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Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Background The rate-limiting step in STEMI diagnosis often is the availability of a 12-lead electrocardiogram (ECG) and its interpretation. The potential may exist to speed the availability of 12-lead ECG information by using commonly available mobile technologies. We sought to test whether combining serial smartphone single-lead ECGs to create a virtual 12-lead ECG can accurately diagnose STEMI. Methods Consenting patients presenting with symptoms consistent with a possible STEMI had contemporaneous standard 12-lead and smartphone ‘12-lead equivalent’ ECG (produced by electronically combining serial single-lead ECGs) recordings obtained. Matched ECGs were evaluated qualitatively and quantitatively by a panel of blinded readers and classified as STEMI/STEMI equivalent (LBBB), Not-STEMI, or uninterpretable. Interpretable ECG pairs were graded as showing good, fair, or poor correlation. Results Two hundred four subjects (age = 60 years, males = 57%, STEMI activation = 45%) were enrolled from 5 international sites. Smartphone ECG quality was graded as good in 151 (74.0%), fair in 32 (15.7%), poor in 8 (3.9%), and uninterpretable in 13 (6.4%). A STEMI/STEMI equivalent diagnosis was identified by standard 12-lead ECG in 57/204 (27.9%) recordings. For all interpretable pairs of smartphone ECGs compared with standard ECGs (n = 190), the sensitivity, specificity, and positive and negative predictive values for STEMI/STEMI equivalent by smartphone were 0.89, 0.84, 0.70 and 0.95, respectively. Conclusions A ‘12-lead equivalent’ ECG obtained from multiple serial single-lead ECGs from a smartphone can identify STEMI with good correlation to a standard 12-lead ECG. This technology holds promise to improve outcomes in STEMI by enhancing the reach and speed of diagnosis and thereby early treatment.

Motivation and Objectives Smartphones are emerging as personal fitness assistants, collecting data through in-built or external sensors. The next frontier for these devices is to use advanced sensors and machine learning algorithms to offer more personalized and advanced medical assessments. Along these lines, the objective of this paper is to develop a multi-label classification model to detect heart complications through electrocardiograms (EKGs) collected by an FDA-approved single-lead EKG sensor attached to a smartphone. The EKG sensor produces a standard EKG chart, but for such a sensor to be useful to a consumer, an interpretation of the graph is necessary. Materials and Method We adapt a machine-learning approach to detect multiple heart conditions simultaneously from the generated EKG graph. Three different multi-label machine learning models (binary relevance, label powerset and multi-perceptron neural network) were built and compared to categorize five different heart states: Normal, Atrial Fibrillation, Atrioventricular Block, Sinus Bradycardia and Sinus Tachycardia. The binary relevance model was selected based on the accuracy. Results and Implications The model generated rules inductively from the data to interpret nine out of every ten heart conditions correctly. Our model is being adapted for commercial use by a company (as a part of their App) that markets the EKG sensor for smartphones. Our model is usable in a cardiovascular disease alert expert system that will potentially allow users to monitor their heart health continuously and prevent a serious illness by providing this information in the early stages.

Background Interval duration measurements (IDMs) were compared between standard 12‐lead electrocardiograms (ECGs) and 6‐lead ECGs recorded with AliveCor's KardiaMobile 6L, a hand‐held mobile device designed for use by patients at home. Methods Electrocardiograms were recorded within, on average, 15 min from 705 patients in Mayo Clinic's Windland Smith Rice Genetic Heart Rhythm Clinic. Interpretable 12‐lead and 6‐lead recordings were available for 685 out of 705 (97%) eligible patients. The most common diagnosis was congenital long QT syndrome (LQTS, 343/685 [50%]), followed by unaffected relatives and patients (146/685 [21%]), and patients with other genetic heart diseases, including hypertrophic cardiomyopathy (36 [5.2%]), arrhythmogenic cardiomyopathy (23 [3.4%]), and idiopathic ventricular fibrillation (14 [2.0%]). IDMs were performed by a central ECG laboratory using lead II with a semi‐automated technique. Results Despite differences in patient position (supine for 12‐lead ECGs and sitting for 6‐lead ECGs), mean IDMs were comparable, with mean values for the 12‐lead and 6‐lead ECGs for QTcF, heart rate, PR, and QRS differing by 2.6 ms, −5.5 beats per minute, 1.0 and 1.2 ms, respectively. Despite a modest difference in heart rate, intervals were close enough to allow a detection of clinically meaningful abnormalities. Conclusions The 6‐lead hand‐held device is potentially useful for a clinical follow‐up of remote patients, and for a safety follow‐up of patients participating in clinical trials who cannot visit the investigational site. This technology may extend the use of 12‐lead ECG recordings during the current COVID‐19 pandemic as remote patient monitoring becomes more common in virtual or hybrid‐design clinical studies.

Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of patients with genetically confirmed LQTS have a normal corrected QT (QTc) at rest. Distinguishing patients with LQTS from those with a normal QTc is important to correctly diagnose disease, implement simple LQTS preventive measures, and initiate prophylactic therapy if necessary.To determine whether artificial intelligence (AI) using deep neural networks is better than the QTc alone in distinguishing patients with concealed LQTS from those with a normal QTc using a 12-lead electrocardiogram (ECG).A diagnostic case-control study was performed using all available 12-lead ECGs from 2059 patients presenting to a specialized genetic heart rhythm clinic. Patients were included if they had a definitive clinical and/or genetic diagnosis of type 1, 2, or 3 LQTS (LQT1, 2, or 3) or were seen because of an initial suspicion for LQTS but were discharged without this diagnosis. A multilayer convolutional neural network was used to classify patients based on a 10-second, 12-lead ECG, AI-enhanced ECG (AI-ECG). The convolutional neural network was trained using 60% of the patients, validated in 10% of the patients, and tested on the remaining patients (30%). The study was conducted from January 1, 1999, to December 31, 2018.The goal of the study was to test the ability of the convolutional neural network to distinguish patients with LQTS from those who were evaluated for LQTS but discharged without this diagnosis, especially among patients with genetically confirmed LQTS but a normal QTc value at rest (referred to as genotype positive/phenotype negative LQTS, normal QT interval LQTS, or concealed LQTS).Of the 2059 patients included, 1180 were men (57%); mean (SD) age at first ECG was 21.6 (15.6) years. All 12-lead ECGs from 967 patients with LQTS and 1092 who were evaluated for LQTS but discharged without this diagnosis were included for AI-ECG analysis. Based on the ECG-derived QTc alone, patients were classified with an area under the curve (AUC) value of 0.824 (95% CI, 0.79-0.858); using AI-ECG, the AUC was 0.900 (95% CI, 0.876-0.925). Furthermore, in the subset of patients who had a normal resting QTc (450 milliseconds), the QTc alone distinguished those with LQTS from those without LQTS with an AUC of 0.741 (95% CI, 0.689-0.794), whereas the AI-ECG increased this discrimination to an AUC of 0.863 (95% CI, 0.824-0.903). In addition, the AI-ECG was able to distinguish the 3 main genotypic subgroups (LQT1, LQT2, and LQT3) with an AUC of 0.921 (95% CI, 0.890-0.951) for LQT1 compared with LQT2 and 3, 0.944 (95% CI, 0.918-0.970) for LQT2 compared with LQT1 and 3, and 0.863 (95% CI, 0.792-0.934) for LQT3 compared with LQT1 and 2.In this study, the AI-ECG was found to distinguish patients with electrocardiographically concealed LQTS from those discharged without a diagnosis of LQTS and provide a nearly 80% accurate pregenetic test anticipation of LQTS genotype status. This model may aid in the detection of LQTS in patients presenting to an arrhythmia clinic and, with validation, may be the stepping stone to similar tools to be developed for use in the general population.

