A detailed explantation assessment protocol for patients with left ventricular assist devices with myocardial recovery.

OBJECTIVES: Left ventricular assist device (LVAD) implantation for end-stage heart failure patients has been on the rise, providing a reliable long-term option. For some LVAD patients, longer term LV unloading leads to recovery; hence, the need for evaluating potential myocardial recovery and weaning eligibility has emerged. METHODS: All patients who underwent contemporary LVAD explantation at our institution between 2009 and 2020 were included in the study. Patients in New York Heart Association I, left ventricular ejection fraction >40%, a cardiac index >2.4 l/min and a peak oxygen intake >50% predicted underwent a 4-phase weaning assessment. A minimally invasive approach using a titanium plug was the surgery of choice in the most recent explants. Kaplan-Meier curves were used to estimate the survival at 1 and 5 years. RESULTS: Twenty-six patients (17 HeartMate II, 9 HeartWare) underwent LVAD explantation after a median 317 days of support [IQ (212-518)], range 131-1437. Mean age at explant was 35.8 ± 12.7 years and 85% were males. Idiopathic dilated cardiomyopathy was the underlying diagnosis in 70% of cases. Thirteen (48%) patients were on short-term mechanical circulatory support and 60% required intensive care unit admission prior to the LVAD implantation. At 1 year, Kaplan-Meier estimated survival was 88%, whereas at 6 years, it was 77%. The average left ventricular ejection fraction at 1 year post-explant was 44.25% ± 8.44. CONCLUSIONS: The use of a standardized weaning protocol (echocardiographic and invasive) and a minimally invasive LVAD explant technique minimizes periprocedural complications and leads to good long-term device-free survival rates.

Dataset regarding baseline and follow-up characteristics of out-of-hospital cardiac arrest patients focused on neurological outcomes.

This data article contains the data related to the research article entitled "Long-term neurological outcomes in out-of-hospital cardiac arrest patients treated with targeted-temperature management" (Caro-Codón et al., 2018). In this dataset, we report details regarding the flow chart of the included patients and the specific exclusion criteria. We also include information on the difference between the patients who attended the structured personal interview (and therefore were finally included in the study) and those who did not attend. Neuropsychiatric and functional data before and after cardiac arrest are also reported. Finally, we list all the "de novo" focal neurological deficits identified after cardiac arrest in the related population.

Long-term neurological outcomes in out-of-hospital cardiac arrest patients treated with targeted-temperature management.

BACKGROUND: This study aimed to assess long-term cognitive and functional outcomes in out-of-hospital cardiac arrest (OHCA) patients treated with targeted-temperature management, investigate the existence of prognostic factors that could be assessed during initial admission and evaluate the usefulness of classic neurological scales in this clinical scenario. METHODS: Patients admitted due to OHCA from August 2007 to November 2015 and surviving at least one year were included. Each patient completed a structured interview focused on the collection of clinical, social and demographic data. All available information in clinical records was reviewed and a battery of neurocognitive and psychometric tests was performed. RESULTS: Seventy-nine patients were finally included in the analysis. Forty-three patients (54.4%) scored below the usual cut-off points for the diagnosis of mild cognitive impairment, even though most of these deficits went unnoticed when patients were assessed using CPC and modified Rankin scale. Nineteen (24%) developed certain degree of impairment in their attention capacity and executive functions. A significant proportion developed new memory-related disorders (43%), depressive symptoms (17.7%), aggressive/uninhibited behavior (12.7%) and emotional lability (8.9%). A greater number of weekly hours of intellectual activity and a qualified job were independent protective factors for the development of cognitive impairment. However, being older at the time of the cardiac arrest was identified as a poor prognostic factor. CONCLUSIONS: There is a high prevalence of long-term cognitive deficits and functional limitations in OHCA survivors. Most commonly used clinical scales in clinical practice are crude and lack sensitivity to detect most of these deficits.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE).

OBJECTIVE: Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors. METHODS: Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale - GCS - ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS=15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis. RESULTS: The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy. CONCLUSIONS: A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE) / Valor pronóstico precoz de un algoritmo basado en variables espectrales de la fibrilación ventricular del ECG de pacientes con muerte súbita cardiaca: estudio observacional multicéntrico (AWAKE)

Abstract Objective: Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors. Methods: Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale GCS --- ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS = 15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis. Results: The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy. Conclusions: A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD.

Resumen Objetivo: La muerte súbita (MS) por fibrilación ventricular (FV) es una importante causa de morbilidad y mortalidad. Los métodos biológicos y de imagen actuales muestran limitaciones para predecir el pronóstico cerebral al ingreso hospitalario. AWAKE es un estudio observacional, multicéntrico, con el objetivo de validar un modelo basado en el análisis espectral del elec- trocardiograma (ECG), que predice precozmente el pronóstico cerebral y la supervivencia en pacientes resucitados y en estado de coma. Métodos: Se recogerán datos de los ECG con FV de pacientes reanimados de MS. Los pacientes pueden ser tanto supervivientes en estado de coma (Glasgow Coma Scale GCS ≤ 8) sometidos a control de temperatura tras la recuperación de circulación espontánea (RCE), como aquellos que recuperan la consciencia (GCS = 15) tras RCE; todos ellos ingresados en unidades de terapia intensiva cardiológica de 4 hospitales de referencia. Los registros de FV previos al primer choque se digitalizarán y analizarán para obtener datos espectrales que se incluirán en un modelo predictivo que estime el pronóstico neurológico favorable (PNF). El resultado del modelo se comparará con el pronóstico real. Resultados: El objetivo principal es el PNF durante la hospitalización. Los pacientes se categorizarán en 4 subgrupos de pronóstico neurológico según la estimación de riesgo obtenida en el modelo predictivo. Los objetivos secundarios son supervivencia al alta hospitalaria, y PNF y supervivencia a los 6 meses. El resultado de este modelo también se comparará con el pronóstico según variables clínicas. Conclusiones: Un modelo basado en el análisis espectral de registros de FV es una herramienta prometedora para obtener datos pronósticos precoces tras MS por FV.