Supraventricular Tachycardia Causing Left Ventricular Dysfunction

Pragmatic electrocardiogram tracings in non-ischaemic dilated cardiomyopathy: diagnostic and prognostic role

Dilated cardiomyopathy (DCM) is a primitive heart muscle disease characterized by a great heterogeneous aetiology and prognostic outcome. Dilated cardiomyopathy is an umbrella term encompassing different aetiologies that might require specific treatments. It principally affects young and male adults, with high-risk arrhythmic competitive risk. Unfortunately, the prevention of major ventricular arrhythmic events remains a clinical challenge. In the era of advanced multimodality imaging and widely available genetic testing, electrocardiogram (ECG) continues to represent a reliable diagnostic tool, for specific work up of every single patient. However, approaching DCM patients, only a cardiomyopathy-oriented reading makes the role of ECG central in the management of DCM, both for diagnosis, prognosis, and therapeutic management. In this paper, we present four ECGs of four different DCM patients, in order to guide a cardiomyopathy-oriented ECG reading, emphasizing its impact in an early, cost-effective, and personalized diagnostic and prognostic work up in this specific setting.

Clinical characterization and natural history of chemotherapy-induced dilated cardiomyopathy

AIMS

Chemotherapy-induced dilated cardiomyopathy (CI-DCM) is a well-recognized phenotype of non-ischemic dilated cardiomyopathy (DCM), characterized by poor outcomes. However, a detailed comparison between idiopathic DCM (iDCM) and CI-DCM is still lacking.

METHODS AND RESULTS

All consecutive DCM patients enrolled in the Trieste Muscle Heart Disease Registry were analysed. CI-DCM and iDCM were defined according to current recommendations. The primary study outcome measure was all-mortality death and secondary outcomes were a) a composite of cardiovascular death/heart-transplantation/ventricular-assist-device implantation, and b) major ventricular arrhythmias. The study included 551 patients (499 iDCM and 52 CI-DCM). At enrolment, compared with iDCM, CI-DCM patients were older (51 ± 14 years vs. 58 ± 3 years, respectively, P < 0.001) and had a higher left ventricular ejection fraction (32% ± 9 vs. 35% ± 10, respectively, P = 0.03). Over a median follow-up of 90 months (IQR 54-140 months), CI-DCM patients had a higher incidence of all-cause mortality compared with iDCM (36.5% vs. 8.4% in CI-DCM and iDCM respectively, P < 0.001), while the incidence of major ventricular arrhythmias was higher in the iDCM group compared with CI-DCM (4% vs. 0%, in CI-DCM and iDCM respectively, P = 0.03). The risk of the composite outcome was comparable between the two groups (P = 0.91). At Cox multivariable analysis, the diagnosis of CI-DCM emerged as independently associated to primary outcome (HR 6.42, 95% C.I. 2.52-16.31, P < 0.001).

CONCLUSIONS

In a well-selected DCM cohort, patients with a chemotherapy-induced aetiology had a higher incidence of all-cause mortality compared with iDCM. Conversely, the incidence of life-threatening ventricular arrhythmic events was higher among patients with iDCM.

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) in the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia. In this study, 158 patients with rapid arrhythmia treated by radiofrequency ablation were divided into effective treatment group (142 cases) and ineffective treatment group (16 cases). ECG examination was performed on all patients, and the indicators of ECG examination were quantified by the deep learning-based convolutional neural network model. The indicators of ECG examination of the effective treatment group and the ineffective treatment group were compared. The results showed that compared with the ineffective treatment group, the end-systolic volume (ESV), end-diastolic volume (EDV), end-systolic volume index (ESVI), and end-diastolic volume index (EDVI) of the effective treatment group were significantly decreased, and the left ventricular ejection fraction (LVEF) was significantly increased (P < 0.05). After radiofrequency ablation, the ventricular rate of patients in the effective treatment group was significantly lower than that of the ineffective treatment group at 12 h and 24 h after treatment (P < 0.05). In addition, compared with patients in the ineffective treatment group, the QT dispersion of the ECG in the effective treatment group was significantly higher (P < 0.05). The accuracy, specificity, and sensitivity of ECG in evaluating the therapeutic effect of patients with tachyarrhythmia were 86.81%, 84.29%, and 77.27%, respectively. The area under the curve was determined as 0.798 according to the receiver operating characteristic (ROC) curve of the subjects. In summary, indicators of ECG examination based on deep learning can provide auxiliary reference information for the efficacy evaluation of radiofrequency ablation in the treatment of tachyarrhythmia.