Utilidad del Holter insertable con detección remota en el diagnóstico etiológico del ictus criptogénico: análisis de 73 pacientes / Use of the insertable Holter with remote detection in the etiological diagnosis of cryptogenic stroke: Analysis of 73 patients

Introducción El ictus criptogénico constituye el 25% del total de ictus isquémicos, de los cuales un 20-30% son debidos a fibrilación auricular (FA). Con el objetivo de aumentar la tasa de detección, surgen los dispositivos de monitorización prolongada implantables. El estudio del perfil del candidato ideal subsidiario de dicha monitorización otorgaría un mejor entendimiento de los mecanismos que subyacen en este subtipo de ictus. Objetivo Determinar qué variables están relacionadas y pueden predecir la detección de FA silente en pacientes con ictus criptogénico. Pacientes y métodos Se trata de una cohorte longitudinal con selección desde marzo de 2017 hasta mayo de 2022. Son pacientes portadores de dispositivo de monitorización implantable e ictus criptogénico con monitorización mínima de un año. Resultados El total de pacientes incluidos fue de 73, con edad media de 58,8 años, siendo el 56,2% varones. Se detectó FA en 21 pacientes (28,8%). Los factores de riesgo cardiovascular más frecuentes fueron hipertensión (47,9%) y dislipidemia (45,2%). La topografía más frecuente fue cortical (52%). En cuanto a los parámetros ecocardiográficos, un 22% tenían la aurícula izquierda dilatada, un 19% tenían foramen oval permeable y un 22% presentaron taquicardia supraventricular de alta densidad (>1%) en el Holter. En el análisis multivariante, la única variable predictora de FA es la presencia taquicardia supraventricular de alta densidad, con un área bajo la curva de 0,726 (IC 0,57-0,87, p=0,04), una sensibilidad del 47,6%, una especificidad del 97,5%, un valor predictivo positivo del 90,9%, un valor predictivo negativo del 78,8% y una exactitud del 80,9%. Conclusiones La presencia de taquicardia supraventricular de alta densidad puede ser orientativa para predecir FA silente. No se han observado otras variables que nos permitan predecir la detección de FA en estos pacientes (AU)

Introduction Cryptogenic stroke constitutes 25% of all ischemic strokes, of which 20–30% are due to atrial fibrillation (AF). With the aim of increasing the detection rate, implantable long-term monitoring devices have emerged. The study of the profile of the ideal candidate subsidiary to such monitoring would provide a better understanding of the mechanisms underlying this subtype of stroke. Objective To determine which variables are related and can predict the detection of silent AF in patients with cryptogenic stroke. Patients and methods This is a longitudinal cohort with recruitment from March 2017 to May 2022. They are patients with an implantable monitoring device and cryptogenic stroke with a minimum monitoring of one year. Result The total number of patients included was 73, with a mean age of 58.8 years, 56.2% were male. AF was detected in 21 patients (28.8%). The most frequent cardiovascular risk factors were hypertension (47.9%) and dyslipidemia (45.2%). The most frequent topography was cortical (52%). Regarding the echocardiographic parameters, 22% had a dilated left atrium, 19% had a patent foramen ovale, and 22% had high-density supraventricular tachycardia (>1%) on Holter monitoring. In the multivariate analysis, the only variable that predicts AF is the presence of high-density supraventricular tachycardia, with an area under the curve of 0.726 (CI 0.57–0.87, p=0.04), sensitivity of 47.6%, specificity of 97.5%, positive predictive value of 90.9%, negative predictive value of 78.8%, and accuracy of 80.9%. Conclusions The presence of high-density supraventricular tachycardia can be indicative for predicting silent AF. No other variables have been observed that allow us to predict detection of AF in these patients (AU)

[Use of the insertable Holter with remote detection in the etiological diagnosis of cryptogenic stroke: Analysis of 73 patients]. / Utilidad del Holter insertable con detección remota en el diagnóstico etiológico del ictus criptogénico: análisis de 73 pacientes.

INTRODUCTION: Cryptogenic stroke constitutes 25% of all ischemic strokes, of which 20-30% are due to atrial fibrillation (AF). With the aim of increasing the detection rate, implantable long-term monitoring devices have emerged. The study of the profile of the ideal candidate subsidiary to such monitoring would provide a better understanding of the mechanisms underlying this subtype of stroke. OBJECTIVE: To determine which variables are related and can predict the detection of silent AF in patients with cryptogenic stroke. PATIENTS AND METHODS: This is a longitudinal cohort with recruitment from March 2017 to May 2022. They are patients with an implantable monitoring device and cryptogenic stroke with a minimum monitoring of one year. RESULTS: The total number of patients included was 73, with a mean age of 58.8 years, 56.2% were male. AF was detected in 21 patients (28.8%). The most frequent cardiovascular risk factors were hypertension (47.9%) and dyslipidemia (45.2%). The most frequent topography was cortical (52%). Regarding the echocardiographic parameters, 22% had a dilated left atrium, 19% had a patent foramen ovale, and 22% had high-density supraventricular tachycardia (>1%) on Holter monitoring. In the multivariate analysis, the only variable that predicts AF is the presence of high-density supraventricular tachycardia, with an area under the curve of 0.726 (CI 0.57-0.87, p=0.04), sensitivity of 47.6%, specificity of 97.5%, positive predictive value of 90.9%, negative predictive value of 78.8%, and accuracy of 80.9%. CONCLUSIONS: The presence of high-density supraventricular tachycardia can be indicative for predicting silent AF. No other variables have been observed that allow us to predict detection of AF in these patients.

