Jussi Aleksi Hernesniemi

There is no consensus on a risk threshold for sudden cardiac death (SCD) that could be used in practical design and evaluation of prediction models and decisions regarding implantable cardioverter-defibrillator (ICD) therapy. Baseline assumptions for a simulation framework were derived from previous randomized controlled trials ( n = 18) to identify minimal average SCD risk threshold that would translate to mortality benefit by ICD therapy also considering the effect of competing non-sudden mortality. ICD efficacy to prevent SCDs and other data for simulations were estimated using inverse-variance weighted meta-analysis of included trials. Number needed to treat (NNT) was evaluated over a 5- and 10-year horizons (≤ 20 defined as clinically relevant). Correlation analysis confirmed average annual SCD incidence in trial populations as the key factor associating with ICD therapy effectiveness to reduce mortality (Pearson’s r = 0.653, p < 0.01). In a simulation assuming 5% annual non-sudden mortality (pooled estimate of included RCTs) and a 56% (48–62%) efficacy for ICDs to reduce SCDs or similar events, 3% average annual SCD risk (≈ 12% over 5 years) emerged as the lowest practical threshold even after controlling for excess (overlapping) mortality among those saved successfully from SCD by ICD therapy. In populations with very low incidence of non-sudden mortality (1%), the lowest practical threshold over a longer 10-year time horizon was 1.3% (assuming no overlapping mortality). Even under substantial competing risk, populations with an average 3% (or higher) annual SCD incidence most likely would benefit from ICD therapy. Lower threshold may be considered only in populations with very low non-sudden mortality.

Background: There is no consensus on a risk threshold for sudden cardiac death (SCD) that could be used in practical design and evaluation of prediction models and decisions regarding implantable cardioverter-defibrillator (ICD) therapy. Methods: Baseline assumptions for a simulation framework were derived from previous randomized controlled trials (n=18) to identify minimal SCD risk threshold that would translate to mortality benefit by ICD therapy also considering the effect of competing non-sudden mortality. ICD efficacy to prevent SCDs and other data for simulations were estimated using inverse-variance weighted meta-analysis of included trials. Number needed to treat (NNT) was evaluated over a five-year horizon (≤21 defined as clinically relevant). Results: Correlation analysis confirmed annual SCD incidence in trial populations as the key factor associating with ICD therapy effectiveness to reduce mortality (Pearson’s r=0.653, p<0.01). In a simulation assuming 5% annual non-sudden mortality (pooled estimate of included RCTs) and a 56% (48-62%) efficacy for ICDs to reduce SCDs or similar events, 3% annual SCD risk (≈12% over five years) emerged as the lowest practical threshold even after controlling for excess (overlapping) mortality among those saved successfully from SCD by ICD therapy. The theoretical minimum threshold for annual SCD risk is 2.0%, 2.5% and 3.5% for populations with the annual incidence of non-sudden deaths 2%, 5% and 10% (assuming no overlapping mortality). Conclusions: Even under substantial competing risk, a 3% annual SCD threshold appears an optimal minimum threshold for identifying patients most likely to benefit from ICD therapy if severe mortality overlap is not observed. Key Questions: What is the minimal risk threshold after which ICD therapy will likely lead to meaningful reduction in overall mortality. This information is needed in practical design of clinical trials and evaluation and development of prediction models Key Finding: Analysis of the data extracted from previous randomized controlled trials revealed that annual SCD risk should be at least 3% in most scenarios (with the annual incidence of non-sudden mortality ≤5%) for ICD therapy to be effective. Take-home Message: Primary prevention SCD and risk models targeted to identify high-risk individual should aim for identifying patients with 3% or higher annual risk for SCD.

We evaluated the performance of 12-channel ECG in predicting sudden cardiac death across different time intervals using a retrospective data set of 17,625 high-risk cardiac patients who underwent coronary angiography (2007–2018) with follow-up data until 2022. Extreme gradient boosting using 12SL Marquette software-derived parameters from digital ECG recording was used to train and validate models using a random 80/20 split. Model performance was evaluated in both unbalanced and risk-factor-balanced case-control sets. Using single ECG, both long-term (from baseline ECG) and short-term predictions (from the last recorded ECG) achieved a modest area under the curve (AUC) of 0.68 in the unbalanced validation and 0.59/0.63 in the balanced validation (long-/short-term). Adding clinical risk factor data resulted in AUC 0.70/0.71 (unbalanced) and 0.64/0.62 (balanced) for long- and short-term prediction. Adding data of observed ECG changes during follow-up for short-term prediction resulted in the best model performance (0.72/0.66; unbalanced/balanced).

Background: Interatrial block (IAB) in an ECG, manifesting atrial pathology, is a possible risk factor for atrial arrhythmias and ischemic stroke. IAB is often transiently expressed, posing a major challenge for risk estimation when only a limited number of ECGs are available. Methods: This is a retrospective study of 9674 consecutive patients diagnosed with acute coronary syndrome (ACS) between 2007 and 2018. All standard ECGs registered between January 2007 and March 2023 (n=206 668) were digitally stored and analyzed using the General Electric Marquette 12SL algorithm for the presence of an IAB, atrial fibrillation, and atrial flutter. The population was stratified by the extent of the IAB, and patients without an IAB served as controls. Clinical phenotype data and end point data for ischemic stroke were collected by a full disclosure review of hospital records and death certificate data including written accounts of deaths. Results: The prevalence of an advanced IAB was 8.5% if all previous ECGs, including those recorded during ACS, were screened, whereas it was only 2.4% during ACS. The risk of atrial fibrillation/atrial flutter after ACS increased with the severity of the IAB, and the highest risk was attributable to advanced IAB (age‐ and sex‐adjusted hazard ratio [HR], 2.87 [95% CI, 2.48–3.32]; P P 2 DS ₂ ‐VASc score (HR, 1.95 [95% CI, 1.47–2.58]; P <0.00001). Conclusions: Automatic detection of an IAB from serial ECGs reveals a strong association between atrial pathology and the risk of atrial fibrillation, atrial flutter, and ischemic stroke in patients with ACS.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

In the meta-analysis, psoas muscle measurements were significantly associated with mortality among men (p < 0.05), with high heterogeneity in the associations across all cohorts. There was very little difference in the association between PMA and PMD and mortality (HR 0.83, 95% CI 0.69-0.99, p = 0.002; HR 0.85, 95% CI 0.77-0.94, p = 0.041 for one SD increase in PMA and PMD in the random effects model). Combining PMA and PMD into one composite variable by multiplying their values together showed the most robust association in terms of the magnitude of the effect size in men (HR, 0.77; 95% CI 0.73-0.87, p < 0.001). Indexing PMA to body size did not result in any significant differences in this association. Among women, psoas muscle measurements were not associated with long-term mortality in this meta-analysis. Different psoas muscle measurements were significantly and very similarly associated with mortality among men but not among women. No single measurement stands out, although combining PMA and PMD seems to be a slightly stronger estimate in terms of effect size and should be considered in further studies.