Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study

Background

Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk.

Methods

Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk.

Results

Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

Conclusions

An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Towards federated transfer learning in electrocardiogram signal analysis

Modern methods in artificial intelligence perform very well on many healthcare datasets, at times outperforming trained doctors. However, many assumptions made in model training are not justifiable in clinical settings. In this work, we propose a method to train classifiers for electrocardiograms, able to deal with data of disparate input dimensions, distributed across different institutions, and able to protect patient privacy. In addition, we propose a simple method for creating federated datasets from any centralized dataset. We use autoencoders in conjunction with federated learning to model a highly heterogeneous modeling problem using the Massachusetts Institute of Technology Beth Israel Hospital Arrhythmia dataset, the Computing in Cardiology 2017 challenge dataset, and the PTB-XL dataset. For an encoding dimension of 1000, our federated classifier achieves an accuracy, precision, recall, and F1 score of 73.0%, 66.6%, 73.0%, and 69.7%, respectively. Our results suggest that dropping commonly made assumptions significantly complicate training and that as a result, estimates of performance of many machine learning models may overestimate performance when adopted for clinical settings.

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.

Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients

At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice.

AttentionCovidNet: Efficient ECG-based diagnosis of COVID-19

The novel coronavirus caused a worldwide pandemic. Rapid detection of COVID-19 can help reduce the spread of the novel coronavirus as well as the burden on healthcare systems worldwide. The current method of detecting COVID-19 suffers from low sensitivity, with estimates of 50%-70% in clinical settings. Therefore, in this study, we propose AttentionCovidNet, an efficient model for the detection of COVID-19 based on a channel attention convolutional neural network for electrocardiograms. The electrocardiogram is a non-invasive test, and so can be more easily obtained from a patient. We show that the proposed model achieves state-of-the-art results compared to recent models in the field, achieving metrics of 0.993, 0.997, 0.993, and 0.995 for accuracy, precision, recall, and F1 score, respectively. These results indicate both the promise of the proposed model as an alternative test for COVID-19, as well as the potential of ECG data as a diagnostic tool for COVID-19.

Activation recovery interval as an electrocardiographic repolarization index to detect doxorubicin-induced cardiotoxicity

BACKGROUND

It has been reported that early detection and treatment of cancer therapy- related cardiac dysfunction (CTRCD) improves its prognosis. The detailed relationships between electrocardiographic repolarization indices and decreased left ventricular function in CTRCD have not been elucidated. We closely assessed such relationships in patients with doxorubicin (DOX)-induced CTRCD.

METHODS

This retrospective, single-center, cohort study included 471 consecutive patients with malignant lymphoma who received chemotherapy including DOX. Of them, 17 patients with CTRCD and 68 patients without CTRCD who underwent 12‑lead electrocardiogram and an echocardiogram before and after chemotherapy were eventually analyzed. The fluctuations of the following electrocardiographic repolarization indices were evaluated in lead V5: QT, JT, T peak to T end interval (Tp-e), and activation recovery interval (ARI). These indices were corrected by heart rate with the Fridericia formula.

RESULTS

The median period from the end of chemotherapy to the diagnosis of the CTRCD group was 346 days (IQR 170-1283 days). After chemotherapy, the QT interval was significantly prolonged in both with and without CTRCD groups compared with that before chemotherapy (pre QTc vs. post QTc in CTRCD group, 386 ± 27 ms vs. 411 ± 37 ms, p = 0.03, pre QTc vs. post QTc in non-CTRCD group, 388 ± 24 ms vs. 395 ± 25 ms, p = 0.04, respectively). ARIc after chemotherapy was characteristically observed only in the CTRCD group (pre ARIc vs. post ARIc in CTRCD group, 258 ± 53 ms vs. 211 ± 28 ms, p = 0.03, pre ARIc vs. post ARIc in non-CTRCD group, 221 ± 19 ms vs. 225 ± 23 ms, NS, respectively) and had negative correlations with left ventricular ejection fraction (r = -0.56, p < 0.001). Using the receiver-operating characteristic curve, the relationship between ARIc and CTRCD morbidity was examined. The optimal cut-off point of ARIc prolongation between before and after chemotherapy was 18 ms (sensitivity 75 %, specificity 79 %, area under the curve 0.76).

CONCLUSIONS

ARIc prolongation may be useful in the early detection of developing late-onset chronic DOX-induced CTRCD and lead to early treatment for cardiac protection.

