Eric Helfenbein

High prevalence of corrected QT interval prolongation in acutely ill patients is associated with mortality: Results of the QT in Practice (QTIP) Study*

Objective: To test the potential value of more frequent QT interval measurement in hospitalized patients. Design: We performed a prospective, observational study. Setting: All adult intensive care unit and progressive care unit beds of a university medical center. Patients: All patients admitted to one of six critical care units over a 2-month period were included in analyses. Interventions: All critical care beds (n = 154) were upgraded to a continuous QT monitoring system (Philips Healthcare). Measurements and Main Results: QT data were extracted from the bedside monitors for offline analysis. A corrected QT interval >500 msecs was considered prolonged. Episodes of QT prolongation were manually over-read. Electrocardiogram data (67,648 hrs, mean 65 hrs/patient) were obtained. QT prolongation was present in 24%. There were 16 cardiac arrests, with one resulting from Torsade de Pointes (6%). Predictors of QT prolongation were female sex, QT-prolonging drugs, hypokalemia, hypocalcemia, hyperglycemia, high creatinine, history of stroke, and hypothyroidism. Patients with QT prolongation had longer hospitalization (276 hrs vs. 132 hrs, p < .0005) and had three times the odds for all-cause in-hospital mortality compared to patients without QT prolongation (odds ratio 2.99 95% confidence interval 1.1–8.1). Conclusions: We find QT prolongation to be common (24%), with Torsade de Pointes representing 6% of in-hospital cardiac arrests. Predictors of QT prolongation in the acutely ill population are similar to those previously identified in ambulatory populations. Acutely ill patients with QT prolongation have longer lengths of hospitalization and nearly three times the odds for mortality then those without QT prolongation.

Improvements in atrial fibrillation detection for real-time monitoring

Electrocardiographic (ECG) monitoring plays an important role in the management of patients with atrial fibrillation (AF). Automated real-time AF detection algorithm is an integral part of ECG monitoring during AF therapy. Before and after antiarrhythmic drug therapy and surgical procedures require ECG monitoring to ensure the success of AF therapy. This article reports our experience in developing a real-time AF monitoring algorithm and techniques to eliminate false-positive AF alarms. We start by designing an algorithm based on R-R intervals. This algorithm uses a Markov modeling approach to calculate an R-R Markov score. This score reflects the relative likelihood of observing a sequence of R-R intervals in AF episodes versus making the same observation outside AF episodes. Enhancement of the AF algorithm is achieved by adding atrial activity analysis. P-R interval variability and a P wave morphology similarity measure are used in addition to R-R Markov score in classification. A hysteresis counter is applied to eliminate short AF segments to reduce false AF alarms for better suitability in a monitoring environment. A large ambulatory Holter database (n = 633) was used for algorithm development and the publicly available MIT-BIH AF database (n = 23) was used for algorithm validation. This validation database allowed us to compare our algorithm performance with previously published algorithms. Although R-R irregularity is the main characteristic and strongest discriminator of AF rhythm, by adding atrial activity analysis and techniques to eliminate very short AF episodes, we have achieved 92% sensitivity and 97% positive predictive value in detecting AF episodes, and 93% sensitivity and 98% positive predictive value in quantifying AF segment duration.

Philips QT Interval Measurement Algorithms for Diagnostic, Ambulatory, and Patient Monitoring ECG Applications

Background: Commonly used techniques for QT measurement that identify T wave end using amplitude thresholds or the tangent method are sensitive to baseline drift and to variations of terminal T wave shape. Such QT measurement techniques commonly underestimate or overestimate the “true” QT interval.

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.

How many patients need QT interval monitoring in critical care units? Preliminary report of the QT in Practice study