Background: Atrial fibrillation (AF) burden and duration appear to be related to stroke risk. A wearable consumer electronic device could provide long-term assessment of these measures inexpensively and noninvasively. This study compares the accuracy of an AF-sensing watch (AFSW; Apple Watch with KardiaBand) with simultaneous recordings from an insertable cardiac monitor (ICM; Reveal LINQ). Methods: SmartRhythm 2.0, a convolutional neural network, was trained on anonymized data of heart rate, activity level, and ECGs from 7500 AliveCor users. The network was validated on data collected in 24 patients with ICMs and a history of paroxysmal AF who simultaneously wore the AFSW with SmartRhythm 0.1 software. The primary outcome was sensitivity of the AFSW for AF episodes ≥1 hour. Secondary end points included sensitivity of the AFSW for detection of AF by subject and sensitivity for total AF duration across all subjects. Subjects with >50% false-positive AF episodes on ICM were excluded. Results: We analyzed 31 348.9 hours (mean (SD), 11.3 (4.4) hours/day) of simultaneous AFSW and ICM recordings in 24 patients. The ICM detected 82 episodes of AF ≥1 hour while the AFSW was worn, with a total duration of 1127.1 hours. Of these, the SmartRhythm 2.0 neural network detected 80 episodes (episode sensitivity, 97.5%) with a total duration of 1101.1 hours (duration sensitivity, 97.7%). Three of the 18 subjects with AF ≥1 hour had AF only when the watch was not being worn (patient sensitivity, 83.3%; or 100% during time worn). Positive predictive value for AF episodes was 39.9%. Conclusions: An AFSW is highly sensitive for detection of AF and assessment of AF duration in an ambulatory population when compared with an ICM. Such devices may represent an inexpensive, noninvasive approach to long-term AF surveillance and management.

Objective To understand the performance of a currently used clinical blood test with regard to the frequency and size of variation of the results. Patients and Methods From November 29, 2012, through November 29, 2013, patients were recruited at 65 sites as part of a previously reported clinical trial (ClinicalTrials.gov Identifier: NCT01737697). Eligible outpatients who had been fasting for at least 8 hours underwent venous phlebotomy at baseline, 30 minutes, and 60 minutes to measure plasma potassium levels in whole blood using a point-of-care device (i-STAT, Abbott Laboratories). We analyzed the results to assess their variability and frequency of pseudohyperkalemia and pseudonormokalemia. Results A total of 1170 patients were included in this study. Absolute differences between pairs of measurements from different time points ranged from 0 to 2.5 mmol/L, with a mean difference of 0.26 mmol/L. The mean percentage differences were approximately 5% with an SD of 5%. Approximately 12% of differences between repeated fasting potassium blood test results were above 0.5 mmol/L (33% of the normal range), and 20% of patients (234) had at least one difference greater than 0.5 mmol/L. In 44.0% of the patients with a hyperkalemic average value (true hyperkalemia) (302 of 686), at least one blood test result was in the normal range (pseudonormokalemia), and in 30.2% of the patients with a normal average value (146 of 484), at least one blood test result was elevated (pseudohyperkalemia). Conclusion Expected variability and errors exist with potassium blood tests, even when conditions are optimized. Pseudohyperkalemia and pseudonormokalemia are common, indicating a need for thoughtful clinical interpretation of unexpected test results.

Although automated ECG analysis has been available for many years, there are some aspects which require to be re-assessed with respect to their value while newer techniques which are worthy of review are beginning to find their way into routine use. At the annual International Society of Computerized Electrocardiology conference held in April 2017, four areas in particular were debated. These were a) automated 12 lead resting ECG analysis; b) real time out of hospital ECG monitoring; c) ECG imaging; and d) single channel ECG rhythm interpretation. One speaker presented the positive aspects of each technique and another outlined the more negative aspects. Debate ensued. There were many positives set out for each technique but equally, more negative features were not in short supply, particularly for out of hospital ECG monitoring.

In patients experiencing an ST-elevation myocardial infarction (STEMI), rapid diagnosis and immediate access to reperfusion therapy leads to optimal clinical outcomes. The rate-limiting step in STEMI diagnosis is the availability and performance of a 12-lead ECG. Recent technology has provided access to a reliable means of obtaining an ECG reading through a smartphone application (app) that works with an attachment providing all 12-leads of a standard ECG system. The ST LEUIS study was designed to validate the smartphone ECG app and its ability to accurately assess the presence or absence of STEMI in patients presenting with chest pain compared with the gold standard 12-lead ECG. We aimed to support the diagnostic utility of smartphone technology to provide a timely diagnosis and treatment of STEMI. The study will take place over 12months at five institutions. Approximately 60 patients will be enrolled per institution, for a total recruitment of 300 patients.

