Dörr, Marcus

Abstract Cardiovascular disease is the leading cause of morbidity and mortality globally, imposing a substantial patient burden. Digital biomarkers derived from digital health technologies enable quantifiable, near-continuous, real-world capture of pathophysiological signals and are increasingly used across cardiovascular medicine. Their growth is often supported by big data analytics, artificial intelligence and connected sensors within the Internet of Things. This scientific statement systematically reviews digital biomarkers, their clinical utility across the cardiovascular care continuum, and summarizes major methodological and implementation challenges. We searched PubMed/MEDLINE, Embase and Scopus for phase III–IV cardiovascular trials published between 1 January 2019 and 1 August 2024 that collected digital biomarkers. We identified 541 records and included 32 trials (40 reports) enrolling 32 246 participants (44.71% women; mean age 66.2 ± 11.0 years). Heart failure was the most frequent target condition (16 trials, 50%), followed by cardiac implantable electronic devices trials (12 trials, 38%). Digital biomarkers included cardiac rhythm and heart rate, blood pressure, body weight, impedance-based measures, haemodynamic pressures, and physical activity and sleep patterns captured using diverse sensor technologies. Methodological quality was good (modal quality score 5; range 3–7), although most trials were unblinded. Current studies suggest potential clinical utility in selected cardiovascular conditions. Implementation remains challenging because of measurement variability, privacy and regulatory requirements, affordability and reimbursement barriers and inequities in access. Robust validation and outcome- and cost-effectiveness evidence are still needed. Registration number: CRD42024573879.

Abstract Background Myocardial feature-tracking (FT) strain from cardiovascular magnetic resonance (CMR) has incremental prognostic value beyond traditional imaging biomarkers. Clinical use is limited by the absence of population-specific healthy reference ranges. This study provides age-, sex-, and ethnicity-specific reference ranges for CMR-derived FT strain from the Healthy Hearts Consortium (HHC). Methods The HHC is an international collaboration comprising CMR scans from 9,088 verified healthy adults across six centres, covering the entire adult age spectrum and with diverse ethnic background. Standardised CMR acquisition protocols were used. Image analysis was performed by two experienced readers following pre-specified standard operating procedures and using two commercially available software platforms (cvi42®, Circle Cardiovascular Imaging Inc.; suiteHEART®, Neosoft LLC). The following metrics were extracted: i) global FT strain metrics from both ventricles (longitudinal, circumferential, and radial) and both atria (longitudinal); ii) strain rate; iii) Mitral/Tricuspid annular plane systolic excursion (MAPSE, TAPSE); iv) rapid strain (long-axis, junctional strain) from the left ventricle and both atria. Quality control included visual inspection of all images and the removal of biologically non-sensical values and extreme statistical outliers. Reference ranges were calculated by age group (deciles: 20-80 years), sex (male/female), and ethnicity (White, Black, Chinese, South Asian, Mixed/Other) using the 95% prediction interval method. Technical variations due to scanner vendor, software platform, and field strength (1.5T vs. 3.0T), were analysed. Results The study included 4,452 men (49%) and 4,636 women (51%). Ages ranged from 18 to 83 (mean 61±13) years. The sample included diverse ethnic backgrounds [White (81.7%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.7%), and Chinese (3.7%)]. Women demonstrated significantly higher absolute strain values than men across. Absolute myocardial strain values were higher in younger age groups and lower in older age groups, most notably in left global circumferential and longitudinal strain. Ethnic differences in strain were most pronounced between White and Black as well as between White and Chinese individuals regarding left and right ventricular strain values, while left and right atrial strain values as well as rapid strain values, were similar across ethnic groups. Conclusion This study provides comprehensive, population-specific reference ranges for FT-CMR-derived myocardial strain, addressing a clinical knowledge gap. These findings inform clinical interpretation, improving diagnostic accuracy and risk stratification in different populations. This work represents a significant step towards a broader clinical implementation of myocardial strain analysis in routine cardiovascular care, ensuring more accurate classification of findings across varying demographic groups.

