Health-related quality of life in patients with implantable cardioverter defibrillators in Sweden: a cross-sectional observational trial

The effect of virtual interactive nurse-led support group intervention on fatigue, shock anxiety, and acceptance of implantable cardioverter defibrillator patients: a randomized trial

BACKGROUND

Implantable cardioverter defibrillators (ICD), as a gold and standard treatment for fatal cardiac arrhythmia, may lead to some physical and psychological problems for the patients. Therefore, performing some interventions to reduce or eliminate these issues is crucial. This study aimed to determine the effect of virtual interactive nurse-led support group intervention on fatigue, shock anxiety, and acceptance of ICD patients.

METHODS

This is a clinical trial study on 72 patients with ICD. They were randomly allocated to the intervention (n = 36) and control (n = 36) groups. A virtual interactive nurse-led support group intervention through WhasApp was performed for one month. Multidimensional fatigue inventory, Florida Shock Anxiety Scale, and Florida Patient Acceptance Scale were used. Data were analyzed to perform the analysis of data through SPSS, using independent and paired-t test, Mann-Whitney U test, Wilcoxon test, and ANCOVA.

RESULTS

Before the intervention, no significant difference was observed between the two groups with regard to fatigue, shock anxiety, and ICD acceptance. However, after the intervention, a significant difference was found between the two groups with regard to fatigue, shock anxiety, and ICD acceptance (P < 0.05).

CONCLUSION

This study showed that virtual interactive nurse-led support group intervention reduced fatigue and shock anxiety and improved the ICD acceptance.

PRACTICE IMPLICATIONS

This flexible, accessible, and interactive nurse-led support group intervention is suggested to be used for ICD patients.

TRIAL REGISTRATION

This trial was registered and approved by Iranian Registry of Clinical Trials (Trial Id: 60,738, date: (24/02/2022). ( https://www.irct.ir/trial/60738 ).

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit

AIMS

Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter-defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features.

METHODS AND RESULTS

A multicentre study of 1010 patients (64.9 ± 10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF ≤ 35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-s ECG was obtained within 90 days before ICD implantation, and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n = 550) from Hospital A to predict ICD non-arrhythmic mortality at three-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n = 460). At three-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 [95% confidence intervals (CI) 0.80-1.00] during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84).

CONCLUSIONS

ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within three years after device implantation in a primary prevention population, with robust performance in an independent cohort.