Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation.

The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12-lead electrocardiogram (ECG) is crucial for achieving appropriate, high-quality, and cost-effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC-VTcalc) as a novel generalizable, accurate, and easy-to-use means to estimate VT probability independent of ECG interpreter competency. The MC-VTcalc, through the use of web-based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T-wave axis) that are routinely displayed on standard 12-lead ECG recordings.

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias.

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning.

BACKGROUND: Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application. PURPOSE: To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). METHOD COMPARISON: Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12­lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12­lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12­lead ECGs. CONCLUSION: We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions.

The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias.

Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12­lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.

Education curriculum assessment for teaching electrocardiography: Rationale and design for the prospective, international, randomized controlled EDUCATE trial.

BACKGROUND: Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. DESIGN: The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. CONCLUSIONS: The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community.

Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals.

BACKGROUND: Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS: A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. RESULTS: The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION: Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy.

BACKGROUND: Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. OBJECTIVE: We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. METHODS: Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. RESULTS: Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. CONCLUSION: The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.

Conceptual and literature basis for wide complex tachycardia and baseline ECG comparison.

Accurate wide QRS complex tachycardia (WCT) differentiation into either ventricular tachycardia or supraventricular wide complex tachycardia using 12­lead electrocardiogram (ECG) interpretation is essential for diagnostic, therapeutic, and prognostic reasons. There is an ever-expanding variety of WCT differentiation methods and criteria available to clinicians. However, only a few make use of the diagnostic value of comparing the ECG during WCT to that of the patient's baseline ECG. Therefore, we highlight the conceptual rationale and scientific literature supporting the diagnostic value of WCT and baseline ECG comparison.

The VT Prediction Model: A simplified means to differentiate wide complex tachycardias.

BACKGROUND: The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. PURPOSE: We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. METHODS: In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. RESULTS: The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903-0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862-0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively. CONCLUSION: The VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.

Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology.

PURPOSE OF REVIEW: To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. RECENT FINDINGS: Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.

A lateral lead variant of the de Winter pattern due to left main stenosis and left anterior descending artery occlusion.

Early recognition of ST-segment elevation myocardial infarction equivalent electrocardiogram patterns is of paramount importance. Successful identification of these ischemic patterns helps ensure proper triage of patients needing urgent restoration of coronary perfusion. The so-called de Winter sign has become increasingly recognized as a ST-segment elevation myocardial infarction equivalent pattern due to proximal left anterior descending artery occlusion. Yet, despite the de Winter pattern's well-defined electrocardiographic-angiographic relationship, the electrophysiologic explanation for its characteristic electrocardiographic manifestations remains unclear. Herein, we report a case in which an ischemic lateral lead variant of the de Winter pattern emerged from a patient inflicted by an abrupt thrombotic occlusion of the ostial left anterior descending artery, which developed in series with a high-grade stenosis of the distal left main coronary artery. We examine the patient's presenting electrocardiographic findings and clinical course to (i) establish causal inferences that align with the distribution of myocardial ischemia supported by coronary angiography and (ii) provide an accompanying analysis of the relevant scientific literature.

Wide complex tachycardia differentiation: An examination of traditional and contemporary approaches.

Despite many technological advances in the field of cardiology, accurate differentiation of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia continues to be challenging. After decades of rigorous clinical research, a wide variety of electrocardiographic criteria and algorithms have been developed to provide an accurate means to distinguish these two entities as accurately as possible. Recently, promising automated differentiation methods that utilize computerized electrocardiographic interpretation software have emerged. In this review, we aim to (1) highlight the clinical importance of accurate wide complex tachycardia differentiation, (2) provide an overview of the conventional manually-applied differentiation algorithms, and (3) describe novel automated approaches to differentiate wide complex tachycardia.

The WCT Formula II: An effective means to automatically differentiate wide complex tachycardias.

