The Computerized ECG: Friend and Foe

Drug repurposing for parasitic protozoan diseases

Protozoan parasites are major hazards to human health, society, and the economy, especially in equatorial regions of the globe. Parasitic diseases, including leishmaniasis, malaria, and others, contribute towards majority of morbidity and mortality. Around 1.1 million people die from these diseases annually. The lack of licensed vaccinations worsens the worldwide impact of these diseases, highlighting the importance of safe and effective medications for their prevention and treatment. However, the appearance of drug resistance in parasites continuously affects the availability of medications. The demand for novel drugs motivates global antiparasitic drug discovery research, necessitating the implementation of many innovative ways to maintain a continuous supply of promising molecules. Drug repurposing has come out as a compelling tool for drug development, offering a cost-effective and efficient alternative to standard de novo approaches. A thorough examination of drug repositioning candidates revealed that certain drugs may not benefit significantly from their original indications. Still, they may exhibit more pronounced effects in other disorders. Furthermore, certain medications can produce a synergistic effect, resulting in enhanced therapeutic effectiveness when given together. In this chapter, we outline the approaches employed in drug repurposing (sometimes referred to as drug repositioning), propose novel strategies to overcome these hurdles and fully exploit the promise of drug repurposing. We highlight a few major human protozoan diseases and a range of exemplary drugs repurposed for various protozoan infections, providing excellent outcomes for each disease.

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.

Visual or computer-based measurements: Which is important for the interpretation of an athlete's electrocardiography?

OBJECTIVE

Preparticipation screening of athletes by electrocardiography is the most crucial step in determining sudden cardiac death risk factors. Several electrocardiography interpretation software programs have been developed for physicians practicing in this field. Our study aimed to assess cardiopoint sudden death screening module by comparing its findings with two cardiologists using Seattle and International criteria.

METHODS

A total of 303 licensed national athletes (37% females) were enrolled. electrocardiographies were examined by the cardiopoint sudden death screening module using Seattle criteria and cardiologists. The consistency between cardiologists and software was compared, and the confidence assessment of the module was tested.

RESULTS

With regard to Seattle criteria, moderate consistency was found between the cardiopoint sudden death screening module and the 1st (κ=0.41) and 2nd cardiologist (κ=0.59). Consistency between two cardiologists was moderate (κ=0.55). When we applied International criteria, there was moderate consistency between the module and the 1st cardiologist (κ=0.42), and good consistency between the module and the 2nd cardiologist (κ=0.63). Consistency between the two cardiologists was good (κ=0.62).

CONCLUSION

The cardiopoint sudden death screening module had similar agreement with cardiologists based on both criteria. However, the software needs to be updated according to International criteria. Using computer-based measurements for preparticipation screening will help to save time and provide standardization of electrocardiography interpretation.

The use of digital health in heart rhythm care

INTRODUCTION

Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to improve patient health and health care services. With the growing use of wearables, artificial intelligence, machine learning, and other novel technologies, digital health is particularly relevant to the field of cardiac arrhythmias, with roles pertinent to education, prevention, diagnosis, management, prognosis, and surveillance.

AREAS COVERED

This review summarizes information on the clinical use of digital health technology in arrhythmia care and discusses its opportunities and challenges.

EXPERT OPINION

Digital health has begun to play an essential role in arrhythmia care regarding diagnostics, long-term monitoring, patient education and shared decision making, management, medication adherence, and research. Despite remarkable advances, integrating digital health technologies into healthcare faces challenges, including patient usability, privacy, system interoperability, physician liability, analysis and incorporation of the huge amount of real-time information from wearables, and reimbursement. Successful implementation of digital health technologies requires clear objectives and deep changes to existing workflows and responsibilities.

Pseudo loss of capture on 12 lead electrocardiogram in patient with an implantable cardiac defibrillator

This case report presents a 70-year-old-male, brought in by ambulance for assessment of chest pain and presyncope, with a paced ECG showing possible non-capture following ventricular pacing spikes. This was demonstrated to be pseudo non-capture with a 12-lead electrocardiogram performed in emergency and a device interrogation demonstrating ventricular capture.

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.

[Clinical characteristics and care times in a chest pain unit of the emergency department of an argentine center]

Objectives

. To report the frequency of precordial pain, describe clinical characteristics, and care times.

Methods

. Retrospective descriptive study that included consultations in the Chest Pain Unit in 2021 in the emergency department of a private hospital in Argentina.

