Coagulopathy and Extremely Elevated PT/INR after Dabigatran Etexilate Use in a Patient with End-Stage Renal Disease

Introduction. Dabigatran is an oral direct thrombin inhibitor which has been approved for prophylaxis of stroke in patients with atrial fibrillation. The use of dabigatran etexilate increased rapidly due to many benefits. However, questions have been raised constantly regarding the safety of dabigatran etexilate. Case. A 58-year-old Caucasian male with a history of recurrent paroxysmal atrial fibrillation status after pacemaker and end-stage renal disease on hemodialysis came to the Emergency Department with the complaint of severe epistaxis. He had been started on dabigatran 150 mg twice a day about 4 months ago as an outpatient by his cardiologist. His prothrombin time (PT) was 63 seconds with international normalized ratio (INR) of 8.8 and his activated partial thromboplastin time (aPTT) was 105.7 seconds. Otherwise, all labs were unremarkable including the liver function test. Dabigatran was stopped immediately. His INR and aPTT trended downward, reaching normal levels 5 days after admission. Conclusion. Dabigatran is contraindicated in patients with severe kidney insufficiency as it is predominantly excreted via the kidney (~80%). Elderly patients over 75 and patients with chronic renal impairment should be carefully evaluated before starting dabigatran. Despite studies showing only mild increase in aPTT and PT/INR in patients receiving dabigatran, close monitoring may be reasonable in patients with renal insufficiency

Small conductance calcium-activated potassium current is important in transmural repolarization of failing human ventricles

BACKGROUND: The transmural distribution of apamin-sensitive small conductance Ca(2+)-activated K(+) (SK) current (IKAS) in failing human ventricles remains unclear. METHODS AND RESULTS: We optically mapped left ventricular wedge preparations from 12 failing native hearts and 2 rejected cardiac allografts explanted during transplant surgery. We determined transmural action potential duration (APD) before and after 100 nmol/L apamin administration in all wedges and after sequential administration of apamin, chromanol, and E4031 in 4 wedges. Apamin prolonged APD from 363 ms (95% confidence interval [CI], 341-385) to 409 (95% CI, 385-434; P<0.001) in all hearts, and reduced the transmural conduction velocity from 36 cm/s (95% CI, 30-42) to 32 cm/s (95% CI, 27-37; P=0.001) in 12 native failing hearts at 1000 ms pacing cycle length (PCL). The percent APD prolongation is negatively correlated with baseline APD and positively correlated with PCL. Only 1 wedge had M-cell islands. The percentages of APD prolongation in the last 4 hearts at 2000 ms PCL after apamin, chromanol, and E4031 were 9.1% (95% CI, 3.9-14.2), 17.3% (95% CI, 3.1-31.5), and 35.9% (95% CI, 15.7-56.1), respectively. Immunohistochemical staining of subtype 2 of SK protein showed increased expression in intercalated discs of myocytes. CONCLUSIONS: SK current is important in the transmural repolarization in failing human ventricles. The magnitude of IKAS is positively correlated with the PCL, but negatively correlated with APD when PCL is fixed. There is abundant subtype 2 of SK protein in the intercalated discs of myocytes

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

Aims: To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P &amp;lt; 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and −17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69–0.87) to 0.83 (0.74–0.91) or 0.84 (0.75–0.92), respectively. Conclusion AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI

Case Report Coagulopathy and Extremely Elevated PT/INR after Dabigatran Etexilate Use in a Patient with End-Stage Renal Disease

Introduction. Dabigatran is an oral direct thrombin inhibitor which has been approved for prophylaxis of stroke in patients with atrial fibrillation. The use of dabigatran etexilate increased rapidly due to many benefits. However, questions have been raised constantly regarding the safety of dabigatran etexilate. Case. A 58-year-old Caucasian male with a history of recurrent paroxysmal atrial fibrillation status after pacemaker and end-stage renal disease on hemodialysis came to the Emergency Department with the complaint of severe epistaxis. He had been started on dabigatran 150 mg twice a day about 4 months ago as an outpatient by his cardiologist. His prothrombin time (PT) was 63 seconds with international normalized ratio (INR) of 8.8 and his activated partial thromboplastin time (aPTT) was 105.7 seconds. Otherwise, all labs were unremarkable including the liver function test. Dabigatran was stopped immediately. His INR and aPTT trended downward, reaching normal levels 5 days after admission. Conclusion. Dabigatran is contraindicated in patients with severe kidney insufficiency as it is predominantly excreted via the kidney (∼80%). Elderly patients over 75 and patients with chronic renal impairment should be carefully evaluated before starting dabigatran. Despite studies showing only mild increase in aPTT and PT/INR in patients receiving dabigatran, close monitoring may be reasonable in patients with renal insufficiency

Effectiveness of Dual External Direct Current Cardioversion for Initial Cardioversion in Atrial Fibrillation

