Adam Ioannou

Aims: To determine the prevalence of asymmetric septal hypertrophy (ASH) across health and in diseases associated with left ventricular hypertrophy (LVH), including hypertrophic cardiomyopathy (HCM). Methods and results: We analysed two complementary datasets. First, a large UK Biobank healthy reference cohort (n = 4020) was used to model demographic determinants of septal-to-lateral wall thickness ratio (SLR) and to provide the reference group for age-, sex-, and body surface area-adjusted comparisons. Second, a multi-cohort clinical CMR dataset included 1655 subjects comprising local healthy volunteers, athletes, patients with hypertension, aortic stenosis, Fabry disease, amyloid light chain (AL) amyloidosis, amyloid transthyretin (ATTR) amyloidosis, and HCM. LVH was defined by maximum wall thickness (MWT) ≥15 mm and asymmetry defined as SLR≥1.3. Left ventricular morphology was classified as normal, asymmetric remodelling (SLR≥1.3; MWT<15 mm), symmetric hypertrophy (SLR<1.3; MWT≥15 mm) or ASH (SLR≥1.3; MWT≥15 mm). ASH was highly prevalent in HCM (61%) but was also observed in ATTR amyloidosis (37%), AS (26%), and AL amyloidosis (23%). In the healthy reference cohort, a higher septal-to-lateral ratio was independently associated with older age, female sex, and larger body surface area (P < 0.001). After adjustment for these demographic factors, only HCM retained a clinically meaningful excess in septal asymmetry (ΔSLR = +0.27; P < 0.001). In contrast, the apparent asymmetry in non-HCM cohorts was partly explained by demographic variation. After adjustment, only HCM showed a clinically meaningful excess in SLR, whereas other disease cohorts showed either no material difference or small negative differences relative to healthy controls. Conclusion: ASH is a common but non-specific finding in diseases associated with LVH. Septal asymmetry increases with age and body-size and is more pronounced in women. After adjusting for age, sex, and body-size, disproportionate septal asymmetry was most characteristic of HCM.

Aims: Serial imaging demands precision—particularly for cardiotoxicity screening where small changes gatekeep major decisions. Automated measurement using artificial intelligence (AI)-based techniques should reduce variability and increase confidence for detection of true change. We directly compared the precision of fully automated AI vs. expert manual analysis of left ventricular function by echocardiography. Methods and results: Consecutive cancer patients referred for cardiotoxicity monitoring underwent same-day repeat 2D echocardiography [n = 60, 83% female, mean left ventricular ejection fraction (LVEF) 57 ± 7%], with 3D acquisition where feasible (n = 45). All 2D images underwent blinded analysis by a fully automated analysis software and four experts. Quantitative 2D LVEF measurement was feasible in 96% scans by both methods. Mean absolute difference (MAD) between repeat scans was significantly lower for AI than experts for both 2D LVEF {3.6% [confidence interval (CI) 2.8–4.4] vs. 6.6% [CI 5.3–7.9], P = 0.011} and global longitudinal strain [1.5% (CI 1.1–1.9) vs. 2.5% (CI 2.0–3.0), P = 0.006]. In participants with 3DE datasets, 3D LVEF MAD was lower than manual 2D LVEF [3.6% (CI 2.8–4.6) vs. 6.4% (CI 5.0–8.0), P = 0.006] but comparable to AI-derived 2D LVEF [3.5% (CI 2.9–4.2), P = 0.4]. In a subset (n = 48) also with same-day cardiovascular magnetic resonance (CMR) studies, AI 2DE analysis demonstrated stronger agreement with CMR than expert 2DE analysis [LVEF MAD 5.0% (CI 4.1–5.9) vs. 7.8% (CI 6.6–9.0) P = 0.006]. Conclusion: In cancer patients at risk of cardiotoxicity, the precision of 2D echocardiographic AI analysis exceeds that of experts, matching that of 3D LVEF assessment. Trial Registration: NCT06817044 Lay summary: Some cancer treatments may weaken the heart. As such, at-risk patients have repeat heart scans (echocardiograms) over the course of their cancer treatment. Doctors monitor these scans over time and watch closely for any change; even a small decline in heart function can trigger a major decision, such as pausing life-saving cancer therapy. It is therefore vital that these measurements are as consistent or ‘precise’ as possible. The problem is that measurements taken by hand can vary widely, and ‘precision’ may be low, even when performed by skilled experts. This makes it hard to know whether a change in heart function across follow-up scans is real or not and whether any action is therefore needed. We tested whether artificial intelligence (AI) software could measure heart function more precisely than experts. Sixty cancer patients each had two echocardiographic heart scans in close succession on the same day. This approach purposefully reduces the impact of other factors (such as differences in the person or equipment performing the heart scan) that might otherwise mean results differ. We then compared how closely the repeat measurements of heart function matched when performed by AI vs. four echocardiography experts. The AI was more precise, giving closer results between the two scans, whilst the experts’ results varied more. In short, AI can measure heart function more precisely than experts in patients with cancer. This could give doctors greater confidence that any change seen on a scan is genuine, helping them make safer, better-informed decisions.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography. METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; left ventricular hypertrophy controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; left ventricular hypertrophy controls, 222). RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91–0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87–0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90–0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91–0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91–0.95] versus AUC, 0.88 [95% CI, 0.85–0.90]; P <0.001). CONCLUSIONS: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.

