Novel technical solutions for wireless ECG transmission & analysis in the age of the internet cloud

A Scoping Review of Varying Mobile Electrocardiographic Devices

The electrocardiogram (ECG) can now be measured using mobile devices. Mobile ECG devices, which are defined as devices capable of recording and transmitting non-standard ECGs, offer numerous advantages such as cost-effectiveness and being user-friendly. Mobile ECG can also extend recording lengths (e.g., 2 days, 14 days), which is necessary to capture important intermittent events (e.g., cardiac arrhythmias) and evaluate prognostic risk markers (e.g., prolonged corrected QT (QTc) interval). Some mobile ECG devices can even connect to broadband networks allowing patients to remotely transmit their ECG to a clinician. This article systematically examines different mobile ECG devices used in prior studies and provides a detailed assessment of five diverse yet commonly used mobile ECG devices: AliveCor KardiaMobile; AliveCor KardiaMobile 6L; iRhythm ZioPatch; Apple Smartwatch ECG; and CardioSecur System. These mobile ECG devices are diverse in the number of leads measured and the duration of monitoring. Similar to their diversity, there has been a wide range of clinical applications of mobile ECG devices. Despite significant progress, questions regarding data quality, and clinican and patient acceptance and compliance persist.

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction

INTRODUCTION

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting.

METHODS

A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics.

RESULTS

There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria.

CONCLUSIONS

Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome

STUDY OBJECTIVE

Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.

METHODS

This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis.

RESULTS

Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation.

CONCLUSION

In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making.

Analysis and postprocessing of ECG or heart rate data from wearable devices beyond the proprietary cloud and app infrastructure of the vendors

Background

The impact of medical-grade wearable electrocardiographic (ECG) recording technology is increasing rapidly. A wide range of different portable smartphone-connected ECG and heart rate trackers is available on the market. Smart ECG devices are especially valuable to monitor either supraventricular arrhythmias or prolonged QT intervals to avoid drug-induced life-threatening arrhythmias. However, frequent false alarms or false-positive arrhythmia results from wearable devices are unwanted. Therefore, for clinical evaluation, it should be possible to measure and evaluate the biosignals of the wearables independent of the manufacturer.

Objective

Unlike radiological devices that do support the universal digital imaging and communications in medicine standard, these medical-grade devices do not yet support a secure standardized exchange pathway between sensors, smartphones/smartwatches, and end services such as cloud storage or universal Web-based application programming interface (API) access. Consequently, postprocessing of recorded ECGs or heart rate interval data requires a whole toolbox of customized software technologies.

Methods/Results

Various methods for measuring and analyzing nonstandardized ECG and heart rate data are proposed, including online measurement of ECG waveforms within a PDF, access to data using manufacturer-specific software development kits, and access to biosignals using modern Web APIs.

Conclusion

With the appropriate workaround, modern software technologies such as JavaScript and PHP allow health care providers and researchers to easily and instantly access necessary and important signal measurements on demand.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

National electrocardiographic mapping by telemedicine for diagnosis and prevention of cardiological pathologies in Paraguay

Introduction:

Telemedicine tools offer multiple advantages to achieve an epidemiological screening of communities in rural settings countrywide. However, evidence on the cardiological pathology surveillance in these communities is limited. The feasibility of telemedicine as an electrocardiographic (EKG) mapping tool for the diagnosis and prevention of cardiological pathologies in Paraguay was investigated.

Methods:

A descriptive study was conducted in 60 telediagnostic centers countrywide in Paraguay to evaluate the feasibility of telemedicine as an EKG mapping tool for the diagnosis and prevention of cardiological pathologies over a period of 5 years from 2014 to 2018. The adherence rate was determined comparing yearly scheduled visits versus fulfilled visits at the telemedicine platform.