An error-bounded median filter for correcting ECG baseline wander

The baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filter CM_Filter, which works on the synopses of straight lines achieved from ECG by piecewise linear approximation (PLA) under maximum error bound. In CM_Filter, a heuristic strategy "Quick-Finding" is deduced by a property of straight lines in order to get the quality-assured median values from the synopses. The extended experimental tests demonstrate that the proposed filter is very efficient in execution time, and effective for correcting both slow and abrupt ECG baseline wander.

CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms

In the rapidly evolving landscape of modern healthcare, the integration of wearable and portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams that were previously accessible only through devices only available to healthcare providers. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. Notably, there has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multi-platform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation and care delivery. The study examines various design considerations, aligning them with specific applications, and develops data flows to maximize efficiency for research and clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake, and facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition, allowing the complete process to be completed in 63.0 to 65.7 seconds. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated and efficient strategy for leveraging 1-lead ECGs across platforms and interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.

Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG

OBJECTIVES

Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses.

METHODS

The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership-common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms.

RESULTS

The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%.

CONCLUSIONS

The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.

Beyond guideline knowledge: a theory-based qualitative study of low-value preoperative testing

BACKGROUND

Choosing Wisely Canada and most major anesthesia and preoperative guidelines recommend against obtaining preoperative tests before low-risk procedures. However, these recommendations alone have not reduced low-value test ordering. In this study, the theoretical domains framework (TDF) was used to understand the drivers of preoperative electrocardiogram (ECG) and chest X-ray (CXR) ordering for patients undergoing low-risk surgery ('low-value preoperative testing') among anesthesiologists, internal medicine specialists, nurses, and surgeons.

METHODS

Using snowball sampling, preoperative clinicians working in a single health system in Canada were recruited for semi-structured interviews about low-value preoperative testing. The interview guide was developed using the TDF to identify the factors that influence preoperative ECG and CXR ordering. Interview content was deductively coded using TDF domains and specific beliefs were identified by grouping similar utterances. Domain relevance was established based on belief statement frequency, presence of conflicting beliefs, and perceived influence over preoperative test ordering practices.

RESULTS

Sixteen clinicians (7 anesthesiologists, 4 internists, 1 nurse, and 4 surgeons) participated. Eight of the 12 TDF domains were identified as the drivers of preoperative test ordering. While most participants agreed that the guidelines were helpful, they also expressed distrust in the evidence behind them (knowledge). Both a lack of clarity about the responsibilities of the specialties involved in the preoperative process and the ease by which any clinician could order, but not cancel tests, were drivers of low-value preoperative test ordering (social/professional role and identity, social influences, belief about capabilities). Additionally, low-value tests could also be ordered by nurses or the surgeon and may be completed before the anesthesia or internal medicine preoperative assessment appointment (environmental context and resources, beliefs about capabilities). Finally, while participants agreed that they did not intend to routinely order low-value tests and understood that these would not benefit patient outcomes, they also reported ordering tests to prevent surgery cancellations and problems during surgery (motivation and goals, beliefs about consequences, social influences).

CONCLUSIONS

We identified key factors that anesthesiologists, internists, nurses, and surgeons believe influence preoperative test ordering for patients undergoing low-risk surgeries. These beliefs highlight the need to shift away from knowledge-based interventions and focus instead on understanding local drivers of behaviour and target change at the individual, team, and institutional levels.

Norwegian Endurance Athlete ECG Database

Athletes often have training-induced remodeling of the heart, and this can sometimes be seen as abnormal but non-pathological changes in the electrocardiogram. However, these changes can be confused with severe cardiovascular diseases that, in some cases, can cause cardiovascular death. Electrocardiogram data from athletes is therefore important to learn more about the difference between normal athletic remodeling and pathological remodeling of the heart. This work provides a dataset of electrocardiograms from 28 Norwegian elite endurance athletes. The electrocardiograms are standard 12-lead resting ECGs, recorded for 10 seconds while the athlete's lay supine on a bench. The electrocardiograms were then interpreted by an interpretation algorithm and by a trained cardiologist. The electrocardiogram waveform data and the interpretations were stored in Python Waveform Database format and made publicly available through PhysioNet.

