Echocardiography in Anderson-Fabry Disease.

Echocardiography is the most common diagnostic tool to screen for Fabry cardiomyopathy as it is fast, non-invasive, low-cost, widely available, easily applicable and reproducible. Echocardiography is the first-line investigation, being useful in all the stages of the disease: (1) in gene-positive patients, to unveil signs of early cardiac involvement and allowing timely treatment; (2) in patients with overt cardiomyopathy to estimate the severity of cardiac involvement, the possible related complications, and the effect of treatment. Recently, advanced echocardiographic techniques, such as speckle tracking analysis, are offering new insights in the assessment of Fabry disease patients and in the differential diagnosis of cardiomyopathies with hypertrophic phenotype. The aim of this review is to provide a comprehensive overview on the cardiac structural and functional abnormalities described in Fabry disease by means of echocardiography.

The Heart in Fabry Disease: Mechanisms Beyond Storage and Forthcoming Therapies.

In patients with Fabry disease (FD), cardiovascular involvement is the main cause of death and reduction of quality of life. Left ventricular hypertrophy mimicking hypertrophic cardiomyopathy is the main feature of FD cardiac involvement although glycolipid storage occurs in all cardiac cellular types. Accumulation of lysosomal globotriasylceramide represents the main mechanism of cardiac damage in early stages, but secondary pathways of cellular and tissue damage, triggered by lysosomal storage, and including altered energy production, inflammation and cell death, contribute to cardiac damage and disease progression. These mechanisms appear prominent in more advanced stages, hampering and reducing the efficacy of FD-specific treatments. Therefore, additional cardiovascular therapies are important to manage cardiovascular symptoms and reduce cardiovascular events. Although new therapies targeting lysosomal storage are in development, a better definition and comprehension of the complex pathophysiology of cardiac damage in FD, may lead to identify new therapeutic targets beyond storage and new therapeutic strategies.

Valvular heart disease and cardiomyopathy: reappraisal of their interplay.

Cardiomyopathies and valvular heart diseases are typically considered distinct diagnostic categories with dedicated guidelines for their management. However, the interplay between these conditions is increasingly being recognized and they frequently coexist, as in the paradigmatic examples of dilated cardiomyopathy and hypertrophic cardiomyopathy, which are often complicated by the occurrence of mitral regurgitation. Moreover, cardiomyopathies and valvular heart diseases can have a shared aetiology because several genetic or acquired diseases can affect both the cardiac valves and the myocardium. In addition, the association between cardiomyopathies and valvular heart diseases has important prognostic and therapeutic implications. Therefore, a better understanding of their shared pathophysiological mechanisms, as well as of the prevalence and predisposing factors to their association, might lead to a different approach in the risk stratification and management of these diseases. In this Review, we discuss the different scenarios in which valvular heart diseases and cardiomyopathies coexist, highlighting the need for an improved classification and clustering of these diseases with potential repercussions in the clinical management and, particularly, personalized therapeutic approaches.

Early cardiac mechanics abnormalities in patients with mitochondrial diseases.

BACKGROUND: Evidence about early cardiac mechanics abnormalities in patients with mitochondrial diseases (MDs) before overt cardiomyopathy is limited. METHODS: In this prospective study, we performed a comparative analysis of conventional and speckle tracking echocardiographic parameters between patients with genetically identified MDs and no overt cardiomyopathy vs controls matched for age, sex and cardiovascular risk factors. The Newcastle mitochondrial disease adult scale (NMDAS) was calculated, using a threshold of > 21 as indicator of high disease severity. RESULTS: We enrolled 24 MDs patients (50 % males, mean age 47.2 ± 14.3 years), the most prevalent mutation was the MT-TL1 m.3243A>G (37.5 %). In MDs patients all dimensional echocardiographic parameters were similar to controls. Conversely, albeit normal, Tissue Doppler septal systolic (p = 0.002) and early diastolic velocities (p = 0.016) were significantly lower and E/e' ratio was higher (p = 0.032) in MDs. Moreover, LV-GLS was significantly reduced in MDs as compared to their counterparties (20.2 ± 1.6 vs 22.6 ± 1.5, p < 0.001). Similarly, LA reservoir and conduit strain were significantly lower in MDs (31.7 ± 7.0 vs 35.9 ± 6.6, p = 0.038; 19.7 ± 5.6 vs 23.1 ± 6.0, p = 0.049 respectively), while LA contractile strain was similar between the two groups. Lower values of LV-GLS were observed in patients with NMDAS > 21 vs patients with NMDAS ≤ 21 (19.0 ± 1.2 vs 21.0 ± 1.3, p = 0.001). CONCLUSIONS: In patients with MDs and no overt cardiomyopathy Tissue Doppler and speckle tracking analysis unveil worse LV systolic and diastolic function indices as compared to controls. Reduced LV-GLS values were found especially in those with worse disease burden.

