Associations between common ECG abnormalities and out-of-hospital cardiac arrest

[Associated factors with the occurrence of in-hospital cardiac arrest in patients admitted to internal medicine wards for non-cardiovascular causes]

BACKGROUND AND OBJECTIVE

In-hospital cardiac arrest (IHCA) has a low survival rate, so it is essential to recognize the cases with the highest probability of developing it. The aim of this study is to identify factors associated with the occurrence of IHCA.

MATERIAL AND METHODS

A single-center case-control study was conducted including 65 patients admitted to internal medicine wards for non-cardiovascular causes who experienced IHCA, matched with 210 admitted controls who did not present with IHCA.

RESULTS

The main reason for admission was pneumonia. The most prevalent comorbidity was arterial hypertension. Four characteristics were strongly and independently associated with IHCA presentation, these are electrical left ventricular hypertrophy (LVH) (OR: 13.8; 95% IC: 4.7-40.7), atrial fibrillation (OR: 9.4: 95% CI: 4.3-20.6), the use of drugs with known risk of torsades de pointes (OR: 2.7; 95% CI: 1.3-5.5) and the combination of the categories known risk plus conditional risk (OR: 17.1; 95% CI: 6.7-50.1). The first two detected in the electrocardiogram taken at the time of admission.

CONCLUSION

In admitted patients for non-cardiovascular causes, the use of drugs with a known risk of torsades de pointes, as well as the detection of electrical LVH and atrial fibrillation in the initial electrocardiogram, is independently associated with a higher probability of suffering a IHCA.

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

BACKGROUND

Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events.

METHODS

This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool.

FINDINGS

2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation.

INTERPRETATION

ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies.

FUNDING

This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

OUP accepted manuscript

More than 40 years after the first implantable cardioverter-defibrillator (ICD) implantation, sudden cardiac death (SCD) still accounts for more than five million deaths worldwide every year. Huge efforts in the field notwithstanding, it is now increasingly evident that the current strategy of long-term prevention based on left ventricular ejection fraction as the key selection criterion is actually of very limited impact, also because the largest absolute numbers of SCD are encountered in the general population not known to be at risk. It has been recently reemphasized that SCD is often not so sudden, with almost half of the victims experiencing typical warning symptoms preceding the event. Importantly, heeded and prompt medical attention can dramatically improve survival. Essentially, such timely action increases the chances of the SCD event being witnessed by emergency medical services and provides the opportunity for early intervention. In addition, newer technologies incorporating digital data acquisition, transfer between interconnected devices, and artificial intelligence, should allow dynamic, real-time monitoring of diverse parameters and therefore better identification of subjects at short-term SCD risk. Along with warning symptoms, these developments allow a new approach of near-term prevention based on the hours and minutes preceding SCD. In the present review, we challenge the current paradigm of mid- and long-term prevention using ICD in patients at the highest risk of SCD, and introduce a complementary concept applicable to the entire population that would aim to pre-empt SCD by timely detection and intervention within the minutes or hours prior to the event.

Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project

Introduction

Early recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information.

Aim

To describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA.

Methods

The RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA.

Conclusion

The RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.

HIV Infection Is Associated With Variability in Ventricular Repolarization: The Multicenter AIDS Cohort Study (MACS)

BACKGROUND

People living with human immunodeficiency virus (HIV+) have greater risk for sudden arrhythmic death than HIV-uninfected (HIV-) individuals. HIV-associated abnormal cardiac repolarization may contribute to this risk. We investigated whether HIV serostatus is associated with ventricular repolarization lability by using the QT variability index (QTVI), defined as a log measure of QT-interval variance indexed to heart rate variance.

METHODS

We studied 1123 men (589 HIV+ and 534 HIV-) from MACS (Multicenter AIDS Cohort Study), using the ZioXT ambulatory electrocardiography patch. Beat-to-beat analysis of up to 4 full days of electrocardiographic data per participant was performed using an automated algorithm (median analyzed duration [quartile 1-quartile 3]: 78.3 [66.3-83.0] hours/person). QTVI was modeled using linear mixed-effects models adjusted for demographics, cardiac risk factors, and HIV-related and inflammatory biomarkers.

RESULTS

Mean (SD) age was 60.1 (11.9) years among HIV- and 54.2 (11.2) years among HIV+ participants (P<0.001), 83% of whom had undetectable (<20 copies/mL) HIV-1 viral load (VL). In comparison with HIV- men, HIV+ men had higher QTVI (adjusted difference of +0.077 [95% CI, +0.032 to +0.123]). The magnitude of this association depended on the degree of viremia, such that in HIV+ men with undetectable VL, adjusted QTVI was +0.064 (95% CI, +0.017 to +0.111) higher than in HIV- men, whereas, in HIV+ men with detectable VL, adjusted QTVI was higher by +0.150 (95% CI, 0.072-0.228) than in HIV- referents. Analysis of QTVI subcomponents showed that HIV+ men had: (1) lower heart rate variability irrespective of VL status, and (2) higher QT variability if they had detectable, but not with undetectable, VL, in comparison with HIV- men. Higher levels of C-reactive protein, interleukin-6, intercellular adhesion molecule-1, soluble tumor necrosis factor receptor 2, and soluble cluster of differentiation-163 (borderline), were associated with higher QTVI and partially attenuated the association with HIV serostatus.

CONCLUSIONS

HIV+ men have greater beat-to-beat variability in QT interval (QTVI) than HIV- men, especially in the setting of HIV viremia and heightened inflammation. Among HIV+ men, higher QTVI suggests ventricular repolarization lability, which can increase susceptibility to arrhythmias, whereas lower heart rate variability signals a component of autonomic dysfunction.