Asselbergs, Folkert W.

Abstract Background Increased QRS voltages in athletes’ ECGs are attributed to electrophysiological cellular adaptations, complicating the detection of left ventricular hypertrophy (LVH). Conversely, higher extracellular volume (ECV), indicative of diffuse fibrosis, reduces QRS voltages – so-called ‘relative voltage deficiency’– and can mask true LVH on ECG. Purpose To investigate sex-specific associations between QRS voltages and myocardial ECV and assess the impact of ECV on false-positive and negative ECG-LVH detection in elite athletes. Methods We conducted a cross-sectional, sex-specific, contrast-enhanced CMR study in healthy, elite athletes with normal ECGs and no complete right bundle branch blocks. Myocardial ECV (%) was measured from pre- and post-contrast T1 mapping. LVH on CMR was defined as an indexed LVM >75 g/m² (men) or >59 g/m² (women). Primary analyses evaluated correlations between ECV, LVM, and summed 12-lead ECG voltages (including voltage/LVM to assess independence), as well as Sokolow-Lyon and Cornell voltages. Secondary multivariable logistic regression examined the association of ECV on false-positive and false-negative ECG-LVH rates using Sokolow-Lyon (≥3.5 mV) and sex-specific Cornell (≥2.8 mV in men; ≥2.3 mV in women) criteria for CMR-LVH. Results In 162 elite athletes (median age 25 years, 44% female, 45% endurance, 96% Caucasian), 23% met Sokolow-Lyon criteria, 6% met Cornell criteria, and 17% had LVH on CMR. The median myocardial ECV was 23% (range 19–31%), which was lower in male compared to female athletes (22% [Q1-Q3: 21–23%] vs 25% [23–26%]; p<0.001). In both sexes, summed 12-lead voltages were inversely correlated with ECV (ρ=–0.36 to –0.37; p=0.001), independent of LVM (voltage/LVM: ρ=–0.33; p=0.004). Only in female athletes did 12-lead voltages correlate with LVM (ρ=0.37; p=0.001). Logistic regression confirmed that lower ECV was associated with an increased likelihood of false-positive Sokolow-Lyon (OR: 0.64 per 1% ECV increment; p=0.011), independent of body mass index (OR: 0.77 per kg/m²; p=0.039) male sex (OR: 2.7; p=0.123) and age (OR: 0.93 per year; p=0.075; AUROC 0.82). Higher ECV conferred greater odds of a false-negative (OR 3.98 per %; p=0.041), though this was limited by small LVH-positive sample size. In Cornell-based models, lower ECV was not statistically significantly associated with increased false-positive rate (OR 0.59; p=0.059). Conclusion In elite athletes, lower ECV ("denser" myocardium) is associated with increased QRS voltages and higher false-positive ECG-LVH rate, particularly for Sokolow-Lyon, while higher ECV may dampen voltages and can mask true LVH on the ECG. Although sex-specific criteria partly mitigate ECV-related effects, these findings demonstrate inherent limitations of voltage-based LVH criteria in athletes. Unexpectedly low QRS voltages in athletes with otherwise high LVM may imply increased ECV, though further research is warranted.Figure:Likelihood of False Pos SL rate Table:Population characteristics

Importance Little research has been done on post-COVID symptoms at 24 months postinfection and on the association these may have on health-related quality of life (HRQOL). Objective We assessed the prevalence and severity of post-COVID symptoms and quantified EuroQol 5 Dimension 5 Level (EQ-5D-5L), self-perceived health question (EuroQol Visual Analogue Scale (EQ-VAS)) and health utility scores (HUS) up to 24 months follow-up. Design The longitudinal multiple cohort CORona Follow-Up (CORFU) study combines seven COVID-19 patient cohorts and a survey among the general public. The participants received questionnaires on several time points. Participants were stratified by: without a known SARS-CoV-2 infection (control group), proven SARS-CoV-2 infection but non-hospitalised, proven SARS-CoV-2 infection hospitalised to the ward, and proven SARS-CoV-2 infection hospitalised to the intensive care unit (ICU). Setting In this study, data of seven COVID-19 patient cohorts and a survey among the general public are included. Participants Former COVID-19 patients and controls participated in this cohort study. Main outcomes and measures Former COVID-19 patients and non-COVID-19 controls were sent questionnaires on symptoms associated with post-COVID condition. The CORFU questionnaire included 14 symptom questions on post-COVID condition using a five-level Likert-scale format. Furthermore, HRQOL was quantified using the EuroQol EQ-5D-5L questionnaire: EQ-VAS and the EQ-5D-5L utility score. The EQ-5D-5L questionnaire includes five domains that are scored on a five-point Likert scale: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Results A total of 901 participants (and 434 controls) responded at 24 months follow-up. In all former COVID-19 patients, the presence of post-COVID condition at 24 months was observed in 62 (42.5%, 95% CI 34.3% to 50.9%) of the non-hospitalised patients, 333 (65.0%, 95% CI 60.7% to 69.2%) of the hospitalised ward patients and 156 (63.2%, 95% CI 56.8% to 69.2%) of the ICU patients, respectively (p<0.001). The most common symptoms included fatigue, sleep problems, muscle weakness/pain and breathing issues, with hospitalised participants reporting most often having symptoms. Multiple post-COVID symptoms were significantly associated with EQ-5D-5L measures. The mean and SD of the EQ-VAS were 71.6 (17.9), 70.0 (17.3) and 71.4 (17.5) for non-hospitalised, ward and ICU participants, respectively, and 75.6 (17.7) for the controls (p<0.001). The HUS resulted in 0.81 (0.20), 0.77 (0.19) and 0.79 (0.22) for non-hospitalised, hospitalised ward and ICU participants, respectively, and 0.84 (0.19) for the control group (CG) (p<0.001). Conclusions Many former COVID-19 patients experience post-COVID symptoms at 24 months follow-up, with the highest prevalence in hospitalised participants. Also, former patients reported a lower HRQOL. Trial registration number The CORFU study was registered at clinicaltrials.gov (registration number NCT05240742).

