Detección automática de residuos de la actividad ventricular presentes en las ondas fibrilatorias extraídas de registros electrocardiográficos de fibrilación auricular

El desarrollo de técnicas de extracción de las ondas fibrilatorias (ondas-f) del electrocardiograma (ECG) de superficie ha posibilitado su análisis para detectar y mejorar el tratamiento de la fibrilación auricular (FA). Sin embargo, estas técnicas tienen un rendimiento limitado por la presencia de artefactos y complejos QRST de morfología variable. Por ello, se propone una metodología para optimizar la selección de tramos de ondas-f libres de residuos de la cancelación ventricular. Primeramente, la detección de valores atípicos fuera del margen comprendido entre 2,5 veces los percentiles de 5 y 95% de la señal de ondas-f extraída del ECG preoperatorio al procedimiento de ablación por catéter (AC) de 148 pacientes de FA persistente permitió la detección de los artefactos registrados. Seguidamente, se caracterizaron los intervalos QRST cancelados para estimar la actividad ventricular remanente en las ondas-f mediante diferentes métricas. RuVR fue la más capacitada para tal fin (71,66% de exactitud), obtenida del producto del valor cuadrático medio de las ondas-f en el intervalo QRST por su valor máximo. No obstante, su combinación con otras métricas mediante un modelo de ensamblado de árboles de decisión mejoró la exactitud en la detección de residuos ventriculares hasta alcanzar casi un 82%. Finalmente, esta metodología demostró la relevancia de asegurar la calidad de los tramos de ondas-f extraídos del ECG de superficie, mejorando la capacidad discriminatoria de la frecuencia dominante de las ondas-f para predecir el resultado del procedimiento de AC.Esta investigación ha sido financiada por las subvenciones públicas PID2021-00X128525-IV0, PID2021-123804OB-I00 y TED2021-130935B-I00 del Gobierno de España, 10.13039/501100011033 junto con el Fondo Europeo de Desarrollo Regional (UE), SBPLY/17/180501/000411 y SBPLY/21/180501/000186 de la Junta de Comunidades de Castilla La Mancha, y AICO/2021/286 de la Generalitat Valenciana. Además, Pilar Escribano es beneficiaria de un contrato predoctoral 2020 PREDUCLM-15540, cofinanciado por el programa operativo del Fondo Social Europeo (FSE) 2014-2020 de Castilla-La Mancha y una ayuda de movilidad 2023-univers-11618 del Vicerrectorado de Política Científica de la UCLM

La Storia del Vaiont: la conoscenza della frana attraverso le foto di Edoardo Semenza

Il “disastro del Vaiont”, paradigma della catastrofe di origine umana, è ripercorso attraverso le eccezionali immagini scattate da Edoardo Semenza, per chi lo conosceva bene “Edo”. In esse traspaiono le sofferenze, le intuizioni e il progredire della consapevolezza dell’uomo che per primo riconobbe l’esistenza dell’antica frana. In esse si rivela il suo approccio mente et malleo, che gli consentì di elaborarne un modello e di definirne i conseguenti scenari di rischio. L’auspicio è che anche attraverso questa esposizione l’opera di Edo possa servire a sensibilizzare le coscienze sulla necessità della conoscenza della geologia per il rispetto e la protezione dell’ambiente. Questa mostra vuole perciò essere anche un invito al lettore a proseguire nelle ricerche a partire da quelle di Edoardo Semenza e di altri studiosi – alcuni dei quali hanno lavorato insieme a lui – che da allora hanno dedicato tanto del loro impegno allo studio della frana del Vaiont. Francesco M. Guadagno, Monica Ghirott

Determination of synchronization of electrical activity in the heart by Shannon entropy measure