Importance For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition. Objective To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. Design, setting, and participants A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018. Exposures Use of a deep-learning model. Main outcomes and measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. Results Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona. Conclusions and relevance In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

I read the article on the performance of two hand-held electrocardiogram devices for detecting atrial fibrillation by Desteghe et al. 1 with great interest. As the founder of AliveCor, I supplied the senior author Dr. Heidbuchel with the AliveCor device, as it was neither approved nor available for sale in Belgium at that time, and his group understood that it could be used only for clinical research purposes. It had been approved by the FDA and EC …

Hyperkalemia is commonly seen in congestive heart failure, chronic kidney disease, diabetes, and with the medications used to treat these conditions. It is associated with significant mortality and arrhythmic risk. Electrocardiogram (ECG) changes in hyperkalemia are clearly visible only in severe

Successful cleaning of medical devices and instruments requires both careful selection of materials and consistent monitoring of the procedures used to process them. Chemical characterization of residues that may be adsorbed onto surfaces and mechanical testing to ensure functionality should provide sufficient information to evaluate the potential success of cleaned and disinfected/sterilized medical devices. By using a combination of chemical and mechanical analysis techniques, both manufacturing and decontamination processes can be optimized to insure a safe and effective product. Choosing the optimal testing protocols, interpreting results, selecting the appropriate chemistries, and implementing rugged, reliable processes require experienced advice and input. It is important to note that no test, however foolproof its design, can ever be considered a definitive predictor of clinical performance. This chapter will examine various kinds of contaminants associated with medical devices and explore various analytical techniques to detect their presence or absence. The use of toxicological risk assessment as a way to set residue limits and as a method to evaluate the overall biological safety of any remaining or detected contaminants left on medical devices will be discussed.

Background 12-lead ECG is a critical component of initial evaluation of cardiac ischemia, but has traditionally been limited to large, dedicated equipment in medical care environments. Smartphones provide a potential alternative platform for the extension of ECG to new care settings and to improve timeliness of care. Objective To gain experience with smartphone electrocardiography prior to designing a larger multicenter study evaluating standard 12-lead ECG compared to smartphone ECG. Methods 6 patients for whom the hospital STEMI protocol was activated were evaluated with traditional 12-lead ECG followed immediately by a smartphone ECG using right (VnR) and left (VnL) limb leads for precordial grounding. The AliveCor™ Heart Monitor was utilized for this study. All tracings were taken prior to catheterization or immediately after revascularization while still in the catheterization laboratory. Results The smartphone ECG had excellent correlation with the gold standard 12-lead ECG in all patients. Four out of six tracings were judged to meet STEMI criteria on both modalities as determined by three experienced cardiologists, and in the remaining two, consensus indicated a non-STEMI ECG diagnosis. No significant difference was noted between VnR and VnL. Conclusions Smartphone based electrocardiography is a promising, developing technology intended to increase availability and speed of electrocardiographic evaluation. This study confirmed the potential of a smartphone ECG for evaluation of acute ischemia and the feasibility of studying this technology further to define the diagnostic accuracy, limitations and appropriate use of this new technology.

This article provides a background to the biological evaluation of medical devices. It discusses what the ISO 10993 standards require for polymeric biomaterials and presents examples of qualitative and quantitative tests that can be used to satisfy these requirements. The article describes infrared (IR) and thermal analyses that are used extensively to fingerprint polymeric materials. It also presents a discussion on the chemical characterization and risk assessment of extracts. Background information on risk assessments of extracts is also included. The four basic steps that are commonly used in the risk assessment process are discussed. These include hazard identification, dose-response assessment, and exposure assessment, and risk characterization.

A process for conformational modification of protein, which we have previously reported, was investigated as a means of generating fluorohydrolase activity in bovine ribonuclease (RNase). The resulting modified RNase had catalytic activity that depended upon the chosen modifier. Bovine pancreatic ribonuclease, modified by addition of hexamethylphosphoramide (HMPA) at pH 3, was derivatized with diimidates of chain lengths from C 1 to C 8 . The derivative with the highest activity was obtained when RNase was crosslinked with dimethyl pimelimidate (C 5 ). This derivative, which was active over a pH range of 6.5 to 8.0 with an optimum pH of 7.4, hydrolyzed phenylmethylsulfonylfluoride (PMSF) and the potent acetylcholinesterase inhibitor, diisopropyl phosphorofluoridate (DFP). The mean fluorohydrolase activity for four preparations using dimethyl pimelimidate was 0.8 ± 0.2 U mg -1 . Gel filtration on G-75 Sephadex and SDS-polyacrylamide gel electrophoresis showed components having a molecular weight of 13,000 and 27,000, with activity restricted to the 27,000 molecular weight fraction. After gel filtration, the specific activity was 9.1 ± 2.4 U mg -1 , resulting in a molecular activity of 125 min -1 . The mechanism of this unique transformation of RNase into a fluorohydrolase is not known, nor has the location of the active site been determined.