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection.

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Left atrial structural and functional remodelling in Fabry disease and cardiac amyloidosis: A comparative analysis.

BACKGROUND: Fabry disease (FD) and transthyretin cardiac amyloidosis (TTR CA) are cardiomyopathies with hypertrophic phenotype that share several features, including left atrial (LA) enlargement and dysfunction, but direct comparative data are lacking. Aim of the present study was to perform a comparative analysis of LA remodelling between the two diseases. METHODS AND RESULTS: In this prospective study, a total of 114 patients (31 FD and 83 TTR CA) were included; all of them had left ventricular hypertrophy (LVH), defined as left ventricular (LV) wall thickness ≥ 12 mm. Despite similar degree of LVH, patients with TTR CA showed worse LV systolic and diastolic function. LA maximal volume index was not significantly different between the two groups (p = 0.084), while patients with TTR CA showed larger LA minimal volume index (p = 0.001). Moreover, all phases of LA mechanics were more impaired in the TTR CA group vs FD (reservoir: 6.9[4.2-15.5] vs 19.0[15.5-29.5], p < 0.001). After excluding patients with atrial fibrillation (AF), these differences remained clearly significant. In multivariable regression analyses, LA reservoir strain showed an independent correlation with TTR CA, controlling for demographic characteristics, AF and LV systolic and diastolic performance (p ≤ 0.001), whereas LV global longitudinal strain did not. Finally, among echocardiographic parameters, LA function demonstrated the highest accuracy in discriminating the two diseases. CONCLUSIONS: TTR CA is characterized by a more advanced LA structural and functional remodelling in comparison to patients with FD and similar degree of LVH. The association between TTR CA and LA dysfunction remains consistent after adjustment for potential confounders.

Pulmonary congestion assessed by lung ultrasound in patients with severe aortic stenosis undergoing transcatheter aortic valve implantation: Prevalence and prognostic implications.

AIMS: Lung ultrasound (LUS) is a sensitive tool to assess pulmonary congestion (PC). Few data are available on LUS-PC evaluation in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation (TAVI). The aim of this study was to assess the prevalence and prognostic impact of LUS-PC in patients with severe AS before and after TAVI. METHODS AND RESULTS: We designed a single-centre prospective study in patients referred for TAVI for severe AS (ClinicalTrials.gov identification number: NCT05024942). All patients underwent echocardiography and LUS (according to a simplified 8-zone scanning protocol) the day before and within 72 h after the procedure. The primary endpoint was the composite of all-cause mortality, hospitalization for heart failure and urgent medical visits for worsening dyspnoea at 12-month follow-up. A total of 127 patients were enrolled (mean age 81.1 ± 5.8 years; 54.3% female). Pre-TAVI LUS-PC was documented in 65 patients (51%). After TAVI, the prevalence of LUS-PC significantly decreased as compared to pre-TAVI evaluation, being documented in only 28 patients (22% vs. 51%, p < 0.001) with a median B-lines score of 4 (interquartile range [IQR] 0-11) versus 11 (IQR 6-19) pre-TAVI (p < 0.001). During a median follow-up of 12 (12-17) months, 25 patients (19.6%) met the composite endpoint. On multivariable Cox regression analysis, pre-TAVI LUS-PC was independently associated with cardiovascular events (hazard ratio 2.764, 95% confidence interval 1.114-6.857; p = 0.028). CONCLUSIONS: Lung ultrasonography reveals a high prevalence of PC in patients with severe AS undergoing TAVI, which is significantly reduced by the procedure. Pre-TAVI PC, measured by LUS, is an independent predictor of 1-year clinical outcome.

Emerging multisystem biomarkers in hereditary transthyretin amyloidosis: a pilot study.