Results:

During the study, 246,217 remote EKG diagnoses were performed in 60 hospitals using telemedicine. The patients were 19.4% children/adolescents and 80.6% adults. The results of EKG tests in the children/adolescent group were 79.4% normal and 20.6% abnormal. The most frequent abnormal heart rhythms observed were sinusal bradicardia (10.6%), sinusal tachycardia (3.2%), and unspecified arrhythmia (2.8%). In the adult group, the results were 66.3% normal and 33.7% abnormal. The most frequent abnormal heart rhythms in this group were sinusal bradicardia (11.2%), blockade of the right branch (4.8%), and left ventricular hypertrophy (4.7%). The most frequent cardiovascular risk factors observed were the association of hypertension and obesity (40%), hypertension and diabetes (20%), and hypertension and dyslipidemia (19%). During the test period (2014–2018), the average rate of patient adherence to the prevention program was 2.26 for each 1000 diagnosis.

Conclusion:

These results demonstrate the feasibility of telemedicine as an EKG mapping tool for the diagnosis and prevention of cardiological pathologies in low-resource countries, thus enhancing cardiovascular disease surveillance and optimizing human and financial resources.

Adults with congenital heart disease: ready for mobile health?

PURPOSE

Mobile health (mHealth) could improve the outcome of grown-up patients with congenital heart disease (GUCH) and reduce their emergency care utilisation. Inappropriate use of mHealth, however, can lead to data overload for professionals and unnecessary data collection for patients, increasing the burden for both. We aimed to determine the clinical characteristics of patients with high emergency care utilisation and to test whether these patients were willing to start using mHealth.

METHODS

Clinical characteristics and emergency care utilisation of consecutive GUCH patients who visited one of the two participating cardiologists at the outpatient clinic of the Academic Medical Centre in Amsterdam were studied retrospectively. All patients were approached to fill in an mHealth questionnaire. A frequency of three or more emergency visits in 5 years was defined as high emergency care utilisation.

RESULTS

In total, 202 consecutive GUCH patients who visited one of the two participating cardiologists were studied. Median age was 41 years, 47% were male, and 51% were symptomatic. In the previous 5 years, 134 emergency visits were identified. Of all patients, 8% had high emergency care utilisation. High emergency care utilisation was associated with patients being symptomatic, using antiarrhythmic drugs or diuretics. In total, 75% of all patients with high emergency care utilisation were willing to start using mHealth.

CONCLUSION

GUCH patients who are symptomatic, those on antiarrhythmic drug therapy and those on diuretics are suitable candidates for enrolment in future mHealth initiatives because of both high care utilisation and high motivation to start using mHealth.

Lack of Significant Coronary History and ECG Misinterpretation Are the Strongest Predictors of Undertriage in Prehospital Chest Pain

INTRODUCTION

Appropriate prehospital (PH) triage of patients with chest pain can significantly improve outcomes in acute myocardial infarction (MI). We sought to explore how PH providers triage chest pain as high versus low risk and to evaluate the accuracy and predictors of their triage decision.

METHODS

This was a prospective, observational cohort study that enrolled consecutive patients with chest pain transported by emergency medical services (EMS) to 3 tertiary care hospitals in the US. EMS triage decision (high risk versus low-risk) was defined based on the transmission of PH electrocardiogram (ECG) to a command center for medical consultation with or without catheter laboratory activation. Two independent reviewers examined in-hospital medical records to adjudicate the presence of acute MI and to audit the findings on the presenting ECG.

RESULTS

We enrolled 2,065 patients (aged 56 ± 17, 53% male) of whom 768 (37%) were triaged as high risk. Those triaged as high risk were older, were more likely to be men or have significant cardiac history, and had a higher rate of acute MI events (14.2% versus 3.5%). The sensitivity and specificity for triaging MI events as high risk were 70% and 97%, respectively. A total of 46/155 (30%) MI events were misclassified as low risk. No previous coronary revascularization and ECG misinterpretation were strong independent predictors of such undertriage.

CONCLUSIONS

PH providers have moderate sensitivity in triaging high-risk patients; 1 in 3 MI events are undertriaged. Emergency nurses need to pay special attention to patients with benign past histories during transition of care and should always reinterpret ECGs for subtle ischemic changes.

Arterial Stiffness Is Associated With QTc Interval Prolongation in Patients With Heart Failure

BACKGROUND

A prolonged corrected QT (QTc) interval is a known risk factor for adverse cardiac events. Understanding the determinants and physiologic correlates of QTc is necessary for selecting proper strategies to reduce the risk of adverse events in high-risk patients. We sought to evaluate the role of arterial stiffness in heart failure as a determinant of QTc prolongation.