Results

There were 1469 admissions for chest pain, yielding a frequency of 1.09% (95%CI 1.04-1.15). They were 52% men, mean age 62 years (SD ±15); 48% had hypertension and 32% dyslipidemia. The median time to initial ECG was 4.3 min (ICR 2.5-7.5); and 26 min (ICR 14-46) to medical evaluation. A total of 206 (14%) were hospitalized with a median of 3 days, 76% were admitted to a closed unit, 9% required non-invasive ventilation/mechanical ventilaction and in-hospital mortality was 2.9%. Those hospitalized presented shorter delay time to medical attention (p<0.01), and greater performance of complementary studies (p<0.01), with no differences in time to ECG (p=0.22).

Conclusions

Care times were within the stipulated standards, being an important indicator of quality. Nursing was crucial, taking care of the correct triage, ECG on admission, and guaranteeing care until medical evaluation.

Electronic monitoring of potential adverse drug events related to lopinavir/ritonavir and hydroxychloroquine during the first wave of COVID-19

During Switzerland's first wave of COVID-19, clinical pharmacy activities during medical rounds in Geneva University Hospitals were replaced by targeted remote interventions. We describe using the electronic PharmaCheck system to screen high-risk situations of adverse drug events (ADEs), particularly targeting prescriptions of lopinavir/ritonavir (LPVr) and hydroxychloroquine (HCQ) in the presence of contraindications or prescriptions outside institutional guidelines. Of 416 patients receiving LPVr and/or HCQ, 182 alerts were triggered for 164 (39.4%) patients. The main associated risk factors of ADEs were drug-drug interactions, QTc interval prolongation, electrolyte disorder and inadequate LPVr dosage. Therapeutic optimisation recommended by a pharmacist or proposals for additional monitoring were accepted in 80% (n=36) of cases. Combined with pharmacist contextualisation to the clinical context, PharmaCheck made it possible to successfully adapt clinical pharmacist activities by switching from a global to a targeted analysis mode in an emergency context.

Role of obesity and blood pressure in epicardial adipose tissue thickness in children

BACKGROUND

Elevated body mass index (BMI) has been associated with cardiac changes, such as higher epicardial adipose tissue (EAT) thickness. This fat has been identified as a predictive factor of cardiovascular diseases during adulthood. However, few studies have tested the association of multiple cardiovascular risk factors (high weight or blood pressure) with EAT in adolescents and children. Therefore, the main objective of this current research was to determine the impact of BMI, overweight, obesity, and blood pressure on EAT thickness in children.

METHODS

A descriptive cross-sectional study focused on elementary and high school students aged 6-16 years was carried out by utilizing diverse measurements and instruments, such as echocardiography.

RESULTS

EAT thickness (N = 228) was linked to sex (more predominant in boys 2.3 ± 0.6; p = 0.044), obesity (2.3 ± 0.6; p < 0.001), and hypertension (2.6 ± 0.6; p = 0.036). The logistic regression indicated that age, sex, and BMI seemed to be more relevant factors in EAT thickness in children (adjusted R square = 0.22; p < 0.001).

CONCLUSIONS

This paper examined the associations of sex, age, and cardiovascular risk factors (arthrometric measures and blood pressure) with EAT thickness, indicating that it is necessary to assess whether the findings are associated with future events.

IMPACT

Excessive weight gain and blood pressure in the early stages of life have been associated with adipose tissue. This increase in weight and blood pressure has been attributed to alterations in the epicardial adipose tissue linked to anthropometric markers in adults, but no related study has been implemented in Spanish children. This study revealed how higher epicardial adipose tissue is linked to body mass index, other anthropometric parameters, and blood pressure in Spanish children. These measurements are related to high epicardial adipose tissue thickness, which in early stages does not imply pathology but increases the risk of developing cardiovascular diseases.

Smart Pacemaker: A Review

Since the first pacemaker was implanted, nearly 60 years have passed. Since then, pacemaker technology has made major advancements that have increased both its safety and effectiveness in treating people with bradyarrhythmias. The repeated stimulation of cells in specialized "pacemaker" regions of the mammalian heart and the transmission of stimulus via the ventricles serve as evidence that the electrical function of the mammalian heart is necessary for a regular mechanical (pump) role. The development of action potentials in individual cardiac cells is linked to myocardial electrical activity and the heart's regular cooperative electrical functioning. A container or pulse initiator that houses the battery and electronics, as well as lines that connect to the myocardium to deliver a depolarizing pulse and detect intrinsic cardiac stimulation, are all parts of a pacemaker. Defibrillators could be used with artificial hearts that have electrical pacemakers integrated into them in order to treat arrhythmia, heart failure, and cardiac arrest. Modern pacemakers have units for supporting patients with other disorders like "heart failure," which happens when the heart does not pump as forcefully as it should. While many pacemakers are effective in treating different types of arrhythmias (irregular heartbeats), they also have units for treating them.