BACKGROUND: Differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional manually-operated electrocardiogram (ECG) interpretation methods is difficult. Recent research has shown that accurate WCT differentiation may be accomplished by automated approaches (e.g., WCT Formula) implemented by computerized ECG interpretation software. OBJECTIVE: We sought to develop a new automated means to differentiate WCTs. METHODS: First, a derivation cohort of paired WCT and baseline ECGs was examined to secure independent VT predictors to be incorporated into a logistic regression model (i.e., WCT Formula II). Second, the WCT Formula II was trialed against a separate validation cohort of paired WCT and baseline ECGs. RESULTS: The derivation cohort comprised 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. The WCT Formula II was composed of baseline QRS duration (p = 0.02), WCT QRS duration (p < 0.001), frontal percent time-voltage area change (p < 0.001), and horizontal percent time-voltage area change (p < 0.001). The area under the curve (AUC) for VT and SWCT differentiation was 0.96 (95% CI 0.94-0.98) for the derivation cohort. The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. WCT Formula II implementation on the validation cohort yielded effective WCT differentiation (AUC 0.96; 95% CI 0.94-0.98). CONCLUSION: The WCT Formula II is an example of how contemporary ECG interpretation software could be used to differentiate WCTs successfully.

Diffuse ST-segment depression despite prior coronary bypass grafting: An electrocardiographic-angiographic correlation.

The standard 12­lead electrocardiogram (ECG) has become a mainstay diagnostic tool in patients suspected to have myocardial ischemia. The identification of hallmark electrocardiographic abnormalities, such as ST-segment deviation or serial T wave changes, not only helps identify the presence of myocardial ischemia but also may help localize myocardial territories with an ongoing injury. Widespread ST-segment depression is commonly attributed to diffuse subendocardial ischemia precipitated by severe multivessel or left main coronary artery disease. However, among patients with prior coronary revascularization, clear electrocardiographic-angiographic relationships responsible for widespread ST-segment depressions have not been well defined. We report a case in which diffuse ST-segment depression emerged from a patient with prior coronary artery bypass grafting. In this report, we examine the patient's presenting ECG pattern as to (1) establish causal inferences which align with the distribution of myocardial ischemia supported by angiography and (2) provide an accompanying analysis of the relevant scientific literature.

The WCT Formula: A novel algorithm designed to automatically differentiate wide-complex tachycardias.

BACKGROUND: The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software. METHODS: In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance. RESULTS: The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p < 0.001), frontal percent amplitude change (%) (p < 0.001), and horizontal percent amplitude change (%) (p < 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT. CONCLUSION: The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.

Electrocardiogram algorithms used to differentiate wide complex tachycardias demonstrate diagnostic limitations when applied by non-cardiologists.

AIMS: Non-cardiologists (NCs) are often responsible for the preliminary diagnosis and early management of patients presenting with ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT). At present, the Vereckei aVR and Brugada algorithms are the most widely recognized and frequently relied upon wide complex tachycardia (WCT) differentiation criteria by NCs. This study aimed to determine the diagnostic efficacy of the Vereckei aVR and Brugada algorithms when applied by NCs. METHODS: In a blinded fashion, three internal medicine residents prospectively interpreted WCTs using the Vereckei aVR and Brugada algorithms. The diagnostic performance of each method was evaluated according to their agreement with the correct rhythm diagnosis. RESULTS: Two-hundred sixty-nine WCTs (160 VT, 109 SWCT) from 186 patients were independently interpreted by each participant (807 separate interpretations per algorithm). The aVR and Brugada algorithms accurately classified 546 out of 807 (67.7%) and 622 out of 807 (77.1%) interpreted WCTs, respectively. Overall sensitivity and specificity of the aVR algorithm for VT was 92.1% and 31.8%, respectively. Overall sensitivity and specificity of the Brugada algorithm for VT was 89.4% and 59.0%, respectively. Both algorithms yielded modestly favorable overall positive predictive values (aVR 66.5%; Brugada 76.2%) and negative predictive values (73.3%; Brugada 79.1%). CONCLUSION: Non-cardiologist algorithm users correctly identified most "actual" VTs, but did not sufficiently revise VT probability to conclusively distinguish VT and SWCT. Newer WCT differentiation methods are needed to improve NC's ability to accurately differentiate WCTs.

EDUCATE: An international, randomized controlled trial for teaching electrocardiography.

INTRODUCTION: Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals. METHODS: In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment. RESULTS: Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings. CONCLUSION: Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution.

Variation in the Use of Targeted Temperature Management for Cardiac Arrest.

