Grupo español de cirugía torácica asistida por videoimagen: método, auditoría y resultados iniciales de una cohorte nacional prospectiva de pacientes tratados con resecciones anatómicas del pulmón

Introduction: our study sought to know the current implementation of video-assisted thoracoscopic surgery (VATS) for anatomical lung resections in Spain. We present our initial results and describe the auditing systems developed by the Spanish VATS Group (GEVATS). Methods: we conducted a prospective multicentre cohort study that included patients receiving anatomical lung resections between 12/20/2016 and 03/20/2018. The main quality controls consisted of determining the recruitment rate of each centre and the accuracy of the perioperative data collected based on six key variables. The implications of a low recruitment rate were analysed for '90-day mortality' and 'Grade IIIb-V complications'. Results: the series was composed of 3533 cases (1917 VATS; 54.3%) across 33 departments. The centres' median recruitment rate was 99% (25-75th:76-100%), with an overall recruitment rate of 83% and a data accuracy of 98%. We were unable to demonstrate a significant association between the recruitment rate and the risk of morbidity/mortality, but a trend was found in the unadjusted analysis for those centres with recruitment rates lower than 80% (centres with 95-100% rates as reference): grade IIIb-V OR=0.61 (p=0.081), 90-day mortality OR=0.46 (p=0.051). Conclusions: more than half of the anatomical lung resections in Spain are performed via VATS. According to our results, the centre's recruitment rate and its potential implications due to selection bias, should deserve further attention by the main voluntary multicentre studies of our speciality. The high representativeness as well as the reliability of the GEVATS data constitute a fundamental point of departure for this nationwide cohort

Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: A New Methodology to Identify Viral Features

In this article, we introduce a novel methodology for characterizing viral genetic features: the Unified Methodology of recombinant virus Identification (UMI). Our methodology converts genomic sequences into spectrograms, applies transfer learning using a pre-trained Convolutional Neural Network (CNN), and employs interpretability tools to identify the genomic regions relevant for characterizing a viral sequence as recombinant. The UMI methodology does not necessitate multiple sequence alignment or manual adjustments. As a result, it operates much faster, has low computational demands, and is capable of handling substantial amounts of data. To validate this, we applied UMI to one extensively studied and documented case: HIV-1 genetic recombination. We worked with all identified HIV-1 complete sequences (13554 sequences up to 2020), searching for mathematical patterns, signatures, that characterize an HIV-1 sequence as recombinant. CNN’s hit rate (test accuracy) is 94%, with consistent and differentiated decision areas in each category. Using interpretability tools, we verified that the hot zones were similar for sequences of the same subtype and phylogenetic proximity. The leading areas for classifying a sequence as recombinant or non-recombinant are coincident with genomic regions that play a key role in genetic recombination processes. By applying UMI methodology we found that there is indeed a genome mathematical pattern that assesses an HIV-1 sequence as recombinant. In addition, we located its position. Considering expert knowledge, our results showed a substantial, robust and biologically-consistent hit rate. This type of solution can successfully guide the location and subsequent characterization of relevant areas, avoiding the heavy analysis of multiple sequence alignment and manual adjustments

Efficient Machine Learning on Edge Computing Through Data Compression Techniques

This paper discusses the increasing amount of data handled by companies and the need to use Big Data and Data Analytics to extract value from this data. However, due to the large amount of data collected, challenges related to the computational capacity of machines often arise when performing this analysis to acquire relevant information for the organization, especially when we are using edge computing. The paper aims to train machine learning models using compressed data, with two compression techniques applied to the original data. The results show that models trained with compressed data achieved similar accuracy to those trained with uncompressed data, and different compression techniques were compared. The research extended a previous study by analyzing the use of autoencoders for compression and reducing both instances and dimensionality of the dataset. The accuracy rate of the models when trained with compressed data instead of original data was maintained

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts

Executive summary of the SEPAR recommendations for the diagnosis and treatment of non-small cell lung cancer

The Thoracic Surgery and Thoracic Oncology groups of the Spanish Society of Pulmonology and Thoracic Surgery (SEPAR) have backed the publication of a handbook on recommendations for the diagnosis and treatment of non-small cell lung cancer. Due to the high incidence and mortality of this disease, the best scientific evidence must be constantly updated and made available for consultation by healthcare professionals.To draw up these recommendations, we called on a wide-ranging group of experts from the different specialties, who have prepared a comprehensive review, divided into 4 main sections. The first addresses disease prevention and screening, including risk factors, the role of smoking cessation, and screening programs for early diagnosis. The second section analyzes clinical presentation, imaging studies, and surgical risk, including cardiological risk and the evaluation of respiratory function. The third section addresses cytohistological confirmation and staging studies, and scrutinizes the TNM and histological classifications, non-invasive and minimally invasive sampling methods, and surgical techniques for diagnosis and staging. The fourth and final section looks at different therapeutic aspects, such as the role of surgery, chemotherapy, radiation therapy, a multidisciplinary approach according to disease stage, and other specifically targeted treatments, concluding with recommendations on the follow-up of lung cancer patients and surgical and endoscopic palliative interventions in advanced stages. (C) 2016 SEPAR. Published by Elsevier Espana, S.L.U. All rights reserved

Impact of SARS-CoV-2 vaccination and monoclonal antibodies on outcome post CD19-CAR-T : an EPICOVIDEHA survey

Patients with previous CD19 directed chimeric antigen receptor T cell therapy (CAR T)-cell therapy have a prolonged vulnerability to viral infections. Coronavirus diseases 2019 (COVID-19) has a great impact and has previously been shown to cause high mortality in this population. Until now, real world data of the impact of vaccination and treatment on patients with COVID-19 after CD19 directed CAR T-cell therapy are lacking. Therefore, this multicenter retrospective study was conducted with data from the EPICOVIDEHA survey. Sixty-four patients were identified. The overall mortality caused by COVID-19 was 31%. Patients infected with the Omicron variant had a significantly lower risk of death due to COVID-19 compared to patients infected with previous variants (7% versus 58% (P=0.012)). Twenty-six patients were vaccinated at time of COVID-19 diagnosis. Two vaccinations showed marked but unsignificant reduction risk of COVID-19 caused mortality (33.3% versus 14.2% (P=0.379)).Also the course of disease appears milder with less frequent ICU admissions (39% versus 14% (P=0.054)) and shorter duration of hospitalization (7 versus 27.5 days (P=0.022)). Of the available treatment options, only monoclonal antibodies seemed to be effectively reducing mortality from 32% to zero (P=0.036). We conclude that survival rates of CAR T-cell recipients with COVID-19 improved over time and that the combination of prior vaccination and monoclonal antibody treatment significantly reduces their risk of death

Subcutaneous anti-COVID-19 hyperimmune immunoglobulin for prevention of disease in asymptomatic individuals with SARS-CoV-2 infection: a double-blind, placebo-controlled, randomised clinical trialResearch in context

Summary: Background: Anti-COVID-19 hyperimmune immunoglobulin (hIG) can provide standardized and controlled antibody content. Data from controlled clinical trials using hIG for the prevention or treatment of COVID-19 outpatients have not been reported. We assessed the safety and efficacy of subcutaneous anti-COVID-19 hyperimmune immunoglobulin 20% (C19-IG20%) compared to placebo in preventing development of symptomatic COVID-19 in asymptomatic individuals with SARS-CoV-2 infection. Methods: We did a multicentre, randomized, double-blind, placebo-controlled trial, in asymptomatic unvaccinated adults (≥18 years of age) with confirmed SARS-CoV-2 infection within 5 days between April 28 and December 27, 2021. Participants were randomly assigned (1:1:1) to receive a blinded subcutaneous infusion of 10 mL with 1 g or 2 g of C19-IG20%, or an equivalent volume of saline as placebo. The primary endpoint was the proportion of participants who remained asymptomatic through day 14 after infusion. Secondary endpoints included the proportion of individuals who required oxygen supplementation, any medically attended visit, hospitalisation, or ICU, and viral load reduction and viral clearance in nasopharyngeal swabs. Safety was assessed as the proportion of patients with adverse events. The trial was terminated early due to a lack of potential benefit in the target population in a planned interim analysis conducted in December 2021. ClinicalTrials.gov registry: NCT04847141. Findings: 461 individuals (mean age 39.6 years [SD 12.8]) were randomized and received the intervention within a mean of 3.1 (SD 1.27) days from a positive SARS-CoV-2 test. In the prespecified modified intention-to-treat analysis that included only participants who received a subcutaneous infusion, the primary outcome occurred in 59.9% (91/152) of participants receiving 1 g C19-IG20%, 64.7% (99/153) receiving 2 g, and 63.5% (99/156) receiving placebo (difference in proportions 1 g C19-IG20% vs. placebo, −3.6%; 95% CI -14.6% to 7.3%, p = 0.53; 2 g C19-IG20% vs placebo, 1.1%; −9.6% to 11.9%, p = 0.85). None of the secondary clinical efficacy endpoints or virological endpoints were significantly different between study groups. Adverse event rate was similar between groups, and no severe or life-threatening adverse events related to investigational product infusion were reported. Interpretation: Our findings suggested that administration of subcutaneous human hyperimmune immunoglobulin C19-IG20% to asymptomatic individuals with SARS-CoV-2 infection was safe but did not prevent development of symptomatic COVID-19. Funding: Grifols