Prevalence and Clinical Impact of Electrocardiographic Abnormalities in Patients with Chronic Kidney Disease

Chronic kidney disease (CKD) is a strong risk factor for cardiovascular disease. An electrocardiogram (ECG) is a basic test for screening cardiovascular disease. However, the impact of ECG abnormalities on cardiovascular prognosis in patients with CKD is largely unknown. A total of 2442 patients with CKD (stages 3−5) who underwent ECG between 2013 and 2015 were selected from the electronic health record database of the Korea University Anam Hospital. ECG abnormalities were defined using the Minnesota classification. The five-year major adverse cerebrocardiovascular event (MACCE), the composite of death, myocardial infarction (MI), and stroke were analyzed. The five-year incidences for MACCE were 27.7%, 20.8%, and 17.2% in patients with no, minor, and major ECG abnormality (p < 0.01). Kaplan−Meier curves also showed the highest incidence of MI, death, and MACCE in patients with major ECG abnormality. Multivariable Cox regression analysis revealed age, sex, diabetes, CKD stage, hsCRP, antipsychotic use, and major ECG abnormality as independent risk predictors for MACCE (adjusted HR of major ECG abnormality: 1.39, 95% CI: 1.09−1.76, p < 01). Among the detailed ECG diagnoses, sinus tachycardia, myocardial ischemia, atrial premature complex, and right axis deviation were proposed as important ECG diagnoses. The accuracy of cardiovascular risk stratification was improved when the ECG results were added to the conventional SCORE model (net reclassification index 0.07). ECG helps to predict future cerebrocardiovascular events in CKD patients. ECG diagnosis can be useful for cardiovascular risk evaluation in CKD patients when applied in addition to the conventional risk stratification model.

Device agnostic AI-based analysis of ambulatory ECG recordings

Deep Convolutional Neural Networks (DCNNs) have been shown to provide improved performance over traditional heuristic algorithms for the detection of arrhythmias from ambulatory ECG recordings. However, these DCNNs have primarily been trained and tested on device-specific databases with standardized electrode positions and uniform sampling frequencies. This work explores the possibility of training a DCNN for Atrial Fibrillation (AF) detection on a database of single‑lead ECG rhythm strips extracted from resting 12‑lead ECGs. We then test the performance of the DCNN on recordings from ambulatory ECG devices with different recording leads and sampling frequencies. We developed an extensive proprietary resting 12‑lead ECG dataset of 549,211 patients. This dataset was randomly split into a training set of 494,289 patients and a testing set of the remaining 54,922 patients. We trained a 34-layer convolutional DCNN to detect AF and other arrhythmias on this dataset. The DCNN was then validated on two Physionet databases commonly used to benchmark automated ECG algorithms (1) MIT-BIH Arrhythmia Database and (2) MIT-BIH Atrial Fibrillation Database. Validation was performed following the EC57 guidelines, with performance assessed by gross episode and duration sensitivity and positive predictive value (PPV). Finally, validation was also performed on a selection of rhythm strips from an ambulatory ECG patch that a committee of board-certified cardiologists annotated. On MIT-BIH, The DCNN achieved a sensitivity of 100% and 84% PPV in detecting episodes of AF. and 100% sensitivity and 94% PPV in quantifying AF episode duration. On AFDB, The DCNN achieved a sensitivity of 94% and PPV of 98% in detecting episodes of AF, and 98% sensitivity and 100% PPV in quantifying AF episode duration. On the patch database, the DCNN demonstrated performance that was closely comparable to that of a cardiologist. The results indicate that DCNN models can learn features that generalize between resting 12‑lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single‑lead ECG recordings and enabling a range of clinical applications.

Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary

Background  A computerized 12-lead electrocardiogram (ECG) can automatically generate diagnostic statements, which are helpful for clinical purposes. Standardization is required for big data analysis when using ECG data generated by different interpretation algorithms. The common data model (CDM) is a standard schema designed to overcome heterogeneity between medical data. Diagnostic statements usually contain multiple CDM concepts and also include non-essential noise information, which should be removed during CDM conversion. Existing CDM conversion tools have several limitations, such as the requirement for manual validation, inability to extract multiple CDM concepts, and inadequate noise removal.

Objectives  We aim to develop a fully automated text data conversion algorithm that overcomes limitations of existing tools and manual conversion.

Methods  We used interpretations printed by 12-lead resting ECG tests from three different vendors: GE Medical Systems, Philips Medical Systems, and Nihon Kohden. For automatic mapping, we first constructed an ontology-lexicon of ECG interpretations. After clinical coding, an optimized tool for converting ECG interpretation to CDM terminology is developed using term-based text processing.

Results  Using the ontology-lexicon, the cosine similarity-based algorithm and rule-based hierarchical algorithm showed comparable conversion accuracy (97.8 and 99.6%, respectively), while an integrated algorithm based on a heuristic approach, ECG2CDM, demonstrated superior performance (99.9%) for datasets from three major vendors.

