Explore the vast range of titles available.

Background Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. Methods 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. Results The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). Conclusions A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.

Abstract Aims The study aims to systematically assess the diagnostic performance of cardiac magnetic resonance (CMR) and nuclear scintigraphy (index tests) for the diagnosis and differentiation of subtypes of cardiac amyloidosis. Methods and results MEDLINE and Embase electronic databases were searched for studies evaluating the diagnostic performance of CMR or nuclear scintigraphy in detecting cardiac amyloidosis and subsequently in differentiating transthyretin amyloidosis (ATTR) from immunoglobulin light-chain (AL) amyloidosis. In this meta-analysis, histopathological examination of tissue from endomyocardial biopsy (EMB) or extra-cardiac organs were reference standards. Pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated, and a random effects meta-analysis was used to estimate diagnostic odds ratios. Methodological quality was assessed using a validated instrument. Of the 2947 studies identified, 27 met the criteria for inclusion. Sensitivity and specificity of CMR in diagnosing cardiac amyloidosis was 85.7% and 92.0% against EMB reference and 78.9% and 93.9% with any organ histology reference. Corresponding sensitivity and specificity of nuclear scintigraphy was 88.4% and 87.2% against EMB reference and 82.0% and 98.8% with histology from any organ. CMR was unable to reliably differentiate ATTR from AL amyloidosis (sensitivity 28.1–99.0% and specificity 11.0–60.0%). Sensitivity and specificity of nuclear scintigraphy in the differentiation of ATTR from AL amyloidosis ranged from 90.9% to 91.5% and from 88.6% to 97.1%. Pooled negative likelihood ratio and positive likelihood ratio for scintigraphy in this setting were 0.1 and 8, with EMB reference standard. Study quality assessed by QUADAS-2 was generally poor with evidence of bias. Conclusions Cardiac magnetic resonance is a useful test for diagnosing cardiac amyloidosis but is not reliable in further classifying the disease. Nuclear scintigraphy offers strong diagnostic performance in both the detection of cardiac amyloidosis and differentiating ATTR from AL amyloidosis. Our findings support the use of both imaging modalities in a non-invasive diagnostic algorithm that also tests for the presence of monoclonal protein.

Background We aim to assess the diagnosis performance of an ensemble machine learning (ML) based radiomic analysis of multiparametric cardiac magnetic resonance (CMR) to differentiate light chain cardiac amyloidosis (AL-CA) and hypertrophic cardiomyopathy (HCM). Methods In the development dataset, we retrospectively collected at Peking Union Medical College Hospital between January 1, 2017, and December 31, 2022, and included 84 patients with AL-CA, 63 patients with HCM, and 34 healthy controls. Radiomics features were extracted from regions of interest in the myocardium on native T1, post-contrast T1, extracellular volume (ECV), and T2 mapping. For each modal data, eight feature selection methods were used to select the top 10 important features; then, seven ML classifiers were trained with the selected features for disease classification, and the best combinations of feature selection and classifiers were chosen by the highest predictive accuracy (ACC). The predictive results of multiple ML classifiers as input to build an ensemble ML model that classified each case (AL-CA, HCM, or controls) using a“soft voting” scheme. Results For native T1, post-contrast T1, T2 mapping, ECV, and clinical data, the best combination of feature selection and classifier is MRMR_RF, XGboost_RF, Lasso_ Lasso, Lasso_RF, and ANOVA_ XGboost, respectively. The myocardial texture and the first-order features of native T1, post-contrast T1, and T2 mapping dominated the ensemble ML model and there was only a marginal role for ventricular shape features. In the hold-out testing dataset (37 AL-CA, 21 HCM, and 14 controls), the ensemble ML model exhibited a better diagnostic value with an area under curve of 0.98 for differentiating the 3 groups. Conclusion An ensemble ML model with competitive diagnostic accuracy was proposed to differentiate AL-CA from HCM patients and healthy controls.