Frequency-domain techniques have been extensively investigated for the analysis of high-resolution electrocardiograms (ECGs), although the merit of frequency-domain analysis is still subject to controversy. Time-frequency analysis methods, which estimate the frequency content of a signal as a function of time, potentially provide even more information for improved ECG analysis. Some researchers report impressive results in predicting the outcome of electrophysiologic studies using the short-time Fourier transform (spectrogram). Other time-frequency representations, such as the Wigner distribution, short-time spectral estimators, and the wavelet transform, have also been investigated. The authors present a unified overview of time-frequency representations, showing that only four classes characterize most time-frequency representations. The authors describe the advantages and drawbacks of the various approaches and speculate on their promise for ECG analysis. Very preliminary experiments in applying some of these techniques to the prediction of the outcome of electrophysiologic studies have suggested some possible new research directions.

Parts 18 and 19 of ISO 10993, Biological Evaluation of Medical Devices, currently in draft, are receiving even more emphasis as they become an integral part of the biological safety evaluation of biomaterials and medical devices. An important step in this process is the characterisation of the material and identification of chemicals that can migrate or extract from the polymer components. This article discusses which tests will meet the requirements.

A method to generate catalytic activity by conformational modification of proteins is discussed and several examples elaborated. Semisynthetic amino acid esterases and glucose isomerases are discussed briefly. The most recent studies, which are described in more detail, concern semisynthetic fluorohydrolases prepared by conformational modification of bovine pancreatic ribonuclease (RNase) as well as other proteins. RNase, modified with hexamethylphosphoramide (HMPA), was derivatized with diimidates of chain lengths from one to eight carbon atoms to determine which chain length produced the maximum fluorohydrolase activity. The highest activity is observed when RNase is crosslinked with dimethyl pimelimidate. This derivative operates over a pH range of 6.5 to 8.0 with an optimum pH of approximately 7.5 and hydrolyzes phenylmethylsulfonylfluoride (PMSF) as well as the potent acetylcholinesterase inhibitor, diisopropylfluorophosphate (DFP). The mean fluorohydrolase activity, after chromatography on G-15 Sephadex to remove reactants, was 0.8 ± 0.2 U/mg.

Heart rate variability has been demonstrated both experimentally and clinically to be of prognostic importance in determining mortality after myocardial infarction. However, no paired studies have been reported to examine heart rate variability before and after myocardial infarction. The hypothesis was tested that low values of heart rate variability provided risk assessment both before and after myocardial infarction with use of an established canine model of sudden cardiac death. Risk for sudden death was assessed 1 month after myocardial infarction by a protocol in which exercise and myocardial ischemia were combined; dogs that developed ventricular fibrillation were classified at high risk for sudden death (susceptible) and the survivors were considered low risk (resistant). In resistant dogs, myocardial infarction did not affect any measure of heart rate variability: 1) mean RR interval, 2) standard deviation of the mean RR interval, and 3) the coefficient of variance (standard deviation/RR interval). By contrast, after myocardial infarction, susceptible dogs showed significant decreases in all measures of heart rate variability. Before myocardial infarction, no differences were seen between susceptible and resistant dogs. However, 30 days after infarction, epidemiologic analysis of the coefficient of variance showed high sensitivity and specificity (88% and 80%, respectively), predicting susceptibility. Therefore, results of analysis of 30 min of beat to beat heart period at rest 30 days after myocardial infarction are highly predictive for increased risk of sudden death.