Conclusion  We developed a user-friendly software that runs the ECG2CDM algorithm that is easy to use even if the user is not familiar with CDM or medical terminology. We propose that automated algorithms can be helpful for further big data analysis with an integrated and standardized ECG dataset.

Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models

Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF-related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3-year new-onset AF prediction (C-statistic, 0.796 [95% CI, 0.785-0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C-statistic, 0.777 [95% CI, 0.766-0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3-year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

NLM’s sponsorship of research in biomedical informatics (1985–2016)

The U.S. National Library of Medicine’s (NLM) funding for biomedical informatics research in the 1980s and 1990s focused on clinical decision support systems, which were also the focus of research for Donald A.B. Lindberg M.D. prior to becoming NLM’s director. The portfolio of projects expanded over the years. At NLM, Dr. Lindberg supported various large infrastructure programs that enabled biomedical informatics research, as well as investigator-initiated research projects that increasingly included biotechnology/bioinformatics and health services research. The authors review NLM’s sponsorship of research during Dr. Lindberg’s tenure as its Director. NLM’s funding significantly increased in the 2000’s and beyond. Authors report an analysis of R01 topics from 1985–2016 using data from NIH RePORTER. Dr. Lindberg’s legacy for biomedical informatics research is reflected by the research NLM supported under his leadership. The number of R01s remained steady over the years, but the funds provided within awards increased over time. A significant amount of NLM funds listed in RePORTER went into various types of infrastructure projects that laid a solid foundation for biomedical informatics research over multiple decades.

Why Machine Learning Should Be Taught in Medical Schools

The rapid development of machine learning (ML) applications in healthcare promises to transform the landscape of healthcare. In order for ML advancements to be effectively utilized in clinical care, it is necessary for the medical workforce to be prepared to handle these changes. As physicians in training are exposed to a wide breadth of clinical tools during medical school, this offers an ideal opportunity to introduce ML concepts. A foundational understanding of ML will not only be practically useful for clinicians, but will also address ethical concerns for clinical decision making. While select medical schools have made effort to integrate ML didactics and practice into their curriculum, we argue that foundational ML principles should be taught broadly to medical students across the country.

Analysis of the accuracy of automatic electrocardiogram interpretation in ST-segment elevation myocardial infarction

Objective

This study aimed to analyze the association between the culprit artery and the diagnostic accuracy of automatic electrocardiogram (ECG) interpretation in patients with ST-segment elevation myocardial infarction (STEMI).

Methods

This single-centered, retrospective cohort study included adult patients with STEMI who visited the emergency department between January 2017 and December 2020. The primary endpoint was the association between the culprit artery occlusion and the misinterpretation of ECG, evaluated by the chi-square test or Fisher exact test.

Results

The rate of misinterpretation of the automated ECG for patients with STEMI was 26.5% (31/117 patients). There was no significant correlation between the ST segment change in the four involved leads (anteroseptal, lateral, inferior, and aVR) and the misinterpretation of ECG (all P > 0.05). Single culprit artery occlusion significantly affected the misinterpretation of ECG compared with multiple culprit artery occlusion (single vs. multiple, 27/86 [31.3%] vs. 4/31 [12.9%], P = 0.045). There was no association between culprit artery and the misinterpretation of ECG (P = 0.132).

Conclusion

Single culprit artery occlusion might increase misinterpretation of ECG compared with multiple culprit artery occlusions in the automatic interpretation of STEMI.

Cardiovascular waveforms - can we extract more from routine signals?

Cardiovascular waveforms such as blood pressure, ECG and photoplethysmography (PPG), are routinely acquired by specialised monitoring devices. Such devices include bedside monitors, wearables and radiotelemetry which sample at very high fidelity, yet most of this numerical data is disregarded and focus tends to reside on single point averages such as the maxima, minima, amplitude, rate and intervals. Whilst, these measures are undoubtedly of value, we may be missing important information by simplifying the complex waveform signal in this way. This Special Collection showcases recent advances in the appraisal of routine signals. Ultimately, such approaches and technologies may assist in improving the accuracy and sensitivity of detecting physiological change. This, in turn, may assist with identifying efficacy or safety signals for investigational new drugs or aidpatient diagnosis and management, supporting scientific and clinical decision making.

Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology

BACKGROUND

Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations.

OBJECTIVES

Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied.

METHODS

An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports.

RESULTS

Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance.

CONCLUSION

The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.

Assessment of QTc and Risk of Torsades de Pointes in Ventricular Conduction Delay and Pacing: A Review of the Literature and Call to Action

BACKGROUND

Assessment of the heart rate-corrected QT-interval on the 12-lead electrocardiogram when prescribing medications known to increase the risk of Torsades de Pointes has become a common part of consultation-liaison psychiatry practice.

