Background COVID-19 is a multisystem disease and patients who survive might have in-hospital complications. These complications are likely to have important short-term and long-term consequences for patients, health-care utilisation, health-care system preparedness, and society amidst the ongoing COVID-19 pandemic. Our aim was to characterise the extent and effect of COVID-19 complications, particularly in those who survive, using the International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol UK.Methods We did a prospective, multicentre cohort study in 302 UK health-care facilities. Adult patients aged 19 years or older, with confirmed or highly suspected SARS-CoV-2 infection leading to COVID-19 were included in the study. The primary outcome of this study was the incidence of in-hospital complications, defined as organ-specific diagnoses occurring alone or in addition to any hallmarks of COVID-19 illness. We used multilevel logistic regression and survival models to explore associations between these outcomes and in-hospital complications, age, and pre-existing comorbidities. FindingsBetween Jan 17 and Aug 4, 2020, 80 388 patients were included in the study. Of the patients admitted to hospital for management of COVID-19, 49•7% (36 367 of 73 197) had at least one complication. The mean age of our cohort was 71•1 years (SD 18•7), with 56•0% (41 025 of 73 197) being male and 81•0% (59 289 of 73 197) having at least one comorbidity. Males and those aged older than 60 years were most likely to have a complication (aged ≥60 years:

No abstract

Características do acesso e utilização de serviços odontológicos em municípios de médio porteCharacteristics of the access and utilization of public dental services in medium-sized cities

Governo local e serviços odontológicos: análise da desigualdade na utilização Local government and public dental health services: an analysis of inequality in use Gobierno local y servicios odontológicos: análisis de la desigualdad en el uso

Objective To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design Two stage individual participant data meta-analysis. Setting Secondary and tertiary care. Participants 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality. Results Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Background: The SARS-CoV-2 virus responsible for COVID-19 poses a significant challenge to healthcare systems worldwide. Despite governmental initiatives aimed at containing the spread of the disease, several countries are experiencing unmanageable increases in the demand for ICU beds, medical equipment, and larger testing capacity. Efficient COVID-19 diagnosis enables healthcare systems to provide better care for patients while protecting caregivers from the disease. However, many countries are constrained by the limited amount of test kits available, the lack of equipment and trained professionals. In the case of patients visiting emergency rooms (ERs) with a suspect of COVID-19, a prompt diagnosis can improve the outcome and even provide information for efficient hospital management. In this context, a quick, inexpensive and readily available test to perform an initial triage at ER could help to smooth patient flow, provide better patient care, and reduce the backlog of exams. Methods: In this Case-control quantitative study, we developed a strategy backed by artificial intelligence to perform an initial screening of suspect COVID-19 cases. We developed a machine learning classifier that takes widely available simple blood exams as input and predicts if that suspect case is likely to be positive (having SARS-CoV-2) or negative(not having SARS-CoV-2). Based on this initial classification, positive cases can be referred for further highly sensitive testing (e.g. CT scan, or specific antibodies). We used publicly available data from the Albert Einstein Hospital in Brazil from 5,644 patients. Focussing on using simple blood exams, a sample of 599 subjects that had the fewest missing values for 16 common exams were selected. From these 599 patients, only 81 were positive for SARS-CoV-2 (determined by RT-PCR). Based on this data, we built an artificial intelligence classification framework, ER-CoV, aiming at determining which patients were more likely to be negative for SARS-CoV-2 when visiting an ER and that were categorized as a suspect case by medical professionals. The primary goal of this investigation is to develop a classifier with high specificity and high negative predictive values, with reasonable sensitivity. Findings: We identified that our framework achieved an average specificity of 92.16% [95% CI 91.73 - 92.59] and negative predictive value (NPV) of 95.29% [95% CI 94.65% - 95.90%]. Those values are completely aligned with our goal of providing an effective low-cost system to triage suspected patients at ERs. As for sensitivity, our model achieved an average of 63.98% [95% CI 59.82% - 67.50%] and positive predictive value (PPV) of 48.00% [95% CI 44.88% - 51.56%]. An error analysis identified that, on average, 45% of the false negative results would have been hospitalized anyway, thus the model is making mistakes for severe cases that would not be overlooked, partially mitigating the fact that the test is not high-sensitive. All code for our AI model, called ER-CoV is publicly available at https://github.com/soares-f/ER-CoV. Interpretation: Based on the capacity of our model to accurately predict which cases are negative from suspected patients arriving at emergency rooms, we envision that this framework can play an important role in patient triage. Probably the most important outcome is related to testing availability, which at this point is extremely low in many countries. Considering the achieved specificity, we would reduce by at least 90% the number of SARS-CoV-2 tests performed at emergency rooms, with the chance of getting a false negative at around 5%. The second important outcome is related to patient management in hospitals. Patients predicted as positive by our framework could be immediately separated from the other patients while waiting for the results of confirmatory tests. This could reduce the spread rate inside hospitals since in many hospitals all suspected cases are kept in the same ward. In Brazil, where the data was collected, rate infection is starting to quickly spread, the lead time of a SARS-CoV-2 can be up to 2 weeks. Funding: University of Sheffield provided financial support for the Ph.D scholarship for Felipe Soares.

Background and Objective Medical machine learning (ML) models tend to perform better on data from the same cohort than on new data, often due to overfitting, or co-variate shifts. For these reasons, external validation (EV) is a necessary practice in the evaluation of medical ML. However, there is still a gap in the literature on how to interpret EV results and hence assess the robustness of ML models. Methods We fill this gap by proposing a meta-validation method, to assess the soundness of EV procedures. In doing so, we complement the usual way to assess EV with an assessment in terms of the dataset cardinality, as well as with a novel method that considers the similarity of the EV dataset with respect to the training set. We then investigate how the notions of cardinality and similarity can be used to inform on the reliability of a validation procedure, by integrating them into two summative data visualizations. Results We illustrate our methodology by applying it to the validation of a state-of-the-art COVID-19 diagnostic model on 8 EV sets, collected across 3 different continents. The model performance was moderately impacted by data similarity (Pearson ρ = .38, p < .001). In the EV, the validated model reported good AUC (average: .84), acceptable calibration (average: .17) and utility (average: .50). The validation datasets were adequate in terms of dataset cardinality and similarity, thus suggesting the soundness of the results. We also provide

The author list is alphabetical and does not reflect the respective author contributions. The task was coordinated by Mariana Neves. sions were judged to be better than the reference translations, for instance, for de/en, en/es and es/en. 10 https://github.com/glample/fastBPE 11 1 encoder layer, 1 decoder layer, both with with GRU cells, embedding dimension of 512, hidden state of dimension 1024, using layer normalization, implemented using Marian NMT and trained using the Adam optimizer.