Sleep disturbance exacerbates the cardiac conduction abnormalities induced by persistent heavy ambient fine particulate matter pollution: A multi-center cross-sectional study

Fine particulate matter (PM_2.5) exposure and sleep disturbance have been significantly associated with adverse cardiovascular outcomes, however, the combined effects of these two factors are still unclear. We conducted a multi-center cross-sectional study from November 2018 to May 2019 in the Beijing-Tianjin-Hebei region in China to investigate the potential modifying effects of sleep disturbance on associations between cardiac conduction abnormalities and PM_2.5 exposure, as well as the combined effects of sleep disturbance and heavy pollution episodes, which were defined based on the PM_2.5 mass concentration (≥75 μg/m³, falling in the 75th/90th percentile) and duration (1 day and ≥2 days). The sleep quality and sleep duration of all participants were evaluated using the Pittsburgh Sleep Quality Index. Standard 12-lead electrocardiogram (ECG) test was performed to measure the heart rate (HR), QRS duration (time taken for ventricular depolarization), HR corrected QT interval (time for ventricular depolarization and repolarization) and PR interval (time for atrioventricular conduction). Multivariable linear regression models were performed to evaluate the associations of PM_2.5 and heavy pollution events on ECG parameters and the joint effects with sleep disturbance. We found PM_2.5 exposure was independently associated with prolonged QRS and QTc intervals. Association between PM_2.5 and the QTc interval was significantly stronger in participants with poor sleep quality. For each 10-μg/m³ increase in PM_2.5 concentration, the QTc interval in the participants with poor sleep quality increased by 0.41 % (95 % confidence interval: 0.19, 0.64). In addition, heavy PM_2.5 pollution episodes, especially extremely heavy pollution of long duration, were found to have synergistic effects with sleep disturbance on ECG parameters. Our findings provide evidence that PM_2.5 exposure, especially heavy pollution episodes, may increase abnormal cardiac conduction and have a synergistic effect with sleep disturbance. Improving sleep hygiene is crucial to protect the heart health of the general population.

Feasibility of a Novel ECG Electrode Placement Method in Newborn Infants

BACKGROUND

International newborn resuscitation guidelines recommend electrocardiogram (ECG) heart rate (HR) monitoring at birth. We evaluated the application time of pre-set ECG electrodes fixed to a polyethene patch allowing adhesive-free attachment to the wet skin of the newborn chest.

OBJECTIVES

Using a three-electrode pre-set ECG patch configuration, application success was calculated using video analysis and measured at three time points, the time to (1) apply electrodes; (2) detect recognizable QRS complexes after application; and (3) display a HR after application.

METHOD

A prospective observational study in two UK tertiary maternity units was undertaken with 71 newborns including 23 who required resuscitation.

RESULTS

The median (IQR) time for ECG patch application was 8 (6-10) seconds, detection of recognizable QRS complexes 8 (2-12) seconds, and time to output HR was 23 (15-37) seconds.

CONCLUSION

Pre-set ECG chest electrodes allow rapid HR information at birth without electrode detachment or compromising skin integrity.

Application of artificial intelligence to the electrocardiogram

Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Real-time frequency-independent single-Lead and single-beat myocardial infarction detection

This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiography dataset, and is tested over the same and the older PTB database. By testing the model over distinct datasets, collected under different conditions and from different patients, a more realistic measure of the performance can be gauged from the deployed system. The detector is trained over 3589 myocardial infarction (MI) patients and 7115 healthy controls (HC) while it is evaluated on 1076 MIs and 1840 HCs. The proposed algorithm, achieved an accuracy of 77.12%, recall/sensitivity of 75.85%, and a specificity of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL validation set (fold 9), and 84.17%, 78.37%, 87.55% over the PTB-XL test set (fold 10). The model also achieves stable performance metrics over the frequency range of 202 Hz to 2.8 kHz. The processing time is dependent on the sampling frequency, ranging from 130 ms at 202 Hz to 1.8 s at 2.8 kHz. Such outcome is within the time required for real-time processing (less than 300 ms for fast heartbeats), between 202 Hz and 500 Hz making the algorithm practically real-time. Therefore, the proposed MI detector could be readily deployed onto existing wearable and/or portable devices and test instruments; potentially having significant societal and clinical impact in the lives of patients at risk for myocardial infarction.

Multi-level longitudinal learning curve regression models integrated with item difficulty metrics for deliberate practice of visual diagnosis: groundwork for adaptive learning

Visual diagnosis of radiographs, histology and electrocardiograms lends itself to deliberate practice, facilitated by large online banks of cases. Which cases to supply to which learners in which order is still to be worked out, with there being considerable potential for adapting the learning. Advances in statistical modeling, based on an accumulating learning curve, offer methods for more effectively pairing learners with cases of known calibrations. Using demonstration radiograph and electrocardiogram datasets, the advantages of moving from traditional regression to multilevel methods for modeling growth in ability or performance are demonstrated, with a final step of integrating case-level item-response information based on diagnostic grouping. This produces more precise individual-level estimates that can eventually support learner adaptive case selection. The progressive increase in model sophistication is not simply statistical but rather brings the models into alignment with core learning principles including the importance of taking into account individual differences in baseline skill and learning rate as well as the differential interaction with cases of varying diagnosis and difficulty. The developed approach can thus give researchers and educators a better basis on which to anticipate learners' pathways and individually adapt their future learning.

A Resident-Authored, Case-Based Electrocardiogram Email Curriculum for Internal Medicine Residents

Introduction

The interpretation of electrocardiograms (ECGs) is a critical competency for internal medicine trainees, yet time and resources to foster proficiency are limited.

Methods

This resident-authored ECG email curriculum for first-year residents involved 129 first-year internal medicine residents at three major academic university hospitals. Residents either received the resident-authored ECG email curriculum (intervention group) or continued standard training (control group). The curriculum involved 10 multiple-choice ECG cases emailed biweekly over the 6-month study period. All participants were asked to complete a pre- and postintervention test to assess ECG interpretation competency and attitudes. The primary outcome was improvement in ECG test performance.

Results

Among the 129 first-year residents participating, 21 of the 65 (32%) randomized to the intervention group and 13 of the 64 (20%) randomized to the control group completed both the pre- and posttests for analysis. While all participants' ECG test scores improved over the study period (p < .001), improvement did not differ between groups (p = .860). We found that the effect of the intervention on ECG test performance varied significantly by the number of cardiology rotations an intern experienced (p = .031), benefiting naïve learners the most. All intervention group participants who completed the posttest reported they would recommend it to a colleague.

Discussion

While it did not improve resident performance on an ECG posttest, this resident-authored ECG email curriculum offers a scalable way to provide trainees additional practice with ECG interpretation, with particular benefit to trainees who have not yet rotated on cardiology.

Electrocardiograms for cardiomyopathy risk stratification in children with anthracycline exposure

Background

Early recognition of anthracycline-induced cardiomyopathy may reduce morbidity and mortality in children, but risk stratification tools are lacking. This study evaluates whether electrocardiogram (ECG) changes precede echocardiographic abnormalities in children with anthracycline-induced cardiomyopathy.

Methods

We performed a retrospective analysis of 589 pediatric cancer patients who received anthracyclines at a tertiary referral center. ECG endpoints were sum of absolute QRS amplitudes in the 6 limb leads (ΣQRS(6 L)) and corrected QT interval (QTc). Cardiomyopathy was defined by echocardiogram as ejection fraction < 50%, shortening fraction < 26%, or left ventricular end-diastolic diameter z-score > 2.5.

Results

Median age at start of therapy was 7.8 years (IQR 3.7–13.6); median follow-up time was 3.6 years (IQR 1.1–5.8). 19.5% of patients met criteria for cardiomyopathy. Male sex, race, older age at first dose, and larger body surface area were associated with development of cardiomyopathy. A 0.6 mV decrease in ΣQRS(6 L) and 10 ms increase in QTc were associated with an increased risk of developing cardiomyopathy with hazard ratios of 1.174 (95% CI = 1.057–1.304, p = 0.003) and 1.098 (95%CI = 1.027–1.173, p = 0.006) respectively. Kaplan-Meier estimates showed a lower chance of cardiomyopathy-free survival for QTc ≥ 440 ms and ΣQRS(6 L) ≤ 3.2 mV over time. After controlling for confounders, total anthracycline dose predicted a decrease in ΣQRS(6 L) and an increase in QTc independent of cardiomyopathy status (p = 0.01 and p < 0.001 respectively). Cardiotoxic radiation did not predict changes in ECG parameters. Cardiomyopathy was associated with increased mortality (34% versus 12%, p < 0.001).

Conclusion

In children receiving anthracyclines, decrease in ΣQRS(6 L) and QTc prolongation are associated with increased risk of developing cardiomyopathy. ECG is a potential non-invasive risk stratification tool for prediction of anthracycline-induced cardiomyopathy and requires prospective validation.