OBJECTIVES

Highlighted by a patient who experienced psychiatric decompensation due to inaccurate interpretation of QTc prolongation in the setting of a wide QRS complex, we aimed to describe the approach to QTc interpretation in patients with ventricular conduction delay.

METHODS

We reviewed the current literature on the approach to assessment of prolonged repolarization in patients with ventricular conduction delay due to bundle branch block (BBB) and ventricular pacing.

RESULTS

Physicians of any specialty may perform initial electrocardiogram interpretation and should be proficient in the definition, recognition, and understanding of the basic pathophysiology of electrocardiographic abnormalities. We discuss current approaches to assessment of the QT-interval in patients with a wide QRS complex due to bundle branch block and ventricular pacing, including bivariate QTc modification, univariate QT-interval modifications, and use of the JT-interval.

CONCLUSIONS

The QT-interval is prolonged ipso facto in patients with a wide QRS complex from ventricular conduction delay/ventricular pacing and must be adjusted for QRS duration. Multiple formulae have been proposed to account for wide QRS complex in this setting with no single universally accepted methodology. We suggest the use of either the Bogossian formula or JT-interval followed by Hodges or Framingham heart-rate correction to adjust for a wide QRS complex. It is critical that the C-L psychiatrist be able to identify a wide QRS complex on the electrocardiogram, understand implications for accurate assessment of prolonged depolarization, and apply an appropriate correction methodology.

The New ISO/IEC Standard for Automated ECG Interpretation

Updates to industry consensus standards for ECG equipment is a work-in-progress by the ISO/IEC Joint Work Group 22. This work will result in an overhaul of existing industry standards that apply to ECG electromedical equipment and will result in a new single international industry, namely 80601-2-86. The new standard will be entitled “80601, Part 2-86: Particular requirements for the basic safety and essential performance of electrocardiographs, including diagnostic equipment, monitoring equipment, ambulatory equipment, electrodes, cables, and leadwires”. This paper will provide a high-level overview of the work in progress and, in particular, will describe the impact it will have on requirements and testing methods for computerized ECG interpretation algorithms. The conclusion of this work is that manufacturers should continue working with clinical ECG experts to make clinically meaningful improvements to automated ECG interpretation, and the clinical validation of ECG analysis algorithms should be disclosed to guide appropriate clinical use. More cooperation is needed between industry, clinical ECG experts and regulatory agencies to develop new data sets that can be made available for use by industry standards for algorithm performance evaluation.

Machine learning with electrocardiograms: A call for guidelines and best practices for 'stress testing' algorithms

This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer manually writes computer code and logical statements which are then used to deduce a possible diagnosis. The computer programmer's code represents the criteria and knowledge that is used by clinicians when reading ECGs. This is in contrast to supervised machine learning (ML) approaches which use large, labelled ECG datasets to induct their own 'rules' to automatically classify ECGs. Although there are many ML techniques, deep neural networks are being increasingly explored as ECG classification algorithms when trained on large ECG datasets. Whilst this paper presents some of the pros and cons of each of these approaches, perhaps there are opportunities to develop hybridised algorithms that combine both knowledge and data driven techniques. In this paper, it is pointed out that open ECG data can dramatically influence what international ECG ML researchers focus on and that, ideally, open datasets could align with real world clinical challenges. In addition, some of the pitfalls and opportunities for ML with ECGs are outlined. A potential opportunity for the ECG community is to provide guidelines to researchers to help guide ECG ML practices. For example, whilst general ML guidelines exist, there is perhaps a need to recommend approaches for 'stress testing' and evaluating ML algorithms for ECG analysis, e.g. testing the algorithm with noisy ECGs and ECGs acquired using common lead and electrode misplacements. This paper provides a primer on ECG ML and discusses some of the key challenges and opportunities.

Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms

OBJECTIVE

Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.

METHODS

A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.

RESULTS

A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.

CONCLUSIONS

The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.

Automated Classification of Radiographic Positioning of Hand X-Rays Using a Deep Neural Network

Background:

Hand X-rays are ordered in outpatient, inpatient, and emergency settings, the results of which are often initially interpreted by non-radiology trained health care providers. There may be utility in automating upper extremity X-ray analysis to aid with rapid initial analysis. Deep neural networks have been effective in several medical imaging analysis applications. The purpose of this work was to apply a deep learning framework to automatically classify the radiographic positioning of hand X-rays.

Methods:

A 152-layer deep neural network was trained using the musculoskeletal radiographs data set. This data set contains 6003 hand X-rays. The data set was filtered to remove pediatric X-rays and atypical views. The X-rays were all labeled as either posteroanterior (PA), lateral, or oblique views. A subset of images was set aside for model validation and testing. Data set augmentation was performed, including horizontal and vertical flips, rotations, as well as modifications in image brightness and contrast. The model was evaluated, and performance was reported as a confusion matrix from which accuracy, precision, sensitivity, and specificity were calculated.

