Design and rationale of a multicentre, randomised, double-blind, placebo-controlled clinical trial to evaluate the effect of vitamin D on ventricular remodelling in patients with anterior myocardial infarction: the VITamin D in Acute Myocardial Infarction (VITDAMI) trial

Introduction:Decreased plasma vitamin D (VD) levels are linked to cardiovascular damage. However, clinical trials have not demonstrated a benefit of VD supplements on left ventricular (LV) remodelling. Anterior ST-elevation acute myocardial infarction (STEMI) is the best human model to study the effect of treatments on LV remodelling. We present a proof-of-concept study that aims to investigate whether VD improves LV remodelling in patients with anterior STEMI. Methods and analysis:The VITamin D in Acute Myocardial Infarction (VITDAMI) trial is a multicentre, randomised, double-blind, placebo-controlled trial. 144 patients with anterior STEMI will be assigned to receive calcifediol 0.266 mg capsules (Hidroferol SGC)/15 days or placebo on a 2:1 basis during 12 months. Primary objective:to evaluate the effect of calcifediol on LV remodelling defined as an increase in LV end-diastolic volume >= 10\% (MRI). Secondary objectives:change in LV end-diastolic and end-systolic volumes, ejection fraction, LV mass, diastolic function, sphericity index and size of fibrotic area; endothelial function; plasma levels of aminoterminal fragment of B-type natriuretic peptide, galectin-3 and monocyte chemoattractant protein-1; levels of calcidiol (VD metabolite) and other components of mineral metabolism (fibroblast growth factor-23 (FGF-23), the soluble form of its receptor klotho, parathormone and phosphate). Differences in the effect of VD will be investigated according to the plasma levels of FGF-23 and klotho. Treatment safety and tolerability will be assessed. This is the first study to evaluate the effect of VD on cardiac remodelling in patients with STEMI. Ethics and dissemination: This trial has been approved by the corresponding Institutional Review Board (IRB) and National Competent Authority (Agencia Espanola de Medicamentos y Productos Sanitarios (AEMPS)). It will be conducted in accordance with good clinical practice (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use-Good Clinical Practice (ICH-GCP)) requirements, ethical principles of the Declaration of Helsinki and national laws. The results will be submitted to indexed medical journals and national and international meetings.The VITDAMI trial is an investigator initiated study, sponsored by the Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz (IIS-FJD). Funding has been obtained from Fondo de Investigaciones Sanitarias (PI14/01567; http://www.isciii.es/) and Spanish Society of Cardiology (http://secardiologia.es/). In addition, the study medication has been provided freely by the pharmaceutical Company FAES FARMA S.A. (Leioa, Vizcaya, Spain; http://faesfarma.com/). This company was the only funder who collaborated in study design (IG-H).S

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Continuous monitoring of intrinsic PEEP based on expired CO2 kinetics : an experimental validation study

Background Quantification of intrinsic PEEP (PEEPi) has important implications for patients subjected to invasive mechanical ventilation. A new non-invasive breath-by-breath method (etCO(2)D) for determination of PEEPi is evaluated. MethodsIn 12 mechanically ventilated pigs, dynamic hyperinflation was induced by interposing a resistance in the endotracheal tube. Airway pressure, flow, and exhaled CO2 were measured at the airway opening. Combining different I:E ratios, respiratory rates, and tidal volumes, 52 different levels of PEEPi (range 1.8-11.7cmH(2)O; mean 8.450.32cmH(2)O) were studied. The etCO(2)D is based on the detection of the end-tidal dilution of the capnogram. This is measured at the airway opening by means of a CO2 sensor in which a 2-mm leak is added to the sensing chamber. This allows to detect a capnogram dilution with fresh air when the pressure coming from the ventilator exceeds the PEEPi. This method was compared with the occlusion method. Results The etCO(2)D method detected PEEPi step changes of 0.2cmH(2)O. Reference and etCO(2)D PEEPi presented a good correlation (R-2 0.80, P<0.0001) and good agreement, bias -0.26, and limits of agreement +/- 1.96 SD (2.23, -2.74) (P<0.0001). Conclusions The etCO(2)D method is a promising accurate simple way of continuously measure and monitor PEEPi. Its clinical validity needs, however, to be confirmed in clinical studies and in conditions with heterogeneous lung diseases

