features to identify patients for appropriate intervention. Given that STEMI can be defined in terms of a pattern that is recognized by the EXMD as well as a clinical classification that can be evaluated in terms of clinical outcomes, the development and validation of a computer algorithm for STEMI need to include both the art of understanding how the human is detecting STEMI as well as the science required to develop quantified criteria based on clinical outcomes. Evidence is presented that demonstrates that reciprocal depression is a strong indicator of STEMI versus other causes of ST elevation.

QT dispersion and principal component analysis in prehospital patients with chest pain

The objective of this study was to measure QT dispersion (QTD) and principal component analysis (PCA) ratio, using a newly developed algorithm, in a broad range of chest pain patients to determine potential value in diagnosing ischemic heart disease. The algorithm for determining QTD is based on least-square-fit technique, which has better reproducibility than threshold and simple slope methods. QTD and PCA measurements were retrospectively computer-calculated in adults with a chief or secondary complaint of chest pain or equivalent syndrome who had prehospital 12-lead ECGs acquired by paramedics. There were 2157 patients with evaluable data in the final study population. 53% were males, 47% females. Using a threshold of 46 ms, QTp global measurement had a sensitivity/specificity of 60%/90% for AMI and 28%/90% for angina. For AMI, using a threshold of 31, PCA ratio had a sensitivity/specificity of 35%/90%. These data support the contention that QTD and PCA may be useful diagnostic adjuncts for detection of ischemic heart disease.

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

Background: Automated measurements of electrocardiographic (ECG) intervals by current‐generation digital electrocardiographs are critical to computer‐based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. Methods: Seven widely used automated algorithms for computer‐based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). Results: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6 milliseconds for the PR interval, 0.1 to 8.1 milliseconds for QRS duration, and 0.1 to 9.3 milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3 milliseconds in the LQT1 subjects and from 0.2 to 11.0 milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5 milliseconds in the LQT1 subjects and from 0.9 to 12.8 milliseconds in the LQT2 subjects. Conclusions: Among current‐generation computer‐based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within‐individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted.

The detection of T-wave variation linked to arrhythmic risk: An industry perspective

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice. To advance the science, computerized algorithms spawned in the academic environment have been essential in order to find, extract and measure these patterns. However, outside the strict control of a core lab, these algorithms are exposed to poor quality signals and need to be effective in the presence of different forms of noise that can either obscure or mimic the T-wave variation (TWV) of interest. To provide a practical solution that can be verified and validated for the market, important tradeoffs need to be made that are based on an intimate understanding of the end-user as well as the key characteristics of either the signal or the noise that can be used by the signal processing engineer to best differentiate them. To illustrate this, two contemporary medical devices used for quantifying T-wave variation are presented, including the modified moving average (MMA) for the detection of T-wave Alternans (TWA) and the quantification of T-wave shape as inputs to the Morphology Combination Score (MCS) for the trending of drug-induced repolarization abnormalities.