UNLABELLED Recent Scientific Statement from the American Heart Association (AHA) recommends that hospital patients should receive QT interval monitoring if certain conditions are present: QT-prolonging drug administration or admission for drug overdose, electrolyte disturbances (K, Mg), and bradycardia. No studies have quantified the proportion of critical care patients that meet the AHAs indications for QT interval monitoring. This is a prospective study of 1039 critical care patients to determine the proportion of patients that meet the AHAs indications for QT interval monitoring. Secondary aim is to evaluate the predictive value of the AHAs indications in identifying patients who actually develop QT interval prolongation. METHODS Continuous QT interval monitoring software was installed in all monitored beds (n = 154) across 5 critical care units. This system uses outlier rejection and median filtering in all available leads to construct an root-mean-squared wave from which the QT measurement is made. Fridericia formula was used for heart rate correction. A QT interval greater than 500 milliseconds for 15 minutes or longer was considered prolonged for analyses. To minimize false positives all episodes of QT prolongation were manually over read. Clinical data was abstracted from the medical record. RESULTS Overall 69% of patients had 1 or more AHA indications for QT interval monitoring. More women (74%) had indications than men (64%, P = .001). One quarter (24%) had QT interval prolongation (>500 ms for ≥15 minutes). The odds for QT interval prolongation increased with the number of AHA indications present; 1 indication, odds ratio (OR) = 3.2 (2.1-5.0); 2 indications, OR = 7.3(4.6-11.7); and 3 or more indications OR = 9.2(4.8-17.4). Positive predictive value of the AHA indications for QT interval prolongation was 31.2%; negative predictive value was 91.3%. CONCLUSION Most critically ill patients (69%) have AHA indications for QT interval monitoring. One quarter of critically ill patients (24%) developed QT interval prolongation. The AHA indications for QT interval monitoring successfully captured the majority of critically ill patients developing QT interval prolongation.

What is inside the electrocardiograph

The details of digital recording and computer processing of a 12-lead electrocardiogram (ECG) remain a source of confusion for many health care professionals. A better understanding of the design and performance tradeoffs inherent in the electrocardiograph design might lead to better quality in ECG recording and better interpretation in ECG reading. This paper serves as a tutorial from an engineering point of view to those who are new to the field of ECG and to those clinicians who want to gain a better understanding of the engineering tradeoffs involved. The problem arises when the benefit of various electrocardiograph features is widely understood while the cost or the tradeoffs are not equally well understood. An electrocardiograph is divided into 2 main components, the patient module for ECG signal acquisition and the remainder for ECG processing which holds the main processor, fast printer, and display. The low-level ECG signal from the body is amplified and converted to a digital signal for further computer processing. The Electrocardiogram is processed for display by user selectable filters to reduce various artifacts. A high-pass filter is used to attenuate the very low frequency baseline sway or wander. A low-pass filter attenuates the high-frequency muscle artifact and a notch filter attenuates interference from alternating current power. Although the target artifact is reduced in each case, the ECG signal is also distorted slightly by the applied filter. The low-pass filter attenuates high-frequency components of the ECG such as sharp R waves and a high-pass filter can cause ST segment distortion for instance. Good skin preparation and electrode placement reduce artifacts to eliminate the need for common usage of these filters.

Where do derived precordial leads fail

A 12-lead electrocardiogram (ECG) reconstructed from a reduced subset of leads is desired in continued arrhythmia and ST monitoring for less tangled wires and increased patient comfort. However, the impact of reconstructed 12-lead lead ECG on clinical ECG diagnosis has not been studied thoroughly. This study compares the differences between recorded and reconstructed 12-lead diagnostic ECG interpretation with 2 commonly used configurations: reconstruct precordial leads V(2), V(3), V(5), and V(6) from V(1),V(4), or reconstruct V(1), V(3), V(4), and V(6) from V(2),V(5). Limb leads are recorded in both configurations. A total of 1785 ECGs were randomly selected from a large database of 50,000 ECGs consecutively collected from 2 teaching hospitals. ECGs with extreme artifact and paced rhythm were excluded. Manual ECG annotations by 2 cardiologists were categorized and used in testing. The Philips resting 12-lead ECG algorithm was used to generate computer measurements and interpretations for comparison. Results were compared for both arrhythmia and morphology categories with high prevalence interpretations including atrial fibrillation, anterior myocardial infarct, right bundle-branch block, left bundle-branch block, left atrial enlargement, and left ventricular hypertrophy. Sensitivity and specificity were calculated for each reconstruction configuration in these arrhythmia and morphology categories. Compared to recorded 12-leads, the V(2),V(5) lead configuration shows weakness in interpretations where V(1) is important such as atrial arrhythmia, atrial enlargement, and bundle-branch blocks. The V(1),V(4) lead configuration shows a decreased sensitivity in detection of anterior myocardial infarct, left bundle-branch block (LBBB), and left ventricular hypertrophy (LVH). In conclusion, reconstructed precordial leads are not equivalent to recorded leads for clinical ECG diagnoses especially in ECGs presenting rhythm and morphology abnormalities. In addition, significant accuracy reduction in ECG interpretation is not strongly correlated with waveform differences between reconstructed and recorded 12-lead ECGs.

