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Stroke is the leading cause of neurological disability in people over 40 years of age and the fourth leading cause of death in Argentina. In the last ten years, the indexed publications related to the treatment of ischemic stroke were more numerous than those of hemorrhagic stroke. The objective of this material is to provide local and updated recommendations for the management of patients with spontaneous intracerebral hemorrhage during hospitalization. For the writing of this manuscript, diferent specialists were convened to form working groups. Ten central topics expressed as epidemiology, initial care, imaging, blood pressure treatment, reversal of antithrombotics, indication for surgery, seizure prophylaxis, prognosis, prevention of complications and resumption of antithrombotics were raised. For each topic, the most frequent questions of daily practice were raised through PICO questions. After a systematic review of the literature, recommendations were generated, evaluated using the GRADE system and agreed between authors and patients.El accidente cerebrovascular (ACV) constituye la principal causa de discapacidad de origen neurológico en los adultos mayores a 40 años y la cuarta causa de muerte en Argentina. En los últimos diez años las publicaciones indexadas relacionadas al tratamiento del ACV isquémico fueron más numerosas que las de ACV hemorrágico. El objetivo de este material es proporcionar recomendaciones locales y actualizadas del abordaje de pacientes con hematoma intraparenquimatoso espontáneo durante la internación. Para la redacción de este manuscrito se convocó a especialistas en esta enfermedad que conformaron grupos de trabajo. Se plantearon 10 tópicos centrales expresados como epidemiologia, atención inicial, imágenes, tratamiento de la presión arterial, reversión de antitrombóticos, indicación de cirugía, profilaxis anticonvulsivante, pronóstico, prevención de complicaciones y reinicio de antitrombóticos. De cada tópico se plantearon mediante preguntas PICO los interrogantes más frecuentes de la práctica diaria. Luego de una revisión sistemática de la literatura, se generaron recomendaciones evaluadas mediante sistema GRADE y consensuadas entre autores y pacientes.