In this paper we propose a new index of synchronization for the study of heart’s electrical activity during atrial fibrillation (AF). The index relies on the measure of the time delays between correspondent activations in two atrial electrograms and on the characterization of their dispersion by a measure of Shannon Entropy. The algorithm was validated on simulated signals mimicking different degree of synchronization. Results showed the index was able to discriminate among different levels of organization, provided that it works on series of at least 50 activations (time resolution of almost 10 sec during AF). Moreover, we applied the algorithm to real bipolar electrograms, obtained from a multipolar basket catheter in right atrium in two patients during atrial fibrillation: this showed the index able to distinguish different levels of complexity in AF

Myocardial fibrosis assessment by late gadolinium enhancement is a powerful predictor of ventricular tachyarrhythmias in patients with ventricular dysfunction of ischemic and nonischemic etiology: a meta-analysis

Objectives We performed a meta-analysis to evaluate the predictive value of late gadolinium enhancement (LGE) cardiac magnetic resonance for ventricular tachyarrhythmia in ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM) patients with ventricular dysfunction. Background The use of LGE to detect myocardial fibrosis and its related arrhythmic substrate is well established. Several recent studies have described the predictive value of LGE for ventricular tachyarrhythmias; however, their validity is limited by small sample size and low number of events. Methods MEDLINE and the Cochrane Library electronic databases were systematically searched to identify studies that applied LGE in ICM and NICM patients with ventricular dysfunction and reported arrhythmic clinical outcomes (sudden death, aborted sudden death, ventricular tachycardia, ventricular fibrillation, and appropriate implantable cardioverter-defibrillator [ICD] therapy, including antitachycardia pacing). A meta-analysis was performed to determine pooled odds ratios (ORs) for these arrhythmic events. Results Nineteen studies that evaluated 2,850 patients with 423 arrhythmic events over a mean/median follow-up of 2.8 years were identified. The composite arrhythmic endpoint was reached in 23.9% of patients with a positive LGE test (annualized event rate of 8.6%) versus 4.9% of patients with a negative LGE test (annualized event rate of 1.7%; p < 0.0001). LGE correlated with arrhythmic events in the different patient groups. In the overall population, the pooled OR was 5.62 (95% confidence interval [CI]: 4.20 to 7.51), with no significant differences between ICM and NICM patients. In a subgroup of 11 studies (1,178 patients) with mean ejection fraction (EF) ≤30%, the pooled OR for the arrhythmic events increased to 9.56 (95% CI: 5.63 to 16.23), with a negative likelihood ratio of 0.13 (95% CI: 0.06 to 0.30). Conclusions LGE is a powerful predictor of ventricular arrhythmic risk in patients with ventricular dysfunction, irrespective of ICM and NICM etiology. The prognostic power of LGE is particularly strong in patients with severely depressed EF, which suggests its potential to improve patient selection for ICD implantation

Comparison of different methods to extract RNA from cardiac tissue for miRNA profiling by qRT-PCR

Despite the growing interest in cardiac miRNA expression profiling, having high quality and yield in RNA extraction from cardiac tissue is still challenging. We compared different methods of tissue homogenization and total RNA extraction from pig cardiac tissue aimed at miRNAs expression profiling. Small biopsies of right atrial appendages were obtained from pig hearts and treated according to four different protocols: no homogenization (P1) and homogenization by manual (P2) or automatic (P3 and P4) methods, followed by Proteinase K digestion (PKD) except in P4. Total RNA was extracted using miRNeasy mini kit, assessing RNA yield and quality by Nanodrop. cDNA synthesis and qRT-PCR were performed using TaqMan MicroRNA Assay. Homogenization was crucial to obtain high yield of pure total RNA. Automatic methods displayed higher yield (0.27 μg RNA/mg tissue in P3) than manual (0.06 μg RNA/mg tissue in P2), with better performance without PKD step (0.38μg RNA/mg tissue in P4). RNA from P4 was suitable for miRNA expression profiling, as demonstrated by qRT-PCR on miRNA 21 and 29. These results suggest the efficacy of an automatic homogenization to extract RNA suitable for miRNA expression profiling