Purpose of ReviewCardiac amyloidosis is an underrecognized cause of heart failure. We review clinical clues to the diagnoses, a rational approach to testing, and current and emerging therapies.Recent FindingsAdvances in the diagnosis of amyloid cardiomyopathy include (1) use of 99mtechnetium (99mTc) bone-avid compounds which allow accurate noninvasive diagnosis of transthyretin cardiac amyloidosis (ATTR-CM) in the context of a negative monoclonal light chain screen; and (2) the use of serum and urine immunofixation electrophoresis with serum free light chains as an accurate first diagnostic step for light chain cardiac amyloidosis (AL-CM). Advances in treatment include tafamidis for ATTR-CM and immunologic therapies for AL-CM.SummaryWith the advent of accurate noninvasive diagnostic modalities and effective therapies, early recognition of cardiac amyloidosis is paramount to implement a diagnostic algorithm and expeditiously institute effective therapies to minimize morbidity and mortality.

Cardiac magnetic resonance (CMR) is a diagnostic tool that provides precise and reproducible information about cardiac structure, function, and tissue characterization, aiding in the monitoring of chemotherapy response in patients with light-chain cardiac amyloidosis (AL-CA). This study aimed to evaluate the feasibility of CMR in monitoring responses to chemotherapy in patients with AL-CA. In this prospective study, we enrolled 111 patients with AL-CA (50.5% male; median age, 54 [interquartile range, 49-63] years). Patients underwent longitudinal monitoring using biomarkers and CMR imaging. At follow-up after chemotherapy, patients were categorized into superior and inferior response groups based on their hematological and cardiac laboratory responses to chemotherapy. Changes in CMR findings across therapies and differences between response groups were analyzed. Following chemotherapy (before vs. after), there were significant increases in myocardial T2 (43.6 ± 3.5 ms vs. 44.6 ± 4.1 ms; = 0.008), recovery in right ventricular (RV) longitudinal strain (median of -9.6% vs. -11.7%; = 0.031), and decrease in RV extracellular volume fraction (ECV) (median of 53.9% vs. 51.6%; = 0.048). These changes were more pronounced in the superior-response group. Patients with superior cardiac laboratory response showed significantly greater reductions in RV ECV (-2.9% [interquartile range, -8.7%-1.1%] vs. 1.7% [-5.5%-7.1%]; = 0.017) and left ventricular ECV (-2.0% [-6.0%-1.3%] vs. 2.0% [-3.0%-5.0%]; = 0.01) compared with those with inferior response. Cardiac amyloid deposition can regress following chemotherapy in patients with AL-CA, particularly showing more prominent regression, possibly earlier, in the RV. CMR emerges as an effective tool for monitoring associated tissue characteristics and ventricular functional recovery in patients with AL-CA undergoing chemotherapy, thereby supporting its utility in treatment response assessment.