Nocturnal Hypoxemia and CT Determined Pulmonary Artery Enlargement in Smokers

Background: Pulmonary artery enlargement (PAE) detected using chest computed tomography (CT) is associated with poor outcomes in chronic obstructive pulmonary disease (COPD). It is unknown whether nocturnal hypoxemia occurring in smokers, with or without COPD, obstructive sleep apnoea (OSA) or their overlap, may be associated with PAE assessed by chest CT. Methods: We analysed data from two prospective cohort studies that enrolled 284 smokers in lung cancer screening programs and completing baseline home sleep studies and chest CT scans. Main pulmonary artery diameter (PAD) and the ratio of the PAD to that of the aorta (PA:Ao ratio) were measured. PAE was defined as a PAD ≥ 29 mm in men and ≥27 mm in women or as a PA:Ao ratio > 0.9. We evaluated the association of PAE with baseline characteristics using multivariate logistic models. Results: PAE prevalence was 27% as defined by PAD measurements and 11.6% by the PA:Ao ratio. A body mass index ≥ 30 kg/m2 (OR 2.01; 95%CI 1.06–3.78), lower % predicted of forced expiratory volume in one second (FEV1) (OR 1.03; 95%CI 1.02–1.05) and higher % of sleep time with O2 saturation < 90% (T90) (OR 1.02; 95%CI 1.00–1.03), were associated with PAE as determined by PAD. However, only T90 remained significantly associated with PAE as defined by the PA:Ao ratio (OR 1.02; 95%CI 1.01–1.03). In the subset group without OSA, only T90 remains associated with PAE, whether defined by PAD measurement (OR 1.02; 95%CI 1.01–1.03) or PA:Ao ratio (OR 1.04; 95%CI 1.01–1.07). Conclusions: In smokers with or without COPD, nocturnal hypoxemia was associated with PAE independently of OSA coexistence

Nocturnal Hypoxemia and CT Determined Pulmonary Artery Enlargement in Smokers

Background: Pulmonary artery enlargement (PAE) detected using chest computed tomography (CT) is associated with poor outcomes in chronic obstructive pulmonary disease (COPD). It is unknown whether nocturnal hypoxemia occurring in smokers, with or without COPD, obstructive sleep apnoea (OSA) or their overlap, may be associated with PAE assessed by chest CT. Methods: We analysed data from two prospective cohort studies that enrolled 284 smokers in lung cancer screening programs and completing baseline home sleep studies and chest CT scans. Main pulmonary artery diameter (PAD) and the ratio of the PAD to that of the aorta (PA:Ao ratio) were measured. PAE was defined as a PAD ≥ 29 mm in men and ≥27 mm in women or as a PA:Ao ratio &gt; 0.9. We evaluated the association of PAE with baseline characteristics using multivariate logistic models. Results: PAE prevalence was 27% as defined by PAD measurements and 11.6% by the PA:Ao ratio. A body mass index ≥ 30 kg/m2 (OR 2.01; 95%CI 1.06–3.78), lower % predicted of forced expiratory volume in one second (FEV1) (OR 1.03; 95%CI 1.02–1.05) and higher % of sleep time with O2 saturation &lt; 90% (T90) (OR 1.02; 95%CI 1.00–1.03), were associated with PAE as determined by PAD. However, only T90 remained significantly associated with PAE as defined by the PA:Ao ratio (OR 1.02; 95%CI 1.01–1.03). In the subset group without OSA, only T90 remains associated with PAE, whether defined by PAD measurement (OR 1.02; 95%CI 1.01–1.03) or PA:Ao ratio (OR 1.04; 95%CI 1.01–1.07). Conclusions: In smokers with or without COPD, nocturnal hypoxemia was associated with PAE independently of OSA coexistence

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score