Development of three methods for extracting respiration from the surface ECG: A review

BACKGROUND Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration. Monitoring respiration signals is also critical for detecting sleep disordered breathing such as sleep apnea. Additionally, analyzing a respiration signal can enhance the quality of medical images by gating image acquisition based on the same phase of the patients respiratory cycle. Although many methods exist for measuring respiration, in this review we focus on three ECG-derived respiration techniques we developed to obtain respiration from an ECG signal. METHODS The first step in all three techniques is to analyze the ECG to detect beat locations and classify them. 1) The EDR method is based on analyzing the heart axis shift due to respiration. In our method, one respiration waveform value is calculated for each normal QRS complex by measuring the peak to QRS trough amplitude. Compared to other similar EDR techniques, this method does not need removal of baseline wander from the ECG signal. 2) The RSA method uses instantaneous heart rate variability to derive a respiratory signal. It is based on the observed respiratory sinus arrhythmia governed by baroreflex sensitivity. 3) Our EMGDR method for computing a respiratory waveform uses measurement of electromyogram (EMG) activity created by respiratory effort of the intercostal muscles and diaphragm. The ECG signal is high-pass filtered and processed to reduce ECG components and accentuate the EMG signal before applying RMS and smoothing. RESULTS Over the last five years, we have performed six studies using the above methods: 1) In 1907 sleep lab patients with >1.5M 30-second epochs, EDR achieved an apnea detection accuracy of 79%. 2) In 24 adult polysomnograms, use of EDR and chest belts for RR computation was compared to airflow RR; mean RR error was EDR: 1.8±2.7 and belts: 0.8±2.1. 3) During cardiac MRI, a comparison of EMGDR breath locations to the reference abdominal belt signal yielded sensitivity/PPV of 94/95%. 4) Another comparison study for breath detection during MRI yielded sensitivity/PPV pairs of EDR: 99/97, RSA: 79/78, and EMGDR: 89/86%. 5) We tested EMGDR performance in the presence of simulated respiratory disease using CPAP to produce PEEP. For 10 patients, no false breath waveforms were generated with mild PEEP, but they appeared in 2 subjects at high PEEP. 6) A patient monitoring study compared RR computation from EDR to impedance-derived RR, and showed that EDR provides a near equivalent RR measurement with reduced hardware circuitry requirements.

Technical challenges and future directions in lead reconstruction for reduced-lead systems

Reduced-lead electrocardiographic systems are currently a widely accepted medical technology used in a number of applications. They provide increased patient comfort and superior performance in arrhythmia and ST monitoring. These systems have unique and compelling advantages over the traditional multichannel monitoring lead systems. However, the design and development of reduced-lead systems create numerous technical challenges. This article summarizes the major technical challenges commonly encountered in lead reconstruction for reduced-lead systems. We discuss the effects of basis lead and target lead selections, the differences between interpolated vs extrapolated leads, the database dependency of the coefficients, and the approaches in quantitative performance evaluation, and provide a comparison of different lead systems. In conclusion, existing reduced-lead systems differ significantly in regard to trade-offs from the technical, practical, and clinical points of view. Understanding the technical limitations, the strengths, and the trade-offs of these reduced-lead systems will hopefully guide future research.

Philips medical systems support for open ECG and standardization efforts

Philips Medical Systems developed a new ECG data formar based on XML technology in response to increased demands fir ECG data access and better ECG device interoperability. Philips has published the schema for the XML-based ECG format that is used by its electrocardiographs, bedside monitors, and defibrillators. To assist access to ECG data, Philips provides users with U set of soJnvare tools for XML-ECG wuveforna manipulation and display. in addition, Philips Medical Systems has been strongly supporting all national and international initiatives that are underway to standardize ECG formats and improve interoperabiliiy between diagnostic ECG devices and healthcare information systems.