Personalized assessment of the cumulative complication risk of the atrial fibrillation ablation track: The AF-TRACK calculator.

Background: Atrial fibrillation (AF) ablation strategy is associated with a non-negligible risk of complications and often requires repeat procedures (AF ablation track), implying repetitive exposure to procedural risk. Objective: The purpose of this study was to develop and validate a model to estimate individualized cumulative risk of complications in patients undergoing the AF ablation track (Atrial Fibrillation TRAck Complication risK [AF-TRACK] calculator). Methods: The model was derived from a multicenter cohort including 3762 AF ablation procedures in 2943 patients. A first regression model was fitted to predict the propensity for repeat ablation. The AF-TRACK calculator computed the risk of AF ablation track complications, considering the propensity for repeat ablation. Internal (cross-validation) and external (independent cohort) validation were assessed for discrimination capacity (area under the curve [AUC]) and goodness of fit (Hosmer-Lemeshow [HL] test). Results: Complications (N = 111) occurred in 3.7% of patients (2.9% of procedures). Predictors included female sex, heart failure, sleep apnea syndrome, and repeat procedures. The model showed fair discrimination capacity to predict complications (AUC 0.61 [0.55-0.67]) and likelihood of repeat procedure (AUC 0.62 [0.60-0.64]), with good calibration (HL χ2 12.5; P = .13). The model maintained adequate discrimination capacity (AUC 0.67 [0.57-0.77]) and calibration (HL χ2 5.6; P = .23) in the external validation cohort. The validated model was used to create the Web-based AF-TRACK calculator. Conclusion: The proposed risk model provides individualized estimates of the cumulative risk of complications of undergoing the AF ablation track. The AF-TRACK calculator is a validated, easy-to-use, Web-based clinical tool to calibrate the risk-to-benefit ratio of this treatment strategy.

Remote heart failure management using the HeartLogic algorithm. RE-HEART registry.

INTRODUCTION AND OBJECTIVES: HeartLogic is a multiparametric algorithm incorporated into implantable cardioverter-defibrillators (ICD). The associated alerts predict impending heart failure (HF) decompensations. Our objective was to analyze the association between alerts and clinical events and to describe the implementation of a protocol for remote management in a multicenter registry. METHODS: We evaluated study phase 1 (the investigators were blinded to the alert state) and phases 2 and 3 (after HeartLogic activation, managed as per local practice and with a standardized protocol, respectively). RESULTS: We included 288 patients from 15 centers. In phase 1, the median observation period was 10 months and there were 73 alerts (0.72 alerts/patient-y), with 8 hospitalizations and 2 emergency room admissions for HF (0.10 events/patient-y). There were no HF hospitalizations outside the alert period. In the active phases, the median follow-up was 16 (95%CI, 15-22) months and there were 277 alerts (0.89 alerts/patient-y); 33 were associated with HF hospitalizations or HF death (n=6), 46 with minor decompensations, and 78 with other events. The unexplained alert rate was 0.39 alerts/patient-y. Outside the alert state, there was only 1 HF hospitalization and 1 minor HF decompensation. Most alerts (82% in phase 2 and 81% in phase 3; P=.861) were remotely managed. The median NT-proBNP value was higher within than outside the alert state (7378 vs 1210 pg/mL; P <.001). CONCLUSIONS: The HeartLogic index was frequently associated with HF-related events and other clinically relevant situations, with a low rate of unexplained events. A standardized protocol allowed alerts to be safely and remotely detected and appropriate action to be taken on them.

Nuevos conceptos sobre los mecanismos de la fibrilación ventricular / New Concepts on the Mechanisms of Ventricular Fibrillation

La fibrilación ventricular es la principal arritmia causante de muerte súbita cardíaca; sin embargo, su mecanismo detallado es aún difícil de explicar. La fibrilación ventricular ha sido definida como una actividad eléctrica cardíaca 'turbulenta', término que nos transmite la idea de una activación desorganizada y totalmente aleatoria de los ventrículos. En este artículo se discuten los resultados de experimentos publicados recientemente en los que se utilizó un nuevo algoritmo, la 'cartografía bidimensional de fases', que asigna un estado de fase a cada segmento del potencial de acción transmembrana y demuestra que la propagación de ondas durante la fibrilación ventricular no es aleatoria y puede, además, ser analizada cuantitativamente. Esta técnica se basa en la grabación en vídeo de imágenes de la fluorescencia de un colorante sensible a los cambios de voltaje, que permite observar simultáneamente, en corazones de conejos y ovejas, los cambios del potencial transmembrana de miles de puntos en la superficie epicárdica ventricular. En el análisis del electrocardiograma durante la fibrilación ventricular se observa un componente predominante de frecuencia cercano a los 500 lat/min. El mapa de fases revela que la fibrilación ventricular depende de la rotación de líneas convergentes alrededor de 'puntos singulares', en los que el estado de fase es indefinido, dando la apariencia de torbellinos eléctricos que rotan a frecuencias que pueden ser más altas que las que se observan en el electrocardiograma. Dichos puntos singulares, formados como consecuencia de la interacción de frentes de onda con obstáculos que encuentran en su camino, son escasos en número y de vida relativamente corta. La observación más importante es la existencia de un alto grado de organización, tanto espacial como temporal, en la fibrilación cardíaca. Estos resultados preparan el terreno para un mejor entendimiento de los mecanismos de la fibrilación ventricular, tanto en corazones sanos como enfermos (AU)