Results:

The augmented training data set consisted of 80 672 images. Their distribution was 38% PA, 35% lateral, and 27% oblique projections. When evaluated on the test data set, the model performed with overall 96.0% accuracy, 93.6% precision, 93.6% sensitivity, and 97.1% specificity.

Conclusions:

Radiographic positioning of hand X-rays can be effectively classified by a deep neural network. Further work will be performed on localization of abnormalities, automated assessment of standard radiographic measures and eventually on computer-aided diagnosis and management guidance of skeletal pathology.

Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

Digital Health and the Care of the Patient With Arrhythmia

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

Accuracy of Physicians' Electrocardiogram Interpretations: A Systematic Review and Meta-analysis

IMPORTANCE

The electrocardiogram (ECG) is the most common cardiovascular diagnostic test. Physicians' skill in ECG interpretation is incompletely understood.

OBJECTIVES

To identify and summarize published research on the accuracy of physicians' ECG interpretations.

DATA SOURCES

A search of PubMed/MEDLINE, Embase, Cochrane CENTRAL (Central Register of Controlled Trials), PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health), ERIC (Education Resources Information Center), and Web of Science was conducted for articles published from database inception to February 21, 2020.

STUDY SELECTION

Of 1138 articles initially identified, 78 studies that assessed the accuracy of physicians' or medical students' ECG interpretations in a test setting were selected.

DATA EXTRACTION AND SYNTHESIS

Data on study purpose, participants, assessment features, and outcomes were abstracted, and methodological quality was appraised with the Medical Education Research Study Quality Instrument. Results were pooled using random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

Accuracy of ECG interpretation.

RESULTS

Of 1138 studies initially identified, 78 assessed the accuracy of ECG interpretation. Across all training levels, the median accuracy was 54% (interquartile range [IQR], 40%-66%; n = 62 studies) on pretraining assessments and 67% (IQR, 55%-77%; n = 47 studies) on posttraining assessments. Accuracy varied widely across studies. The pooled accuracy for pretraining assessments was 42.0% (95% CI, 34.3%-49.6%; n = 24 studies; I2 = 99%) for medical students, 55.8% (95% CI, 48.1%-63.6%; n = 37 studies; I2 = 96%) for residents, 68.5% (95% CI, 57.6%-79.5%; n = 10 studies; I2 = 86%) for practicing physicians, and 74.9% (95% CI, 63.2%-86.7%; n = 8 studies; I2 = 22%) for cardiologists.

CONCLUSIONS AND RELEVANCE

Physicians at all training levels had deficiencies in ECG interpretation, even after educational interventions. Improved education across the practice continuum appears warranted. Wide variation in outcomes could reflect real differences in training or skill or differences in assessment design.

Machine Learning in Electrocardiography and Echocardiography: Technological Advances in Clinical Cardiology

PURPOSE OF REVIEW

Electrocardiography (ECG) and echocardiography are the most widely used diagnostic tools in clinical cardiology. This review focuses on recent advancements in applying machine learning (ML) in ECG and echocardiography and potential synergistic ML integration of ECG and echocardiography.

RECENT FINDINGS

ML algorithms have been used in ECG for technical quality assurance, arrhythmia identification, and prognostic predictions, and in echocardiography to recognize image views, quantify measurements, and identify pathologic patterns. Synergistic application of ML in ECG and echocardiograph has demonstrated the potential to optimize therapeutic response, improve risk stratification, and generate new disease classification. There is mounting evidence that ML potentially outperforms in disease diagnoses and outcome prediction with ECG and echocardiography when compared with trained healthcare professionals. The applications of ML in ECG and echocardiography are playing increasingly greater roles in medical research and clinical practice, particularly for their contributions to developing novel diagnostic/prognostic prediction models. The automation in data acquisition, processing, and interpretation help streamline the workflows of ECG and echocardiography in contemporary cardiology practice.

"Off-label" use of hydroxychloroquine, azithromycin, lopinavir-ritonavir and chloroquine in COVID-19: A survey of cardiac adverse drug reactions by the French Network of Pharmacovigilance Centers

INTRODUCTION

COVID-19 is an unprecedented challenge for physicians and scientists. Several publicized drugs are being used with not much evidence of their efficacy such as hydroxychloroquine, azithromycin or lopinavir-ritonavir. Yet, the cardiac safety of these drugs in COVID-19 deserves scrutiny as they are known to foster cardiac adverse ADRs, notably QTc interval prolongation on the electrocardiogram and its arrhythmogenic consequences.