Abstract Introduction Cardiovascular magnetic resonance (CMR) imaging enables precise, non-invasive assessment of cardiac structure and function, yet the clinical utility of CMR metrics is limited by the lack of comprehensive, population-specific reference values. By directly reanalyzing data from a large, multi-ethnic cohort rather than relying solely on pooled literature, could enable the establishment of standardized reference ranges and severity grading thresholds. Aim To provide comprehensive, age-, sex-, and ethnicity-stratified CMR reference ranges and severity grading thresholds for key ventricular and atrial parameters. Methods We analyzed over 9,000 CMR scans from the Healthy Hearts Consortium (HHC), comprising six international cohorts. All individuals were free from overt cardiovascular disease and major comorbidities, ensuring a verified healthy sample. Two automated segmentation software solutions—cvi42 (Circle Cardiovascular Imaging) and suiteHEART (Neosoft)—were used to quantify left and right ventricular volumes, myocardial mass, and atrial volumes in standard cine stacks. All endorsed segmentation modes were applied. Quality control protocol combined expert visual review and statistical outlier analysis. We derived 95% prediction intervals (PI) to define the normal range, with additional severity thresholds for mild (95–99.73% PI), moderate (99.73–99.99% PI), and severe (>99.99% PI) deviation from the reference range. Results The final dataset comprised of 9,059 verified healthy adults aged 18–83 years (mean 61 ± 13 years), with a near-equal sex distribution (51% women). Demographics included White (81.6%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.8%), and Chinese (3.7%) participants. Results demonstrated that sex, age, and ethnicity strongly influenced cardiac indices. Men had higher indexed LV and RV volumes and greater myocardial mass than women. Black participants presented the highest indexed LV myocardial mass across age groups, whereas Chinese participants had comparatively lower myocardial mass values (Figure 1). In contrast, atrial volumes remained relatively stable across age categories. Comparative analysis showed minor differences between the two software tools, particularly for atrial parameters; however, derived ejection fractions remained consistent. We established age-, sex-, and ethnicity-stratified reference ranges for key CMR parameters. To facilitate clinical interpretation, we defined severity thresholds corresponding to mild, moderate, and severe abnormalities. Conclusion This large-scale, international effort addresses prior limitations in CMR interpretation by offering robust, contemporary reference ranges and severity grading applicable to diverse populations and imaging protocols. Our severity grading thresholds support consistency in clinical reporting and decision-making, enabling clearer identification of early subclinical changes and more accurate monitoring of disease progression.Subset of CMR value ranges in females

Abstract Background Myocardial strain derived from cardiovascular magnetic resonance (CMR) using feature tracking (FT) offers incremental prognostic value beyond traditional imaging markers. However, its broader clinical application is hindered by the lack of population-specific reference ranges. This study establishes age-, sex-, and ethnicity-specific normative data for FT-CMR-derived myocardial strain metrics from the Healthy Hearts Consortium (HHC). Methods The HHC is a large, international, multicohort initiative comprising CMR data from 9,088 rigorously screened healthy adults across six centers. The cohort spans the full adult age range and includes greater ethnic diversity than any comparable dataset. CMR scans were acquired using standardized protocols. Image analysis followed pre-specified standard operating procedures and was performed by two experienced readers using two commercial platforms (CVI42®, Circle Cardiovascular Imaging; suiteHEART®, Neosoft). Metrics extracted included: (i) global longitudinal, circumferential, and radial FT strain for both ventricles and atria; (ii) strain rate; (iii) annular plane systolic excursion (MAPSE, TAPSE); and (iv) rapid strain metrics (long-axis, junctional strain) for the left ventricle and atria. Comprehensive quality control involved visual review and exclusion of biologically implausible or extreme outlier values. Reference ranges were calculated using the 95% prediction interval method, stratified by age deciles (20–80 years), sex, and ethnicity (White, Black, Chinese, South Asian, Mixed/Other). The impact of technical factors—scanner vendor, field strength (1.5T vs 3.0T), and software platform—was also assessed. Results The study included 4,452 men (49%) and 4,636 women (51%), aged 18 to 83 years (mean 61 ± 13). The cohort was ethnically diverse: White (81.7%), South Asian (5.6%), Mixed/Other (5.3%), Black (3.7%), and Chinese (3.7%). Women consistently exhibited higher absolute strain values than men. A clear age-related decline in strain was observed, particularly in left ventricular global circumferential and longitudinal strain. Ethnic differences were most pronounced between White and Black, and White and Chinese individuals for RV and LV strain metrics; in contrast, RA, LA, and rapid strain metrics showed minimal ethnic variation. Conclusion This study delivers the most comprehensive age-, sex-, and ethnicity-specific reference ranges to date for FT-CMR-derived myocardial strain. These normative values support improved clinical interpretation, aiding diagnosis and risk stratification in diverse populations. The findings mark a pivotal advancement towards the routine integration of myocardial strain assessment in cardiovascular imaging practice.

Abstract Aims Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP). Methods and results We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants’ median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition, and CMR labelling as important factors. Conclusion Our findings support the feasibility of scalable DL-based screening tools for the prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

Objective: Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodelling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH; however, its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes. Methods: ECG biomarkers were extracted from the 12-lead ECG of 20 439 hypertensive patients in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM), and random forest. The models were trained in 80% of the participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensive participants from the Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure. Results: Among UKB participants 19 408 had normal LV, 758 LV remodelling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable in UKB. SVM (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) was taken forward for external validation with similar results in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (hazard ratio 3.42, 95% CI 1.06–9.86). Conclusion: ECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes.