Network Theoretical Approach to Describe Epileptic Processes

Epilepsy is characterized by recurrent unprovoked seizures. Recent studies suggest that seizure generation may be caused by the abnormal activity of the entire network. This new paradigm requires new tools and methods for its study. In this sense, synchronization by linear as well as nonlinear measures are used to determine network structure and functional connectivity of neurophysiological data. Electroencephalography (EEG) data can be analyzed using each electrode’s activity as a node of the underlying cortical network. The information provided by the synchronization matrix is the basic brick upon which several lines of analysis can be performed thereafter. Detection of community structures, identification of centrality nodes, transformation of the underlying network into a simpler one, and the identification of the basic network architecture are only some of the many lines of basic works that can be done in order to characterize the epilepsy as a network disease. This chapter describes new approaches in network epilepsy, provides mathematical concepts in order to understand the complex network analyses, and reviews the advances in network analyses and its application to epilepsy research

Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms

Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development.Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed.Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%.Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Vera, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Camarasaltas, Juan Miguel. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Alfonso, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Ortega, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Jimenez Borreguero, Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; Españ

Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study

Objective: Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. Methods: We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. Results: After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). Conclusion: This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stabilityEuropean Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No860813 – TReSPAsS-ETNTRESPASS-ET

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study

Producción CientíficaIdentifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation—validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51–81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841–0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.Junta de Castilla y León (Gerencia Regional de Salud - grant number GRS 1903/A/19 and GRS 2131/A/20)Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033/’, ERDF A way of making Europe, and Next GenerationEU/PRTR (under projects PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red (Instituto de Salud Carlos III) (CB19/01/00012)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach

Objective. Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale -4 and -5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes. Approach. We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twenty-six adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm. Main results. More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement. Significance. The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.Fil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pérez Romero, Miriam. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pastor, Jesús. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Sola, Rafael G.. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Vega Zelaya, Lorena. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Vega, Gema. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Monasterio, Fernando. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Torrecilla, Carmen. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pulido, Paloma. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa. Servicio de Neurocirugia. Grupo de Epilepsia; EspañaFil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Prehospital point-of-care lactate increases the prognostic accuracy of national early warning score 2 for early risk stratification of mortality: results of a multicenter, observational study

The objective of this study was to assess whether the use of prehospital lactate (pLA) can increase the prognostic accuracy of the National Early Warning Score 2 (NEWS2) to detect the risk of death within 48 h. A prospective, multicenter study in adults treated consecutively by the emergency medical services (EMS) included six advanced life support (ALS) services and five hospitals. Patients were assigned to one of four groups according to their risk of mortality (low, low-medium, medium, and high), as determined by the NEWS2 score. For each group, the validity of pLA in our cohort was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. In this study, 3081 participants with a median age of 69 years (Interquartile range (IQR): 54–81) were included. The two-day mortality was 4.4% (137 cases). The scale derived from the implementation of the pLA improved the capacity of the NEWS2 to discriminate low risk of mortality, with an AUC of 0.910 (95% CI: 0.87–0.94; p < 0.001). The risk stratification provided by the NEWS2 can be improved by incorporating pLA measurement to more accurately predict the risk of mortality in patients with low risk.Fil: Martín Rodríguez, Francisco. Universidad de Valladolid; España. Emergency Medical Services; EspañaFil: López Izquierdo, Raul. Hospital Universitario Rio Hortega; EspañaFil: Delgado Benito, Juan F.. Emergency Medical Services; EspañaFil: Sanz García, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Pozo Vegas, Carlos del. Hospital Clínico Universitario de Valladolid; EspañaFil: Castro Villamor, Miguel Ángel. Universidad de Valladolid; EspañaFil: Martín Conty, José Luis. Universidad de Castilla-La Mancha; EspañaFil: Ortega, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; Españ

DNA copy number variation associated with anti-tumour necrosis factor drug response and paradoxical psoriasiform reactions in patients with moderate-to-severe psoriasis

Biological drugs targeting tumour necrosis factor are effective for psoriasis. However, 30–50% of patients do not respond to these drugs and may even develop paradoxical psoriasiform reactions. This study search-ed for DNA copy number variations that could predict anti-tumour necrotic factor drug response or the ap-pearance of anti-tumour necrotic factor induced pso-riasiform reactions. Peripheral blood samples were collected from 70 patients with anti-tumour necrotic factor drug-treated moderate-to-severe plaque pso-riasis. Samples were analysed with an Illumina 450K methylation microarray. Copy number variations were obtained from raw methylation data using conumee and Chip Analysis Methylation Pipeline (ChAMP) R packa-ges. One copy number variation was found, harbouring one gene (CPM) that was significantly associated with adalimumab response (Bonferroni-adjusted p-value < 0.05). Moreover, one copy number variation was identified harbouring 3 genes (ARNT2, LOC101929586 and MIR5572) related to the development of paradoxical psoriasiform reactions. In conclusion, this study has identified DNA copy number variations that could be good candidate markers to predict response to ada-limumab and the development of anti-tumour necrotic factor paradoxical psoriasiform reactions.This study was supported by Instituto de Salud Carlos III PI 13/01598 and the Ministry of Science and Innovation and the European Regional Development’s funds (FEDER). Conflicts of interest. FA-S has been a consultant or investigator in clinical trials sponsored by the following pharmaceutical companies: Abbott, Alter, Chemo, Farmalíder, Ferrer, GlaxoSmithKline, Gilead, Janssen-Cilag, Kern, Normon, Novartis, Servier, Teva, and Zambon. ED has potential conflicts of interest (advisory board member, consultant, grants, research support, participation in clinical trials, honoraria for speaking, and research support) with the following pharmaceutical companies: AbbVie (Abbott), Amgen, Janssen-Cilag, Leo Pharma, Novartis, Pfizer, MSD, Lilly and Celgene. ML-V has potential conflicts of interest as she has participated in clinical trials or as consultant with Abbvie (Abbott), Galderma, Janssen-Cilag, Leo Pharma, Pfizer, Novarties, Lilly, Almirall and Celgene. MCO-B has potential conflicts of interest (honoraria for speaking and research support) with Janssen-Cilag and Leo Pharma. The other authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. AS-T has served as a consultant and/or paid speaker for and/or participated in clinical trials sponsored by companies that manufacture drugs used for the treatment of psoriasis, including AbbVie, Celgene, Janssen-Cilag, LEO Pharma, Lilly, Novartis and Pfizer. RB-E has served as a consultant and/or paid speaker for and/or participated in clinical trials sponsored by companies that manufacture drugs used for the treatment of psoriasis, including AbbVie, Celgene, Janssen-Cilag, LEO Pharma, Lilly, Novartis and Pfizer

Neuronal and astrocytic tetraploidy is increased in drug-resistant epilepsy

Aims: Epilepsy is one of the most prevalent neurological diseases. A third of patients with epilepsy remain drug-resistant. The exact aetiology of drug-resistant epilepsy (DRE) is still unknown. Neuronal tetraploidy has been associated with neuropathology. The aim of this study was to assess the presence of tetraploid neurons and astrocytes in DRE. Methods. For that purpose, cortex, hippocampus and amygdala samples were obtained from patients subjected to surgical resection of the epileptogenic zone. Post-mortem brain tissue of subjects without previous records of neurological, neurodegenerative or psychiatric diseases was used as control. Results: The percentage of tetraploid cells was measured by immunostaining of neurons (NeuN) or astrocytes (S100β) followed by flow cytometry analysis. The results were confirmed by image cytometry (ImageStream X Amnis System Cytometer) and with an alternative astrocyte biomarker (NDRG2). Statistical comparison was performed using univariate tests. A total of 22 patients and 10 controls were included. Tetraploid neurons and astrocytes were found both in healthy individuals and DRE patients in the three brain areas analysed: cortex, hippocampus and amygdala. DRE patients presented a higher number of tetraploid neurons (p = 0.020) and astrocytes (p = 0.002) in the hippocampus than controls. These results were validated by image cytometry. Conclusions: We demonstrated the presence of both tetraploid neurons and astrocytes in healthy subjects as well as increased levels of both cell populations in DRE patients. Herein, we describe for the first time the presence of tetraploid astrocytes in healthy subjects. Furthermore, these results provide new insights into epilepsy, opening new avenues for future treatment