METHODS

Since March 27th, 2020, the French Pharmacovigilance Network directed all cardiac adverse drug reactions associated with "off-label" use of hydroxychloroquine, azithromycin and lopinavir-ritonavir in COVID-19 to the Nice Regional Center of Pharmacovigilance. Each Regional Center of Pharmacovigilance first assessed causality of drugs. We performed a specific analysis of these cardiac adverse drug reactions amidst an array of risk factors, reassessed the electrocardiograms and estimated their incidence in coronavirus disease 2019.

RESULTS

In one month, 120 reports of cardiac adverse drug reactions have been notified, 103 of which associated with hydroxychloroquine alone (86%), or associated with azithromycin (60%). Their estimated incidence is 0.77% to 1.54% of all patients, notwithstanding strong underreporting. Lopinavir-ritonavir came third with 17 reports (14%) and chloroquine fourth with 3 reports (2.5%). There were 8 sudden, unexplained or aborted deaths (7%), 8 ventricular arrhythmias (7%), 90 reports of prolonged QTc (75%) most of them "serious" (64%), 48 of which proved ≥ 500ms, 20 reports of severe conduction disorders (17%) and 5 reports of other cardiac causes (4%). Six reports derived from automedication.

DISCUSSION AND CONCLUSION

"Off-label" use of treatments in COVID-19 increases the risk of cardiac ADRs, some of them avoidable. Even if these drugs are perceived as familiar, they are used in patients with added risk factors caused by infection. Precautions should be taken to mitigate the risk, even if they will be proven efficacious.

A Sustainable Approach to the Metabolic Syndrome in Children and Its Economic Burden

The prevalence of obesity continues to grow, resulting in metabolic syndrome and increasing economic burden for health systems. The objectives were to measure the ability of the NIM-MetS test, previously used in the adults, for the early and sustainable detection of the Metabolic Syndrome (MetS) in children and adolescents. Moreover, to determine the economic burden of the children with MetS. Furthermore, finally, to use and implement the NIM-MetS test, via a self-created online software, as a new method to determine the risk of MetS in children. The method used was an observational study using different instruments (NIM-MetS test, International Diabetes Federation (IDF), or Cook) and measures (body mass index). Additionally, the economic burden was estimated via a research strategy in different databases, e.g., PubMed, to identify previous papers. The results (N = 265 children, age from 10–12) showed that 23.1% had obesity and 7.2% hypertension. The prevalence of MetS using the NIM-Mets was 5.7, and the cost of these children was approximate 618,253,99 euros. Finally, a model was obtained and later implemented in a web platform via simulation. The NIM-MetS obtained is a non-invasive method for the diagnosis of risk of MetS in children.

Performance of seven ECG interpretation programs in identifying arrhythmia and acute cardiovascular syndrome

BACKGROUND

No direct comparison of current electrocardiogram (ECG) interpretation programs exists.

OBJECTIVE

Assess the accuracy of ECG interpretation programs in detecting abnormal rhythms and flagging for priority review records with alterations secondary to acute coronary syndrome (ACS).

METHODS

More than 2,000 digital ECGs from hospitals and databases in Europe, USA, and Australia, were obtained from consecutive adult and pediatric patients and converted to 10 s analog samples that were replayed on seven electrocardiographs and classified by the manufacturers' interpretation programs. We assessed ability to distinguish sinus rhythm from non-sinus rhythm, identify atrial fibrillation/flutter and other abnormal rhythms, and accuracy in flagging results for priority review. If all seven programs' interpretation statements did not agree, cases were reviewed by experienced cardiologists.

RESULTS

All programs could distinguish well between sinus and non-sinus rhythms and could identify atrial fibrillation/flutter or other abnormal rhythms. However, false-positive rates varied from 2.1% to 5.5% for non-sinus rhythm, from 0.7% to 4.4% for atrial fibrillation/flutter, and from 1.5% to 3.0% for other abnormal rhythms. False-negative rates varied from 12.0% to 7.5%, 9.9% to 2.7%, and 55.9% to 30.5%, respectively. Flagging of ACS varied by a factor of 2.5 between programs. Physicians flagged more ECGs for prompt review, but also showed variance of around a factor of 2. False-negative values differed between programs by a factor of 2 but was high for all (>50%). Agreement between programs and majority reviewer decisions was 46-62%.

CONCLUSIONS

Automatic interpretations of rhythms and ACS differ between programs. Healthcare institutions should not rely on ECG software "critical result" flags alone to decide the ACS workflow.

