Explaining the Unexplained: A Practical Approach to Investigating the Cardiac Arrest Survivor

Sudden cardiac arrest (SCA) is a common cause of death. The majority of SCA is caused by ventricular arrhythmia due to underlying CHD. Aborted SCA with no apparent diagnosis after initial assessment with ECG, echocardiography and coronary assessment is referred to as unexplained cardiac arrest (UCA). Systematic evaluation of such patients may reveal a specific diagnosis in up to half of patients before a diagnosis of idiopathic VF is assigned. Specific diagnoses include inherited cardiac conditions, such as latent cardiomyopathies or inherited primary electrical disease. Identifying the cause of UCA is therefore not only critical for appropriate management of the SCA survivors to prevent recurrence, but also for their family members who may be at risk of the same condition. This review provides a tiered, systematic approach for the investigation of UCA.

Multiple anthropometric measures and proarrhythmic 12-lead ECG indices: A mendelian randomization study

BACKGROUND

Observational studies suggest that electrocardiogram (ECG) indices might be influenced by obesity and other anthropometric measures, though it is difficult to infer causal relationships based on observational data due to risk of residual confounding. We utilized mendelian randomization (MR) to explore causal relevance of multiple anthropometric measures on P-wave duration (PWD), PR interval, QRS duration, and corrected QT interval (QTc).

METHODS AND FINDINGS

Uncorrelated (r2 < 0.001) genome-wide significant (p < 5 × 10-8) single nucleotide polymorphisms (SNPs) were extracted from genome-wide association studies (GWAS) on body mass index (BMI, n = 806,834), waist:hip ratio adjusted for BMI (aWHR, n = 697,734), height (n = 709,594), weight (n = 360,116), fat mass (n = 354,224), and fat-free mass (n = 354,808). Genetic association estimates for the outcomes were extracted from GWAS on PR interval and QRS duration (n = 180,574), PWD (n = 44,456), and QTc (n = 84,630). Data source GWAS studies were performed between 2018 and 2022 in predominantly European ancestry individuals. Inverse-variance weighted MR was used for primary analysis; weighted median MR and MR-Egger were used as sensitivity analyses. Higher genetically predicted BMI was associated with longer PWD (β 5.58; 95%CI [3.66,7.50]; p = < 0.001), as was higher fat mass (β 6.62; 95%CI [4.63,8.62]; p < 0.001), fat-free mass (β 9.16; 95%CI [6.85,11.47]; p < 0.001) height (β 4.23; 95%CI [3.16, 5.31]; p < 0.001), and weight (β 8.08; 95%CI [6.19,9.96]; p < 0.001). Finally, genetically predicted BMI was associated with longer QTc (β 3.53; 95%CI [2.63,4.43]; p < 0.001), driven by both fat mass (β 3.65; 95%CI [2.73,4.57]; p < 0.001) and fat-free mass (β 2.08; 95%CI [0.85,3.31]; p = 0.001). Additionally, genetically predicted height (β 0.98; 95%CI [0.46,1.50]; p < 0.001), weight (β 3.45; 95%CI [2.54,4.36]; p < 0.001), and aWHR (β 1.92; 95%CI [0.87,2.97]; p = < 0.001) were all associated with longer QTc. The key limitation is that due to insufficient power, we were not able to explore whether a single anthropometric measure is the primary driver of the associations observed.

CONCLUSIONS

The results of this study support a causal role of BMI on multiple ECG indices that have previously been associated with atrial and ventricular arrhythmic risk. Importantly, the results identify a role of both fat mass, fat-free mass, and height in this association.

Familial Evaluation in Idiopathic Ventricular Fibrillation: Diagnostic Yield and Significance of J Wave Syndromes

[Figure: see text].

Type 8 long QT syndrome: pathogenic variants in CACNA1C-encoded Cav1.2 cluster in STAC protein binding site

Aims

Pathogenic gain-of-function variants in CACAN1C cause type-8 long QT syndrome (LQT8). We sought to describe the electrocardiographic features in LQT8 and utilize molecular modelling to gain mechanistic insights into its genetic culprits.

Methods and results

Rare variants in CACNA1C were identified from genetic testing laboratories. Treating physicians provided clinical information. Variant pathogenicity was independently assessed according to recent guidelines. Pathogenic (P) and likely pathogenic (LP) variants were mapped onto a 3D modelled structure of the Cav1.2 protein. Nine P/LP variants, identified in 23 patients from 19 families with non-syndromic LQTS were identified. Six variants, found in 79% of families, clustered to a 4-residue section in the cytosolic II–III loop region which forms a region capable of binding STAC SH3 domains. Therefore, variants may affect binding of SH3-domain containing proteins. Arrhythmic events occurred in similar proportions of patients with II–III loop variants and with other P/LP variants (53% vs. 48%, P = 0.41) despite shorter QTc intervals (477 ± 31 ms vs. 515 ± 37 ms, P = 0.03). A history of sudden death was reported only in families with II–III loop variants (60% vs. 0%, P = 0.03). The predominant T-wave morphology was a late peaking T wave with a steep descending limb. Exercise testing demonstrated QTc prolongation on standing and at 4 min recovery after exercise.

Conclusion

The majority of P/LP variants in patients with CACNA1C-mediated LQT8 cluster in an SH3-binding domain of the cytosolic II–III loop. This represents a ‘mutation hotspot’ in LQT8. A late-peaking T wave with a steep descending limb and QT prolongation on exercise are commonly seen.