Usefulness of the CONUT index upon hospital admission as a potential prognostic indicator of COVID-19 health outcomes

Background: In-hospital mortality in patients with coronavirus disease 2019 (COVID-19) is high. Simple prognostic indices are needed to identify patients at high-risk of COVID-19 health outcomes. We aimed to determine the usefulness of the CONtrolling NUTritional status (CONUT) index as a potential prognostic indicator of mortality in COVID-19 patients upon hospital admission. Methods: Our study design is of a retrospective observational study in a large cohort of COVID-19 patients. In addition to descriptive statistics, a Kaplan-Meier mortality analysis and a Cox regression were performed, as well as receiver operating curve (ROC). Results: From February 5, 2020 to January 21, 2021, there was a total of 2969 admissions for COVID-19 at our hospital, corresponding to 2844 patients. Overall, baseline (within 4 days of admission) CONUT index could be scored for 1627 (57.2%) patients. Patients' age was 67.3 ± 16.5 years and 44.9% were women. The CONUT severity distribution was: 194 (11.9%) normal (0-1); 769 (47.2%) light (2-4); 585 (35.9%) moderate (5-8); and 79 (4.9%) severe (9-12). Mortality of 30 days after admission was 3.1% in patients with normal risk CONUT, 9.0% light, 22.7% moderate, and 40.5% in those with severe CONUT (P < 0.05). An increased risk of death associated with a greater baseline CONUT stage was sustained in a multivariable Cox regression model (P < 0.05). An increasing baseline CONUT stage was associated with a longer duration of admission, a greater requirement for the use of non-invasive and invasive mechanical ventilation, and other clinical outcomes (all P < 0.05). The ROC of CONUT for mortality had an area under the curve (AUC) and 95% confidence interval of 0.711 (0.676-0746). Conclusion: The CONUT index upon admission is potentially a reliable and independent prognostic indicator of mortality and length of hospitalization in COVID-19 patientsThe work is supported by a grant from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement (No 101016216

Sex differences in the impact of frailty in elderly outpatients with heart failure

Introduction: Frailty is common among patients with heart failure (HF). Our aim was to address the role of frailty in the management and prognosis of elderly men and women with HF. Methods and results: Prospective multicenter registry that included 499 HF outpatients ≥75 years old. Mean age was 81.4 ± 4.3 years, and 193 (38%) were women. Compared with men, women were older (81.9 ± 4.3 vs. 81.0 ± 4.2 years, p = 0.03) and had higher left ventricular ejection fraction (46 vs. 40%, p < 0.001) and less ischemic heart disease (30 vs. 57%, p < 0.001). Women had a higher prevalence of frailty (22 vs. 10% with Clinical Frailty Scale, 34 vs. 15% with FRAIL, and 67% vs. 46% with the mobility visual scale, all p-values < 0.001) and other geriatric conditions (Barthel index ≤90: 14.9 vs. 6.2%, p = 0.003; malnutrition according to Mini Nutritional Assessment Short Formulary ≤11: 55% vs. 42%, p = 0.007; Pfeiffer cognitive test's errors: 1.6 ± 1.7 vs. 1.0 ± 1.6, p < 0.001; depression according to Yesavage test; p < 0.001) and lower comorbidity (Charlson index ≥4: 14.1% vs. 22.1%, p = 0.038). Women also showed worse self-reported quality of life (6.5 ± 2.1 vs. 6.9 ± 1.9, on a scale from 0 to 10, p = 0.012). In the univariate analysis, frailty was an independent predictor of mortality in men [Hazard ratio (HR) 3.18, 95% confidence interval (CI) 1.29-7.83, p = 0.012; HR 4.53, 95% CI 2.08-9.89, p < 0.001; and HR 2.61, 95% CI 1.23-5.43, p = 0.010, according to FRAIL, Clinical Frailty Scale, and visual mobility scale, respectively], but not in women. In the multivariable analysis, frailty identified by the visual mobility scale was an independent predictor of mortality (HR 1.95, 95% CI 1.04-3.67, p = 0.03) and mortality/readmission (HR 2.06, 95% CI 1.05-4.04, p = 0.03) in men. Conclusions: In elderly outpatients with HF frailty is more common in women than in men. However, frailty is only associated with mortality in men