Systemic light-chain (AL) amyloidosis is a rare clonal plasma cell disorder characterized by the production of abnormal immunoglobulin free light-chains that misfold into amyloid fibrils and deposit in the extracellular matrix of tissues. Despite being a multisystemic disease process, the presence and severity of cardiac involvement remains the main determinant of prognosis. Improved understanding of the underlying pathophysiology has resulted in transformative changes in both diagnostics and treatment. Improvements in multimodality cardiac imaging have enabled accurate diagnosis, facilitated rapid initiation of treatment and allowed the direct visualization changes in the myocardial substrate in response to chemotherapy. Significant progress has also been made through leveraging treatments that directly target the underlying abnormal plasma cell clone responsible the production of the amyloidogenic immunoglobulin free light-chains. Current treatment options successfully target amyloid production, but novel anti-amyloid therapies seek to target amyloid fibrils that have already deposited in the organs and facilitate their removal through an immune-mediated degradation process are at advanced stages of development. These treatments have the potential to induce disease regression by depleting amyloid deposits and if successful will represent a significant step forward in the treatment of systemic AL amyloidosis, especially for patients with advanced cardiac disease.

Background and Aims: Cardiovascular magnetic resonance (CMR) and extracellular volume (ECV) mapping can measure amyloid burden in vivo. This study sought to assess the natural history of transthyretin amyloid cardiomyopathy (ATTR-CM) in terms of amyloid deposition burden (ECV) over time, changes in cardiac amyloid load following treatment with patisiran, and the association between change in ECV and mortality. Methods: All 189 patients (untreated = 119, patisiran = 70) underwent assessment with CMR at baseline, 160 patients (untreated = 94, patisiran = 66) had a 1-year follow-up CMR and 75 patients (untreated = 42, patisiran = 33) had a 2-year follow-up CMR, of whom 36 patients (untreated = 17, patisiran = 29) had follow-up CMR at both timepoints. CMR response was graded by change in ECV: progression (≥5% increase), stable (<5% change) and regression (≥5% decrease). Results: In untreated patients, 36% progressed at 1 year which significantly increased to 62% at 2 years. Mean increase in ECV of 4.1% (after 1 year) and 6.8% (after 2 years) was observed and associated with significant worsening in biomarkers and structural parameters. No significant difference in mean ECV was observed following treatment with patisiran at both timepoints. Stable ECV was observed in 88% of patients (after 1 year) and 100% of patients (after 2 years). Structural parameters remained stable following treatment. ECV progression after 1 year was independently associated with mortality after adjusting for known predictors (hazard ratio 2.021; 95% confidence interval 1.081–3.781; P = .028). Conclusions: CMR with ECV mapping can track changes in cardiac amyloid burden and treatment response in ATTR-CM, with changes in ECV being independently associated with outcomes.

Background: Cardiac amyloidosis (CA) is the leading cause of mortality in systemic amyloidosis, highlighting the need for accurate risk assessment to guide patient management. While the diagnostic value of cardiac MR (CMR) parametric mapping is well established, its prognostic utility remains inconsistent across studies. To perform a systematic review and meta-analysis to evaluate the prognostic value of CMR parametric mapping in predicting all-cause mortality, heart failure hospitalisation and major adverse cardiovascular events in patients with CA. Methods: An extensive search was conducted in Medline, Scopus, Embase and Web of Science databases. Eligible studies were observational studies that reported HRs for predicting predefined outcomes in patients with CA using CMR parametric mapping. Results: 22 studies with 3398 patients were included in this systematic review. Higher extracellular volume (ECV) values were associated with increased mortality, both as a dichotomous (HR: 2.90; 95% CI: 1.68 to 5.01) and continuous variable (HR for 1% increase: 1.08; 95% CI: 1.06 to 1.10; HR for 3% increase: 1.17; 95% CI: 1.11 to 1.22 and HR for 10% increase: 2.11; 95% CI: 1.70 to 2.62). Higher native T1 mapping values were associated with mortality as a dichotomous variable (HR: 1.33; 95% CI: 0.79 to 2.24). Native T2 mapping showed inconsistent associations with prognosis across studies. Conclusions: Higher ECV and native T1 values are associated with worse prognosis in CA, supporting their role in risk stratification. Further studies with standardised CMR protocols are needed to enhance the prognostic utility of parametric mapping in clinical practice.

Transthyretin amyloidosis (ATTR) is a condition caused by TTR protein misfolding and amyloid deposition, particularly in the heart and nervous system, leading to organ dysfunction. Advances in therapeutic strategies have revolutionised the management of ATTR amyloidosis. Treatments available in clinical practice include TTR stabilisers (tafamidis and acoramidis), which prevent the dissociation of TTR tetramer into monomers and oligomers that subsequently form amyloid fibrils, and gene-silencing therapies (patisiran, inotersen and vutrisiran), which suppress the hepatic synthesis of TTR, which is the amyloid precursor protein. Novel treatment strategies that are at various stages of development include Clustered Regularly Interspaced Short Palindromic Repeats-Cas9 gene-editing technology (nexiguran ziclumeran), which, if successful, offers the prospect of a single-dose treatment, and monoclonal (cormitug and ALXN220) and pan-amyloid antibodies (AT-02) that seek to target and remove amyloid fibrils that have deposited in the myocardium. Amyloid removal remains a significant unmet clinical need, and hence, the ability to promote amyloid degradation and clearance through the use of antiamyloid therapies would represent a groundbreaking advancement in the treatment of ATTR amyloidosis. The success of ATTR-specific disease-modifying therapies has already altered the treatment landscape and changed the perception of ATTR amyloidosis from a progressive and fatal disease to one that is treatable through the availability of highly effective disease-modifying therapies. However, important questions remain, including the long-term safety of these drugs, whether combining therapies with different mechanisms of action has an additive prognostic benefit and how best to monitor the treatment response.