Erroneous computer-based interpretations of atrial fibrillation and atrial flutter in a Swedish primary health care setting

Objective: To describe the incidence of incorrect computerized ECG interpretations of atrial fibrillation or atrial flutter in a Swedish primary care population, the rate of correction of computer misinterpretations, and the consequences of misdiagnosis.Design: Retrospective expert re-analysis of ECGs with a computer-suggested diagnosis of atrial fibrillation or atrial flutter.Setting: Primary health care in Region Kronoberg, Sweden.Subjects: All adult patients who had an ECG recorded between January 2016 and June 2016 with a computer statement including the words 'atrial fibrillation' or 'atrial flutter'.Main outcome measures: Number of incorrect computer interpretations of atrial fibrillation or atrial flutter; rate of correction by the interpreting primary care physician; consequences of misdiagnosis of atrial fibrillation or atrial flutter.Results: Among 988 ECGs with a computer diagnosis of atrial fibrillation or atrial flutter, 89 (9.0%) were incorrect, among which 36 were not corrected by the interpreting physician. In 12 cases, misdiagnosed atrial fibrillation/flutter led to inappropriate treatment with anticoagulant therapy. A larger proportion of atrial flutters, 27 out of 80 (34%), than atrial fibrillations, 62 out of 908 (7%), were incorrectly diagnosed by the computer.Conclusions: Among ECGs with a computer-based diagnosis of atrial fibrillation or atrial flutter, the diagnosis was incorrect in almost 10%. In almost half of the cases, the misdiagnosis was not corrected by the overreading primary-care physician. Twelve patients received inappropriate anticoagulant treatment as a result of misdiagnosis.Key pointsData regarding the incidence of misdiagnosed atrial fibrillation or atrial flutter in primary care are lacking. In a Swedish primary care setting, computer-based ECG interpretations of atrial fibrillation or atrial flutter were incorrect in 89 of 988 (9.0%) consecutive cases.Incorrect computer diagnoses of atrial fibrillation or atrial flutter were not corrected by the primary-care physician in 47% of cases.In 12 of the cases with an incorrect computer rhythm diagnosis, misdiagnosed atrial fibrillation or flutter led to inappropriate treatment with anticoagulant therapy.

SPICED-ACS: Study of the potential impact of a computer-generated ECG diagnostic algorithmic certainty index in STEMI diagnosis: Towards transparent AI

BACKGROUND

Computerised electrocardiogram (ECG) interpretation diagnostic algorithms have been developed to guide clinical decisions like with ST segment elevation myocardial infarction (STEMI) where time in decision making is critical. These computer-generated diagnoses have been proven to strongly influence the final ECG diagnosis by the clinician; often called automation bias. However, the computerised diagnosis may be inaccurate and could result in a wrong or delayed treatment harm to the patient. We hypothesise that an algorithmic certainty index alongside a computer-generated diagnosis might mitigate automation bias. The impact of reporting a certainty index on the final diagnosis is not known.

PURPOSE

To ascertain whether knowledge of the computer-generated ECG algorithmic certainty index influences operator diagnostic accuracy.

METHODOLOGY

Clinicians who regularly analyse ECGs such as cardiology or acute care doctors, cardiac nurses and ambulance staff were invited to complete an online anonymous survey between March and April 2019. The survey had 36 ECGs with a clinical vignette of a typical chest pain and which were either a STEMI, normal, or borderline (but do not fit the STEMI criteria) along with an artificially created certainty index that was either high, medium, low or none. Participants were asked whether the ECG showed a STEMI and their confidence in the diagnosis. The primary outcomes were whether a computer-generated certainty index influenced interpreter's diagnostic decisions and improved their diagnostic accuracy. Secondary outcomes were influence of certainty index between different types of clinicians and influence of certainty index on user's own-diagnostic confidence.

RESULTS

A total of 91 participants undertook the survey and submitted 3262 ECG interpretations of which 75% of ECG interpretations were correct. Presence of a certainty index significantly increased the odds ratio of a correct ECG interpretation (OR 1.063, 95% CI 1.022-1.106, p = 0.004) but there was no significant difference between correct certainty index and incorrect certainty index (OR 1.028, 95% CI 0.923-1.145, p = 0.615). There was a trend for low certainty index to increase odds ratio compared to no certainty index (OR 1.153, 95% CI 0.898-1.482, p = 0.264) but a high certainty index significantly decreased the odds ratio of a correct ECG interpretation (OR 0.492, 95% CI 0.391-0.619, p < 0.001). There was no impact of presence of a certainty index (p = 0.528) or correct certainty index (p = 0.812) on interpreters' confidence in their ECG interpretation.

CONCLUSIONS

Our results show that the presence of an ECG certainty index improves the users ECG interpretation accuracy. This effect is not seen with differing levels of confidence within a certainty index, with reduced ECG interpretation success with a high certainty index compared with a trend for increased success with a low certainty index. This suggests that a certainty index improves interpretation when there is an increased element of doubt, possibly forcing the ECG user to spend more time and effort analysing the ECG. Further research is needed looking at time spent analysing differing certainty indices with alternate ECG diagnoses.