SummaryBackgroundMore contagious SARS-CoV-2 virus variants, breakthrough infections, waning immunity, and sub-optimal rates of COVID-19 vaccination account for a new surge of infections leading to record numbers of hospitalizations and deaths in several European countries. This is a particularly concerning scenario for resource-limited countries, which have a lower vaccination rate and fewer clinical tools to fight against the next pandemic waves. There is an urgent need for clinically valuable, generalizable, and parsimonious triage tools assisting the appropriate allocation of hospital resources. We aimed to develop and extensively validate CODOP, a machine learning-based tool for accurately predicting the clinical outcome of hospitalized COVID-19 patients.MethodsCODOP was built using modified stable iterative variable selection and linear regression with lasso regularisation. To avoid generalization problems, CODOP was trained and tested with three time-sliced and geographically distinct cohorts encompassing 40 511 blood-based analyses of COVID-19 patients from more than 110 hospitals in Spain and the USA during 2020-21. We assessed the discriminative ability of the model using the Area Under the Receiving Operative Curve (AUROC) as well as horizon and Kaplan-Meier risk stratification analyses. To reckon the fluctuating pressure levels in hospitals through the pandemic, we offer two online CODOP calculators suited for undertriage or overtriage scenarios. We challenged their generalizability and clinical utility throughout an evaluation on a cohort of patients hospitalized in five hospitals from three Latin American countries.FindingsCODOP uses 12 clinical parameters commonly measured at hospital admission and associated with the pathophysiology of COVID-19. CODOP reaches high discriminative ability up to nine days before clinical resolution (AUROC: 0·90-0·96, 95% CI 0·879-0·970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. The two CODOP online calculators demonstrate their potential for triage decisions when challenged with the distinctive Latin American evaluation cohorts (73-100% sensitivity and 84-100% specificity).InterpretationThe high predictive performance of CODOP in geographically disperse patient cohorts and the easiness-of-use, strongly suggest its clinical utility as a global triage tool, particularly in resource-limited countries.FundingThe Max Planck Society.Research in contextEvidence before this studyWe have searched PubMed for articles about the existence of in-hospital COVID-19 mortality predictive models, using the search terms “coronavirus”, “COVID-19”, “risk”, “death”, “mortality”, and “prediction”, focusing on studies published between March 1, 2020 and 31 August, 2021. The studies we identified generally used small-medium size cohorts of patients that are geographically restricted to small regions of the developed world (many times, to the same city). We haven’t found studies that challenged their models in extended cohorts of patients from very distinct health system populations, particularly from resource-limited countries. Further, most of the previous models are rigid by not acknowledging the fluctuating availability of hospital resources during the pandemic (e.g., beds, oxygen supply). These and other limitations have been pointed out by expert reviews indicating that published in-hospital COVID-19 mortality predictive models are subject to high risk of bias, report an over-optimistic performance, and have limited clinical value in assisting daily triage decisions. A parsimonious, accurate and extensively validated model is yet to be developed.Added value of this studyWe analysed clinical data from different cohorts totalling 21 607 COVID-19 patients treated in more than 110 hospitals in Spain and the USA during three different pandemic waves extending from February 2020 to April 2021. The new CODOP in-hospital mortality prediction model is based on 11 blood biochemistry parameters (representing main biological pathways involved in the pathogenesis of SARS-CoV-2) plus Age, all of them commonly measured upon hospitalization. CODOP accurately predicted mortality risk up to nine days before clinical resolution (AUROC: 0·90-0·96, 95% CI 0·879-0·970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. We offer two online CODOP calculator subtypes (https://gomezvarelalab.em.mpg.de/codop/) tailored to overtriage and undertriage scenarios. The online calculators were able to reach the desired prediction performance in five independent evaluation cohorts gathered in hospitals of three Latin American countries from March 7th 2020 to June 7th 2021.Implications of all the available evidenceWe present here a highly accurate, parsimonious and extensively validated COVID-19 in-hospital mortality prediction model, derived from working with the largest number and the most geographically extended representation of patients and health systems to date.The rigorous analytical methods, the generalizability of the model in distinct world regions, and its flexibility to reckon with the changing availability of hospital resources point to CODOP as a clinically useful tool potentially improving the outcome prediction and the management of COVID-19 hospitalized patients.

Purpose To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. Materials and methods An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. Results Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p, Highlights • An AI system predicted COVID-19 pneumonia with AUROC of 0.96 and 0.83 in the validation and external test sets respectively. • The AI system outperformed physicians (70% increase in sensitivity and 1% increase in specificity, p

It has been established that first-degree relatives of patients with chronic kidney disease (CKD) have a higher CKD risk than the overall population. This paper deals with the relative frequency of CKD markers and cardiovascular (CV) risk factors within first-degree relatives of ESRD patients in Argentina.810 family members volunteered to participate; of them 668 over 18 ys. old. Trained nurses interviewed them and completed a questionnaire dealing with family history of renal and cardiovascular disease. Blood pressure, urine and blood analysis and anthropometric data were collected. Selected parameters were: smoking habit, presence of high blood pressure, diabetes, hypercholesterolemia, high plasma creatinine and creatinine clearance estimated by MDRD formula, proteinuria and microalbuminuria. In pediatric population, weight and blood pressure parameters were evaluated as percentiles. CKD were classified in stage (National Kidney Foundation).The relative frequencies were: CKD: 29.6%; proteinuria: 13.9%; microalbuminuria: 8.7%. The prevalence values found for main CV risks factors, adjusted by sex and age, were: high blood pressure= 41.8%; overweight/obesity by BMI= 62.1%, hypercholesterolemia= 42.9% and hyperglycemia= 5.2%. Smoking habit was present in 34.8%.Prevalence of overweight/obesity, hypertension and hypercholesterolemia in first-degree relatives of ESRD patients is higher than previously communicated in studies of national reference populations. Prevalence of CKD is high, estimated as three-fold higher than for a general population as reported in poblational studies. These results support the fact that first-degree relatives of ESRD patients, as has been established elsewhere, constitute a population at high risk for developing ESRD.