The authors introduce a new technique for the analysis of ventricular late potentials: spectrotemporal mapping. Spectrotemporal mapping displays a signal in both the time and frequency domains simultaneously, overcoming some of the limitations of single domain analysis. Spectrotemporal analysis of late potentials was developed from a critique of classical spectral analysis methods. Several examples of spectrotemporal analysis of signal-averaged ECG wave-forms are presented. These include cases in which spectrotemporal mapping was able to represent late potentials that were not seen after conventional time-domain processing. Spectrotemporal mapping reveals that ventricular late potentials have a time-varying energy spectrum, which theoretically would preclude the use of classical Fourier analysis techniques. Both the vector magnitude and Fourier transformations are a reduced representation of the information available in the signal average. Spectrotemporal mapping combines time and frequency information in a way that is compatible with the basic statistical structure of late potentials.

A unique method to generate catalytic activity is described. The method consists of perturbation of a host protein, addition of a modifier, and crosslinking of the conformationally modified protein. The preparation and properties of three types of catalytic conformationally modified proteins (CCMP) are described. CCMP with amino acid esterase, glucose isomerase, and fluorohydrolase activities have been prepared from several host proteins, crosslinking reagents and modifiers. Although a fundamental understanding of this process is not known, the ease of preparation and unique properties of CCMP suggest that they will find numerous applications in biotechnology.

Peak aortic blood acceleration is recognized to be a sensitive index of global left ventricular performance. In the present study peak acceleration was assessed noninvasively in patients with a continuous-wave Doppler velocity meter. Peak aortic blood velocity and peak blood acceleration were measured by placing the ultrasonic transducer at the suprasternal notch. Measurements were obtained in 36 patients undergoing diagnostic cardiac catheterization. Peak velocity and acceleration were measured at rest just before left ventriculography. In patients with ejection fractions greater than 60%, peak acceleration was 19 +/- 5 m/sec/sec. In patients with ejection fractions of 41% to 60%, peak acceleration was lower, at 12 +/- 2 m/sec/sec (p less than .001). In patients with ejection fractions of 40% or less, peak acceleration (8 +/- 2 m/sec/sec) was markedly lower than in patients with ejection fractions greater than 60% (p less than .001). Peak acceleration showed a good linear correlation with ejection fraction (r = .90), and a better power fit (r = .93). These results indicate that peak acceleration, measured noninvasively with a continuous-wave Doppler velocity meter, is a useful indicator of global left ventricular performance.

Peak aortic blood velocity (Vel), peak acceleration (Acc), stroke volume (SV), and left ventricular (LV) ejection fraction (EF) have been used as noninvasive indicators of global LV performance. The purpose of this study was to determine which of these indices of LV performance relates best to the extent of LV ischemic mass at risk. Studies were performed in 24 open-chest anesthetized dogs. Acute ischemia was produced by occlusion of various levels of the left anterior descending and circumflex coronary arteries. LV ischemic mass, measured as a percent of total LV mass, was delineated by injection of Evans blue dye into the nonischemic zone. Acc and Vel were measured with continuous-wave Doppler ultrasound. EF was measured angiographically. All parameters were measured during a control period and within 6 minutes of coronary occlusion. The percent change during ischemia of each parameter relative to control (% delta) was calculated. The correlation coefficient between the percent ischemic mass at risk and % delta Acc was 0.88. It was 0.84 for % delta EF, 0.77 for % delta Vel, and 0.17 for % delta SV. These results indicate that among the various global indices of LV performance that have been used noninvasively, Acc correlates most closely with the extent of LV ischemic mass at risk.

An independently developed and previously validated QRS scoring system for estimating myocardial infarct size has been used to compare the development and regression of changes associated with myocardial infarcts occurring in 2 different clinical settings. It is known that QRS changes suggesting myocardial infarction occur after coronary artery bypass grafting. This study compares the magnitudes and time courses of these QRS changes in 40 patients with the QRS changes observed in a control group of 46 patients with nonoperative acute myocardial infarcts. Only patients in both groups who had a baseline electrocardiogram (ECG) with no evidence of previous myocardial infarcts, ventricular hypertrophy, or bundle branch block were included. Both groups attained similar peak QRS scores during the acute phase but different rates of resolution of scores were observed. During the subsequent 2 months, regression of QRS changes occurred more rapidly in the perioperative group than in the control group (43 versus 19%). Rates of regression were similar in both groups during the remainder of the follow-up period, attaining total decreases of 62% in the operative group and 37% in the nonoperative group by 18 months. These results could mean either that factors other than acute infarction are responsible for the perioperative QRS changes or that the infarct healing process in the 2 clinical settings are quite different.