A new classification for biomarkers in Alzheimer's disease and cognitive ageing research is based on grouping the markers into three categories: amyloid deposition (A), tauopathy (T), and neurodegeneration or neuronal injury (N). Dichotomising these biomarkers as normal or abnormal results in eight possible profiles. We determined the clinical characteristics and prevalence of each ATN profile in cognitively unimpaired individuals aged 50 years and older. All participants were in the Mayo Clinic Study of Aging, a population-based study that uses a medical records linkage system to enumerate all individuals aged 50–89 years in Olmsted County, MN, USA. Potential participants are randomly selected, stratified by age and sex, and invited to participate in cognitive assessments; individuals without medical contraindications are invited to participate in brain imaging studies. Participants who were judged clinically as having no cognitive impairment and underwent multimodality imaging between Oct 11, 2006, and Oct 5, 2016, were included in the current study. Participants were classified as having normal (A−) or abnormal (A+) amyloid using amyloid PET, normal (T−) or abnormal (T+) tau using tau PET, and normal (N−) or abnormal (N+) neurodegeneration or neuronal injury using cortical thickness assessed by MRI. We used the cutoff points of standard uptake value ratio (SUVR) 1·42 (centiloid 19) for amyloid PET, 1·23 SUVR for tau PET, and 2·67 mm for MRI cortical thickness. Age-specific and sex-specific prevalences of the eight groups were determined using multinomial models combining data from 435 individuals with amyloid PET, tau PET, and MRI assessments, and 1113 individuals who underwent amyloid PET and MRI, but not tau PET imaging. The numbers of participants in each profile group were 165 A−T−N−, 35 A−T+N−, 63 A−T−N+, 19 A−T+N+, 44 A+T−N−, 25 A+T+N−, 35 A+T−N+, and 49 A+T+N+. Age differed by ATN group (p<0·0001), ranging from a median 58 years (IQR 55–64) in A–T–N– and 57 years (54–64) in A–T+N– to a median 80 years (75–84) in A+T–N+ and 79 years (73–87) in A+T+N+. The number of APOE ε4 carriers differed by ATN group (p=0·04), with carriers roughly twice as frequent in each A+ group versus the corresponding A– group. White matter hyperintensity volume (p<0·0001) and cognitive performance (p<0·0001) also differed by ATN group. Tau PET and neurodegeneration biomarkers were discordant in most individuals who would be categorised as stage 2 or 3 preclinical Alzheimer's disease (A+T+N−, A+T−N+, and A+T+N+; 86% at age 65 years and 51% at age 80 years) or with suspected non-Alzheimer's pathophysiology (A−T+N−, A−T−N+, and A−T+N+; 92% at age 65 years and 78% at age 80 years). From age 50 years, A−T−N− prevalence declined and A+T+N+ and A−T+N+ prevalence increased. In both men and women, A−T−N− was the most prevalent until age late 70s. After about age 80 years, A+T+N+ was most prevalent. By age 85 years, more than 90% of men and women had one or more biomarker abnormalities. Biomarkers of fibrillar tau deposition can be included with those of β-amyloidosis and neurodegeneration or neuronal injury to more fully characterise the heterogeneous pathological profiles in the population. Both amyloid- dependent and amyloid-independent pathological profiles can be identified in the cognitively unimpaired population. The prevalence of each ATN group changed substantially with age, with progression towards more biomarker abnormalities among individuals who remained cognitively unimpaired. National Institute on Aging (part of the US National Institutes of Health), the Alexander Family Professorship of Alzheimer's Disease Research, the Mayo Clinic, and the GHR Foundation.

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85–0.91 for ECG and 0.89–1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3–4% at 52–71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74–77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases. Cardiac amyloidosis is difficult to identify, given low prevalence and similarity of the symptoms to more prevalent disorders. Here the authors present a multi-modality, artificial intelligence-enabled pipeline, that enables automated detection of cardiac amyloidosis from inexpensive and accessible measures.

Alzheimer's disease and its complications are the leading cause of death in adults with Down syndrome. Studies have assessed Alzheimer's disease in individuals with Down syndrome, but the natural history of biomarker changes in Down syndrome has not been established. We characterised the order and timing of changes in biomarkers of Alzheimer's disease in a population of adults with Down syndrome. We did a dual-centre cross-sectional study of adults with Down syndrome recruited through a population-based health plan in Barcelona (Spain) and through services for people with intellectual disabilities in Cambridge (UK). Cognitive impairment in participants with Down syndrome was classified with the Cambridge Cognitive Examination for Older Adults with Down Syndrome (CAMCOG-DS). Only participants with mild or moderate disability were included who had at least one of the following Alzheimer's disease measures: apolipoprotein E allele carrier status; plasma concentrations of amyloid β peptides 1–42 and 1–40 and their ratio (Aβ1–42/1–40), total tau protein, and neurofilament light chain (NFL); tau phosphorylated at threonine 181 (p-tau), and NFL in cerebrospinal fluid (CSF); and one or more of PET with 18F-fluorodeoxyglucose, PET with amyloid tracers, and MRI. Cognitively healthy euploid controls aged up to 75 years who had no biomarker abnormalities were recruited from the Sant Pau Initiative on Neurodegeneration. We used a first-order locally estimated scatterplot smoothing curve to determine the order and age at onset of the biomarker changes, and the lowest ages at the divergence with 95% CIs are also reported where appropriate. Between Feb 1, 2013, and June 28, 2019 (Barcelona), and between June 1, 2009, and Dec 31, 2014 (Cambridge), we included 388 participants with Down syndrome (257 [66%] asymptomatic, 48 [12%] with prodromal Alzheimer's disease, and 83 [21%] with Alzheimer's disease dementia) and 242 euploid controls. CSF Aβ1–42/1–40 and plasma NFL values changed in individuals with Down syndrome as early as the third decade of life, and amyloid PET uptake changed in the fourth decade. 18F-fluorodeoxyglucose PET and CSF p-tau changes occurred later in the fourth decade of life, followed by hippocampal atrophy and changes in cognition in the fifth decade of life. Prodromal Alzheimer's disease was diagnosed at a median age of 50·2 years (IQR 47·5–54·1), and Alzheimer's disease dementia at 53·7 years (49·5–57·2). Symptomatic Alzheimer's disease prevalence increased with age in individuals with Down syndrome, reaching 90–100% in the seventh decade of life. Alzheimer's disease in individuals with Down syndrome has a long preclinical phase in which biomarkers follow a predictable order of changes over more than two decades. The similarities with sporadic and autosomal dominant Alzheimer's disease and the prevalence of Down syndrome make this population a suitable target for Alzheimer's disease preventive treatments. Instituto de Salud Carlos III, Fundació Bancaria La Caixa, Fundació La Marató de TV3, Medical Research Council, and National Institutes of Health.