Targeted temperature management (TTM) is recommended for patients who do not respond after return of spontaneous circulation after cardiac arrest. However, the degree to which patients with cardiac arrest have access to this therapy on a national level is not known. Understanding hospital- and patient-level factors associated with receipt of TTM could inform interventions to improve access to this treatment among appropriate patients. Therefore, we performed a retrospective analysis using National Inpatient Sample data from 2016 to 2019. We used International Classification of Diseases, Tenth Edition diagnosis and procedure codes to identify adult patients with in-hospital and out-of-hospital cardiac arrest and receipt of TTM. We evaluated patient and hospital factors associated with receiving TTM. We identified 478,419 patients with cardiac arrest. Of those, 4,088 (0.85%) received TTM. Hospital use of TTM was driven by large, nonprofit, urban, teaching hospitals, with less use at other hospital types. There was significant regional variation in TTM capabilities, with the proportion of hospitals providing TTM ranging from >21% in the Mid-Atlantic region to <11% in the East and West South Central and Mountain regions. At the patient level, age >74 years (odds ratio [OR] 0.54, p <0.001), female gender (OR 0.89, p >0.001), and Hispanic ethnicity (OR 0.74, p <0.001) were all associated with decreased odds of receiving TTM. Patients with Medicare (OR 0.75, p <0.001) and Medicaid (OR 0.89, p = 0.027) were less likely than patients with private insurance to receive TTM. Part of these differences was driven by inequitable access to TTM-capable hospitals. In conclusion, TTM is rarely used after cardiac arrest. Hospital use of TTM is predominately limited to a subset of academic hospitals with substantial regional variation. Older age, female gender, Hispanic ethnicity, and Medicare or Medicaid insurance are all associated with a decreased likelihood of receiving TTM.

Implementation of high-sensitivity troponin with a rapid diagnostic algorithm reduces emergency department length of stay for discharged patients.

INTRODUCTION: High sensitivity troponin (hs-cTn) and diagnostic algorithms are used to rapidly triage patients with symptoms of acute myocardial infarction in emergency departments (ED). However, few studies have evaluated the impact of simultaneously implementing hs-cTn and a rapid rule-out algorithm on length of stay (LOS). METHODS: We assessed the impact of transitioning from contemporary cTnI to hs-cTnI in 59,232 ED encounters over three years. hs-cTnI was implemented with an orderable series that included baseline, two-, four-, and six-hour specimens collected at provider discretion and operationalized with an algorithm to calculate the change in hs-cTnI from baseline and provide interpretations of "insignificant", "significant," or "equivocal." Patient demographics, results, chief complaint, disposition, and ED LOS were captured from the electronic medical record. RESULTS: cTnI was ordered for 31,875 encounters prior to hs-cTnI implementation and 27,357 after. The proportion of cTnI results above the 99th percentile upper reference limit decreased from 35.0% to 27.0% for men and increased from 27.8% to 34.8% for women. Among discharged patients, the median LOS decreased by 0.6 h (0.5-0.7). LOS among discharged patients with a chief complaint of chest pain decreased by 1.0 h (0.8-1.1) and further decreased by 1.2 h (1.0-1.3) if the initial hs-cTnI was below the limit of quantitation. The rate of acute coronary syndrome upon re-presentation within 30 days did not change post-implementation (0.10% versus 0.07%). CONCLUSION: Implementation of an hs-cTnI assay with a rapid rule-out algorithm decreased ED LOS among discharged patients, particularly among those with a chief complaint of chest pain.

Multidisciplinary Critical Care Management of Electrical Storm: JACC State-of-the-Art Review.

Electrical storm (ES) reflects life-threatening cardiac electrical instability with 3 or more ventricular arrhythmia episodes within 24 hours. Identification of underlying arrhythmogenic cardiac substrate and reversible triggers is essential, as is interrogation and programming of an implantable cardioverter-defibrillator, if present. Medical management includes antiarrhythmic drugs, beta-adrenergic blockade, sedation, and hemodynamic support. The initial intensity of these interventions should be matched to the severity of ES using a stepped-care algorithm involving escalating treatments for higher-risk presentations or recurrent ventricular arrhythmias. Many patients with ES are considered for catheter ablation, which may require the use of temporary mechanical circulatory support. Outcomes after ES are poor, including frequent ES recurrences and deaths caused by progressive heart failure and other cardiac causes. A multidisciplinary collaborative approach to the management of ES is crucial, and evaluation for heart transplantation or palliative care is often appropriate, even for patients who survive the initial episode.