Hereditary transthyretin (ATTRv) amyloidosis is a rare, adult-onset, progressive, multisystemic condition caused by TTR pathogenic variants. Reliable biomarkers are needed to allow early diagnosis and to monitor disease severity and progression. We measured serum concentrations of growth differentiation factor-15 (GDF-15) and uromodulin (Umod) in ATTRv patients to evaluate correlations with standard markers of disease severity (FAP stage and PND score). Blood samples were collected from 16 patients diagnosed with ATTRv amyloidosis and a verified TTR variant and from 26 healthy controls. ATTRv patients were stratified by clinical phenotype (neurologic vs. mixed), genotype (V30M vs. non-V30M), and disease severity. We found significantly higher levels of serum GDF-15 in ATTRv patients compared with controls. Mean serum Umod levels were significantly lower in patients with ATTRv than controls. A positive correlation was found between serum Umod and estimated glomerular filtration rate (eGFR), while an inverse correlation was found with cystatin C levels. Conversely, GDF-15 showed a negative correlation with eGFR, and a direct correlation with cystatin C levels. No correlation was demonstrated between GDF-15 or Umod levels and traditional cardiac biomarkers. The results identify alteration of serum levels of GDF-15 and Umod in ATTRv amyloidosis.

Variabilidad en los errores preanalíticos del laboratorio entre centros periféricos de extracción: un reto para la seguridad del paciente / Variability of preanalytical errors between decentralized phlebotomy centers: A challenge for patient safety

Objetivo. Mostrar el número y tipo de incidencias preanalíticas en los centros de extracción periférica (CEP) del Departamento de Salud 17 de la Agencia Valenciana de Salud Método. El estudio se ha llevado a cabo durante 35 meses (mayo 2005–marzo 2008) sobre las 362.054 solicitudes y las 2.880.742 pruebas que se han recibido de los 16 CEP de Atención Primaria del Departamento de Salud 17. Método. Las incidencias son registradas en el sistema de información de laboratorio mediante un resultado codificado específico en la prueba solicitada. La procedencia de la muestra afectada se conoce mediante el número de petición que es específico para cada CEP. Método. Los resultados codificados y las muestras afectadas son recogidos automáticamente mediante un software basado en cubos On-Line Analytical Processing (OLAP) (Omnium®, Roche Diagnostics®). Calculamos las incidencias (expresadas en defectos por millón de oportunidades) para cada tipo de muestra en cada uno de los CEP. Se clasificaron los errores preanalíticos en dos grandes grupos: errores debidos a la pericia extractora (muestra coagulada, insuficiente o hemolizada) y error debido a fallo de proceso (muestra no disponible). El tratamiento de los datos obtenidos se ha realizado mediante Microsoft Excel 2003. Las variables son expresadas como frecuencia y porcentaje. Resultados. El mayor número de incidencias tuvo lugar en las muestras de orina (5.358 [52%]), seguidas por las de coagulación (2.164 [21%]), hematología (1.752 [17%]) y bioquímica (1.030 [10%]). Con respecto al tipo de error, la mayor proporción de errores fue debida a fallos de proceso (7.007 [62%]). Conclusiones. La alta incidencia de errores preanalíticos y su variabilidad entre centros sugieren que existe una necesidad de homogeneizar la práctica de la extracción de muestras(AU)

Objective. The aim of the study is to show the most frequent preanalytical sample errors from distinct decentralized phlebotomy centers. Method. The study was conducted from May 2005 to March 2008. In this period 36,2054 requests and 2,880,742 tests were received from the 16 decentralized phlebotomy centers. When an unsuitable sample is received specific coded results are registered as test results to inform the physician that an error had occurred and a new specimen collection is recommended. We used the the request number, which is specific for each phlebotomy center to ascertain where the samples with errors had been drawn, The preanalytical errors were identified by looking for coded results and were collected automatically from the LIS using a software program based on OLAP's cube (Omnium® Roche Diagnostic®), obtaining number and type of preanalytical error for each sample. The errors are calculated as number per million samples requested. Analysis of data was carried out using Microsoft Excel 2003. Categorical variables were expressed as frequency and percentage.ResultsThe highest number of incidences occurred in urine samples (52%), followed by coagulation (21%), haematology (17%) and biochemistry (10%). With regard to the type of error, the largest proportion of errors was due to failures of process (62%). Conclusions. The high incidence of preanalytical errors and variability between centers suggests that there is a need to standardize the drawing practice(AU)