METHOD

This was an observational study that recruited ambulatory heart failure patients (New York Heart Association Classes I-II) from an outpatient heart failure clinic. In the supine resting position, consented patients underwent noninvasive 12-lead electrocardiograph (ECG) and hemodynamic monitoring using BioZ Dx impedance cardiography. ECGs were evaluated by a reviewer blinded to clinical data, and QTc interval was automatically computed. Patients with pacing or bundle branch block (BBB) were analyzed separately. Strengths of associations were evaluated using Pearson's r coefficients and multivariate linear regression.

RESULTS

The final sample ( N = 44) was 62 ± 13 years of age and 64% male with ejection fraction of 34% ± 12%. At univariate level, QTc interval moderately ( r > .50) correlated with cardiac output, left cardiac work index, systemic vascular resistance, and total arterial compliance in patients with intrinsically narrow QRS complexes. At the multivariate level, increasing systemic vascular resistance and decreasing total arterial compliance remained independent predictors of widening QTc interval in this group ( R² = .54). No significant correlations were seen in patients with pacing or BBB.

CONCLUSIONS

In the absence of conduction abnormalities, magnitude of arterial stiffness, an indirect measure of endothelial dysfunction, is associated with QTc interval prolongation.

[Public health impact of a remote diagnosis system implemented in regional and district hospitals in Paraguay]

Objetivo.

Determinar la viabilidad y puesta en marcha de un sistema de telediagnóstico para dar asistencia sanitaria a poblaciones remotas y dispersas del Paraguay.

Métodos.

El estudio fue realizado en todos los hospitales regionales, generales y principales hospitales distritales de las 18 regiones sanitarias del Paraguay. En el sistema se registraron los datos clínicos y las imágenes tomográficas, ecográficas y trazados electrocardiográficos del paciente que precisaba de un diagnóstico por parte de un médico especialista. Esta información se transmitió a los especialistas en imagenología y en cardiología para su diagnóstico remoto y posterior envío del informe a los hospitales conectados al sistema. Se analizó el costo-beneficio e impacto de la herramienta de telediagnóstico desde la perspectiva del Sistema Nacional de Salud.

Resultados.

Entre enero de 2014 y mayo de 2015 se realizaron 34 096 telediagnósticos distribuidos en 25 hospitales a través del Sistema de Telemedicina del Ministerio de Salud. El costo unitario promedio del diagnóstico remoto fue de USD 2,6 (dólares estadounidenses) para electrocardiograma (ECG), tomografía y ecografía, mientras que el costo unitario para el diagnóstico “cara a cara” fue de UDS 11,8 para ECG; USD 68,6 para tomografía y USD 21,5 para ecografía. La reducción del costo mediante el diagnóstico remoto fue de 4,5 veces para ECG; 26,4 veces para tomografía y de 8,3 veces para ecografía. En términos monetarios, la implementación del sistema de telediagnóstico, durante los 16 meses del estudio, significó un ahorro promedio de USD 2 420 037.

Conclusión.

Paraguay cuenta con un sistema de telediagnóstico para electrocardiografía, tomografía y ecografía aplicando las tecnologías de la información y comunicación (TIC) de bajo costo, basadas en software libre y escalable a otros tipos de estudios diagnósticos a distancia; de interés para la salud pública. Con una aplicación práctica del telediagnóstico, se contribuyó al fortalecimiento de la red integrada de servicios y programas de salud, lo que permitió maximizar el tiempo del profesional y su productividad, mejorar la calidad, aumentar el acceso y la equidad, y disminuir los costos.

The Technological Growth in eHealth Services

The infusion of information communication technology (ICT) into health services is emerging as an active area of research. It has several advantages but perhaps the most important one is providing medical benefits to one and all irrespective of geographic boundaries in a cost effective manner, providing global expertise and holistic services, in a time bound manner. This paper provides a systematic review of technological growth in eHealth services. The present study reviews and analyzes the role of four important technologies, namely, satellite, internet, mobile, and cloud for providing health services.

A scoping review of cloud computing in healthcare

BACKGROUND

Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.

METHODS

MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.

RESULTS

102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.

CONCLUSIONS

Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.