Introduction Dual external direct current cardioversion (dual‐DCCV) is a rhythm control strategy for persistent atrial fibrillation (AF), involving simultaneous delivery of two shocks from two defibrillators. The long‐term effectiveness of this approach has not been studied in the biphasic cardioversion era. Methods Seventy‐seven consecutive patients at a single center were identified to receive dual‐DCCV at the time of their initial cardioversion for AF, when maximum output standard external direct current cardioversion failed in two vectors. Logistic regression was used to analyze risk factors for dual‐DCCV in a historical control group of 77 patients undergoing standard cardioversion and Cox proportional hazard models were used to compare time to AF recurrence. Results The dual‐DCCV group had a significantly larger body mass index (BMI), but similar AF duration and left atrial size as controls. Multivariable logistic regression revealed that BMI and absence of prior paroxysmal AF were risk factors for dual‐DCCV (P \u3c 0.05). There was no difference observed between dual‐DCCV and control groups (adjusted hazard ratio = 0.57; P = .12) after adjusting for number of shocks and age. Transient hypoxia was the only acute complication in either group (P \u3e .999). Conclusion Dual‐DCCV appears to be a safe and effective cardioversion strategy for patients with AF. The need for dual‐DCCV in the treatment of AF appears to be influenced more by body habitus than atrial substrate

Simplified Integrated Clinical and Electrocardiographic Algorithm for Differentiation of Wide QRS Complex Tachycardia: The Basel Algorithm.

BACKGROUND Prompt differential diagnosis of wide QRS complex tachycardia (WCT) is crucial to patient management. However, distinguishing ventricular tachycardia (VT) from supraventricular tachycardia (SVT) with wide QRS complexes remains problematic, especially for nonelectrophysiologists. OBJECTIVES This study aimed to develop a simple-to-use algorithm with integration of clinical and electrocardiographic (ECG) parameters for the differential diagnosis of WCT. METHODS The 12-lead ECGs of 206 monomorphic WCTs (153 VT, 53 SVT) with electrophysiology-confirmed diagnoses were analyzed. In the novel Basel algorithm, VT was diagnosed in the presence of at least 2 of the following criteria: 1) clinical high risk features; 2) lead II time to first peak >40 ms; and 3) lead aVR time to first peak >40 ms. The algorithm was externally validated in 203 consecutive WCT cases (151 VT, 52 SVT). Its' diagnostic performance and clinical applicability were compared with those of the Brugada and Vereckei algorithms. RESULTS The Basel algorithm showed a sensitivity, specificity, and accuracy of 92%, 89%, and 91%, respectively, in the derivation cohort and 93%, 90%, and 93%, respectively, in the validation cohort. There were no significant differences in the performance characteristics between the 3 algorithms. The evaluation of the clinical applicability of the Basel algorithm showed similar diagnostic accuracy compared with the Brugada algorithm (80% vs 81%; P = 1.00), but superiority compared with the Vereckei algorithm (72%; P = 0.03). The Basel algorithm, however, enabled a faster diagnosis (median 36 seconds vs 105 seconds for the Brugada algorithm [P = 0.002] and 50 seconds for the Vereckei algorithm [P = 0.02]). CONCLUSIONS The novel Basel algorithm based on simple clinical and ECG criteria allows for a rapid and accurate differential diagnosis of WCT

Catheter ablation of ventricular fibrillation: importance of left ventricular outflow tract and papillary muscle triggers

Background: Monomorphic ventricular premature depolarizations (VPDs) have been found to initiate ventricular fibrillation (VF) or polymorphic ventricular tachycardia (PMVT) in patients with and without structural heart disease. Objective: The purpose of this study was to describe and characterize sites of origin of VPDs triggering VF and PMVT. Methods: The distribution of mapping-confirmed VPDs, electrophysiology laboratory findings, and results of radiofrequency catheter ablation were analyzed. Results: Among 1132 consecutive patients who underwent ablation for ventricular arrhythmias, 30 patients (2.7%) with documented VF/PMVT initiation were identified. In 21 patients, VF/PMVT occurred in the setting of cardiomyopathy; in 9 patients, VF/PMVT was idiopathic. The origin of VPD trigger was from the Purkinje network in 9, papillary muscles in 8, left ventricular outflow tract in 9, and other low-voltage areas unrelated to Purkinje activity in 4. Each distinct anatomic area of origin was associated with VF/PMVT triggers in patients with and without heart disease. Acute VPD elimination was achieved in 26 patients (87%), with a decrease in VPDs in another 3 patients (97%). During median follow-up of 418 days (interquartile range [IQR] 144-866), 5 patients developed a VF/PMVT recurrence after a median of 34 days (IQR 1-259). Rare recurrence was noted in patients with and without structural disease and from each distinct anatomic origin. The total burden of VF/PMVT episodes/shocks was reduced from a median of 9 (IQR 2.5-22.5) in the 3 months before ablation to 0 (IQR 0-0, total range 0-2) during follow-up (

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

AimsTo evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection.Methods and resultsSEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P &lt; 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and −17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69–0.87) to 0.83 (0.74–0.91) or 0.84 (0.75–0.92), respectively.ConclusionAI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI

Automated echocardiographic detection of severe coronary artery disease using artificial intelligence

The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence. [Abstract copyright: Copyright © 2021 The Authors