Simultaneous monitoring of mouse respiratory and cardiac rates through a single precordial electrode

Normal respiratory and circulatory functions are crucial for survival. However, conventional methods of monitoring respiration, some of which use sensors inserted into the nasal cavity, may interfere with naïve respiratory rates. In this study, we conducted a single-point measurement of electrocardiograms (ECGs) from the pectoral muscles of anesthetized and waking mice and found low-frequency oscillations in the ECG baseline. Using the fast Fourier transform of simultaneously recorded respiratory signals, we demonstrated that the low-frequency oscillations corresponded to respiratory rhythms. Moreover, the baseline oscillations changed in parallel with the respiratory rhythm when the latter was altered by pharmacological manipulation. We also demonstrated that this method could be combined with in vivo whole-cell patch-clamp recordings from the hippocampus. Thus, we developed a non-invasive form of respirometry in mice. Our recording method using a simple derivation algorithm is applicable to a variety of physiological and pharmacological experiments, providing an experimental platform in studying the mechanisms underlying the interaction of the central nervous system and the peripheral functions.

Is an Abnormal ECG Just the Tip of the ICE-berg? Examining the Utility of Electrocardiography in Detecting Methamphetamine-Induced Cardiac Pathology

BACKGROUND

Methamphetamine use is escalating in Australia and New Zealand, with increasing emergency department attendance and mortality. Cardiac complications play a large role in methamphetamine-related mortality, and it would be informative to assess the frequency of abnormal electrocardiograms (ECGs) amongst methamphetamine users.

OBJECTIVE

To determine the frequency and severity of ECG abnormalities amongst methamphetamine users compared to a control group.

METHODS

We conducted a retrospective cohort analysis on 212 patients admitted to a tertiary hospital (106 patients with methamphetamine use, 106 age and gender-matched control patients). Electrocardiograms were analysed according to American College of Cardiology guidelines.

RESULTS

Mean age was 33.4 years, with 73.6% male gender, with no significant differences between groups in smoking status, ECG indication, or coronary angiography rates. Methamphetamine users were more likely to have psychiatric admissions (22.6% vs 1.9%, p<0.0001). Overall, ECG abnormalities were significantly more common (71.7% vs 32.1%, p<0.0001) in methamphetamine users, particularly tachyarrhythmias (38.7% vs 26.4%, p<0.0001), right axis deviation (7.5% vs 0.0%, p=0.004), left ventricular hypertrophy (26.4% vs 4.7%, p<0.0001), P pulmonale pattern (7.5% vs 0.9%, p=0.017), inferior Q waves (10.4% vs 0.0%, p=0.001), lateral T wave inversion (3.8% vs 0.0%, p=0.043), and longer QTc interval (436.41±31.61ms vs 407.28±24.38ms, p<0.0001). Transthoracic echocardiogram (n=24) demonstrated left ventricular dysfunction (38%), thrombus (8%), valvular lesions (17%), infective endocarditis (17%), and pulmonary hypertension (13%). Electrocardiograms were only moderately sensitive at predicting abnormal TTE.

CONCLUSION

Electrocardiographic abnormalities are more common in methamphetamine users than age and gender-matched controls. Due to the high frequency of abnormalities, ECGs should be performed in all methamphetamine users who present to hospital. Methamphetamine users with abnormal ECGs should undergo further cardiac investigations.

Preoperative Laboratory Testing

Routine preoperative testing is not cost-effective, because it is unlikely to identify significant abnormalities. Abnormal findings from routine testing are more likely to be false positive, are costly to pursue, introduce a new risk, increase the patient's anxiety, and are inconvenient to the patient. Abnormal findings rarely alter the surgical or anesthetic plan, and there is usually no association between perioperative complications and abnormal laboratory results. Incidental findings and false positive results may lead to increased hospital visits and admissions. Preoperative testing needs to be done based on a targeted history and physical examination and the type of surgery.

Lyme Arrhythmia in an Avid Golfer: A Diagnostic Challenge and a Therapeutic Dilemma

Lyme disease is a multisystem disorder affecting dermatologic, cardiac, nervous and musculoskeletal systems. Cardiac manifestations occur in about 5% of Lyme infections and stem from the involvement of the cardiac conduction system, resulting in varying degrees of sino-atrioventricular block. Occasionally, Lyme infection may also present with myopericarditis. Unlike isolated conduction node disease, myocardial involvement presents a great diagnostic and therapeutic dilemma for the physician. We report the case of a 68 year-old male cardiologist who presented with new onset exertional dyspnea and palpitations. Electrocardiograms revealed intermittent Wenckebach with markedly prolonged PR interval varying between 290-350ms. During his hospitalization, he also had a transient episode of atrial fibrillation/flutter with AV block. The patient was promptly treated with intravenous Ceftriaxone. He remained hemodynamically stable, and within 48 hours of antibiotic treatment, the patient's arrhythmias began to resolve, and the PR interval had shortened to 230ms. He was discharged on oral Doxycyline for three weeks.