BackgroundThe diagnosis of cardiac amyloidosis (CA) is challenging owing to vague symptomatology and non-specific echocardiographic findings.ObjectiveTo describe regional patterns in longitudinal strain (LS) using two-dimensional speckle-tracking echocardiography in CA and to test the hypothesis that regional differences would help differentiate CA from other causes of increased left ventricular (LV) wall thickness.Methods and results55 consecutive patients with CA were compared with 30 control patients with LV hypertrophy (n=15 with hypertrophic cardiomyopathy, n=15 with aortic stenosis). A relative apical LS of 1.0, defined using the equation (average apical LS/(average basal LS + mid-LS)), was sensitive (93%) and specific (82%) in differentiating CA from controls (area under the curve 0.94). In a logistic regression multivariate analysis, relative apical LS was the only parameter predictive of CA (p=0.004).ConclusionsCA is characterised by regional variations in LS from base to apex. A relative ‘apical sparing’ pattern of LS is an easily recognisable, accurate and reproducible method of differentiating CA from other causes of LV hypertrophy.

Non-invasive cardiac imaging allows detection of cardiac amyloidosis (CA) in patients with aortic stenosis (AS). Our objective was to estimate the prevalence of clinically suspected CA in patients with moderate and severe AS referred for cardiovascular magnetic resonance (CMR) in age and gender categories, and assess associations between AS-CA and all-cause mortality. We retrospectively identified consecutive AS patients defined by echocardiography referred for further CMR assessment of valvular, myocardial, and aortic disease. CMR identified CA based on typical late-gadolinium enhancement (LGE) patterns, and ancillary clinical evaluation identified suspected CA. Survival analysis with the Log rank test and Cox regression compared associations between CA and mortality. There were 113 patients (median age 74 years, Q1-Q3: 62–82 years), 96 (85%) with severe AS. Suspected CA was present in 9 patients (8%) all > 80 years. Among those over the median age of 74 years, the prevalence of CA was 9/57 (16%), and excluding women, the prevalence was 8/25 (32%). Low-flow, low-gradient physiology was very common in CA (7/9 patients or 78%). Over a median follow-up of 18 months, 40 deaths (35%) occurred. Mortality in AS + CA patients was higher than AS alone (56% vs. 20% at 1-year, log rank 15.0, P < 0.0001). Adjusting for aortic valve replacement modeled as a time-dependent covariate, Society of Thoracic Surgery predicted risk of mortality, left ventricular ejection fraction, CA remained associated with all-cause mortality (HR = 2.92, 95% CI = 1.09–7.86, P = 0.03). Suspected CA appears prevalent among older male patients with AS, especially with low flow, low gradient AS, and associates with all-cause mortality. The importance of screening for CA in older AS patients and optimal treatment strategies in those with CA warrant further investigation, especially in the era of transcatheter aortic valve implantation.