Background We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high‐sensitivity cardiac troponin T, N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity C‐reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long‐term mortality risk. Methods and Results BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high‐frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker‐based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all‐cause death were evaluated using accelerated failure time models (median follow‐up, 9.1 years; 141 deaths). Three biomarker‐based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long‐term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44–0.93; P =0.018) compared with cluster 1, and 0.71 (95% CI, 0.39–1.32; P =0.281) compared with patients with a repeat ACS. Conclusions Patients with subphenotypes of post‐ACS with different all‐cause mortality risks during long‐term follow‐up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

Abstract Aims Evidence regarding the role of serial measurements of biomarkers for risk assessment in post-acute coronary syndrome (ACS) patients is limited. The aim was to explore the prognostic value of four, serially measured biomarkers in a large, real-world cohort of post-ACS patients. Methods and results BIOMArCS is a prospective, multi-centre, observational study in 844 post-ACS patients in whom 12 218 blood samples (median 17 per patient) were obtained during 1-year follow-up. The longitudinal patterns of high-sensitivity cardiac troponin T (hs-cTnT), N-terminal-pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), and growth differentiation factor 15 (GDF-15) were analysed in relation to the primary endpoint (PE) of cardiovascular mortality and recurrent ACS using multivariable joint models. Median age was 63 years, 78% were men and the PE was reached by 45 patients. The average biomarker levels were systematically higher in PE compared with PE-free patients. After adjustment for 6-month post-discharge Global Registry of Acute Coronary Events score, 1 standard deviation increase in log[hs-cTnT] was associated with a 61% increased risk of the PE [hazard ratio (HR) 1.61, 95% confidence interval (CI) 1.02–2.44, P = 0.045], while for log[GDF-15] this was 81% (HR 1.81, 95% CI 1.28–2.70, P = 0.001). These associations remained significant after multivariable adjustment, while NT-proBNP and hs-CRP were not. Furthermore, GDF-15 level showed an increasing trend prior to the PE (Structured Graphical Abstract). Conclusion Longitudinally measured hs-cTnT and GDF-15 concentrations provide prognostic value in the risk assessment of clinically stabilized patients post-ACS. Clinical Trial Registration The Netherlands Trial Register. Currently available at URL https://trialsearch.who.int/; Unique Identifiers: NTR1698 and NTR1106.

Introduction The variety, time patterns and long-term prognosis of persistent COVID-19 symptoms (long COVID-19) in patients who suffered from mild to severe acute COVID-19 are incompletely understood. Cohort studies will be combined to describe the prevalence of long COVID-19 symptoms, and to explore the pathophysiological mechanisms and impact on health-related quality of life. A prediction model for long COVID-19 will be developed and internally validated to guide care in future patients. Methods and analysis Data from seven COVID-19 cohorts will be aggregated in the longitudinal multiple cohort CORona Follow Up (CORFU) study. CORFU includes Dutch patients who suffered from COVID-19 at home, were hospitalised without or with intensive care unit treatment, needed inpatient or outpatient rehabilitation and controls who did not suffer from COVID-19. Individual cohort study designs were aligned and follow-up has been synchronised. Cohort participants will be followed up for a maximum of 24 months after acute infection. Next to the clinical characteristics measured in individual cohorts, the CORFU questionnaire on long COVID-19 outcomes and determinants will be administered digitally at 3, 6, 12, 18 and 24 months after the infection. The primary outcome is the prevalence of long COVID-19 symptoms up to 2 years after acute infection. Secondary outcomes are health-related quality of life (eg, EQ-5D), physical functioning, and the prevalence of thromboembolic complications, respiratory complications, cardiovascular diseases and endothelial dysfunction. A prediction model and a patient platform prototype will be developed. Ethics and dissemination Approval was obtained from the medical research ethics committee of Maastricht University Medical Center+ and Maastricht University (METC 2021-2990) and local committees of the participating cohorts. The project is supported by ZonMW and EuroQol Research Foundation. Results will be published in open access peer-reviewed scientific journals and presented at (inter)national conferences. Trial registration number NCT05240742 .

Abstract This article presents some of the most important developments in the field of digital medicine that have appeared over the last 12 months and are related to cardiovascular medicine. The article consists of three main sections, as follows: (i) artificial intelligence-enabled cardiovascular diagnostic tools, techniques, and methodologies, (ii) big data and prognostic models for cardiovascular risk protection, and (iii) wearable devices in cardiovascular risk assessment, cardiovascular disease prevention, diagnosis, and management. To conclude the article, the authors present a brief further prospective on this new domain, highlighting existing gaps that are specifically related to artificial intelligence technologies, such as explainability, cost-effectiveness, and, of course, the importance of proper regulatory oversight for each clinical implementation.