Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis.

Background and objectives: We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19. Methods: A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test. Results: Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04. Conclusion: ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality.

ARMIA: A Sensorized Arm Wearable for Motor Rehabilitation.

In this paper, we present ARMIA: a sensorized arm wearable that includes a combination of inertial and sEMG sensors to interact with serious games in telerehabilitation setups. This device reduces the cost of robotic assistance technologies to be affordable for end-users at home and at rehabilitation centers. Hardware and acquisition software specifications are described together with potential applications of ARMIA in real-life rehabilitation scenarios. A detailed comparison with similar medical technologies is provided, with a specific focus on wearable devices and virtual and augmented reality approaches. The potential advantages of the proposed device are also described showing that ARMIA could provide similar, if not better, the effectivity of physical therapy as well as giving the possibility of home-based rehabilitation.

Heterologous gam-COVID-vac (sputnik V)/mRNA-1273 (moderna) vaccination induces a stronger humoral response than homologous sputnik V in a real-world data analysis.

OBJECTIVES: To compare the homologous prime-boost vaccination scheme of Gam-COVID-Vac (Sputnik V (SpV)) to its heterologous combination with mRNA-1273 (Moderna (Mod)) vaccine. METHODS: SARS-CoV-2 anti-spike (S)-receptor binding domain (RBD) IgG concentration was assessed three to seven weeks after complete vaccination. Reactogenicity was evaluated by declared side events and medical assistance required until day 7 post boost. RESULTS: Of 190 participants enrolled, 105 received homologous SpV/SpV and the remaining heterologous SpV/Mod vaccination scheme, respectively. Median (interquartile range (IQR)) age was 54 (37-63) years, 132 out of 190 (69.5%) were female, and 46 out of 190 (24.2%) individuals had a prior confirmed COVID-19. Anti-S-RBD IgG median (IQR) titers were significantly higher for SpV/Mod (2511 (1476-3992) binding antibody units (BAU)/mL) than for SpV/SpV (582 (209-1609) BAU/mL; p < 0.001] vaccination scheme. In a linear model adjusted for age, gender, time to the serological assay, and time between doses, SpV/Mod (4.154 (6.585-615.554); p < 0.001] and prior COVID (3.732 (8.641-202.010); p < 0.001) were independently associated with higher anti-S-RBD IgG values. A higher frequency of mild and moderate adverse effects was associated with the heterologous scheme (20 of 85 (23.5%) vs. 13 of 105 (12.4%); p = 0.043 and 27 of 85 (31.8%) vs. 14 of 105 (13.3%); p = 0.002), respectively, although it was well tolerated by all individuals and no medical assistance was required. DISCUSSION: The heterologous SpV/Mod combination against SARS-CoV-2 is well tolerated and significantly increases humoral immune response as compared to the homologous SpV/SpV immunization.

Elderly Patient with DM2 during the COVID-Pandemia: Forgotten but Good Glycemic Control

COVIDpandemic has changed clinical practice in Primary and Hospital Care. Chronic Diseases Care have been relegated after COVID19, emergencies and severe pathologies (cancer) Elderly patients with DM2 had less access to medical services due to COVIDprotocols. HbA1c is important to manage patients with DM2. Between COVIDwaves and specially in 2021 there was a program to recover Diabetic Care in our 250000 inhabitants Health Department, specially most frail elderly patients with DM2 Aim: Find out if Elderly patients with DM2 chronic care was reduced during the COVIDpandemic period and if that affected their diabetic control. Methodology: HbA1c tests taken to patients with DM2 and > 70 year between January 20 and November 2021 were obtained as they are all done in our hospital either from Primary or Hospital Care. Data was calculated by Mean Monthly Number HbA1c tests (MMNHT) during prepandemic: March 20to February 2020, pandemic: March 2020 to February 2021 and postpandemic: March 2021 to November 2021. Diabetic control was calculated in three groups: % HbA1c <7%;7-8% and > 8% of all tests done in those three periods. Results: MMNHT had increased by 5% annually and a trend to better diabetic control was seen since 20until 2019. MMNHT were prepandemic vs. pandemic: 1313 vs. 10 (- 22.5%) and pandemic vs. postpandemic: 10vs. 1334 (+ 31%) % . Those changes were similar in Primary and Hospital Care.%HbA1c 7;7-8 and > 8% was in prepandemic: 50, 31 and 19%;pandemic: 50, 30 and 20% and postpandemic: 67, 28 and 11%. Summary: During COVIDpandemic, Elderly patients with DM2 had less chronic medical care as had a 23% reduction of MMNHT although it did recover posteriorly. Protocols to identify and recover diabetic chronic care in patients > 70 year might be helpful in our experience. Diabetic control was similar before, during and after the COVIDpandemic period: March 2020 to February 2021.

Antenatal screening of depressive and manic symptoms in south Brazilian childbearing women: A transversal study in advance of the pandemic scenario.

BACKGROUND: The diagnosis of mood disorders (MD) during pregnancy is challenging and may bring negative consequences to the maternal-fetal binomial. The long waitlist for specialized psychiatric evaluation in Brazil contributes to the treatment omission. Almost 20.0% of women treated with antidepressants have a positive screening for bipolar disorder. Therefore, it has been recommended the investigation of depressive and bipolar disorder during prenatal care. Unfortunately, the screening for mood disorders is not a reality in Brazil and many childbearing women remain undiagnosed. The objective of this study is to observe the frequency of MD and the effectiveness of screening scales for routine use by health professionals during prenatal care in high-risk pregnancies. METHODOLOGY/PRINCIPAL FINDINGS: This cross-sectional study included 61 childbearing women in their second trimester who were interviewed using the Edinburgh Postnatal Depression Scale (EPDS) and the Mood Disorder Questionnaire (MDQ). The cut-off point was EPDS ≥ 13 and MDQ ≥ 7 and the SCID-5 was the gold standard diagnosis. MD were diagnosed in 24.6% of the high-risk pregnancies. EDPS was positive in 19.7% and the frequency of major depression was 8.2%. 16.4% of the childbearing women were diagnosed with bipolar disorder, while MDQ was positive in 36.1%. 11.5% of the women had EPDS and MDQ positive. EPDS sensitivity was 80.0% and specificity 92.1%, whereas MDQ presented a sensitivity of 70.0% and specificity of 70.6%. CONCLUSION/SIGNIFICANCE: There is a high prevalence of MD in high-risk pregnancies. The routine use of EPDS simultaneously to MDQ during antenatal care is effective and plays an important role in early diagnosis, counselling, and promotion of perinatal mental health.

[Translated article] Spanish Asthma Management Guidelines (GEMA) v.5.1. Highlights and Controversies.

In this fifth phase of development, the contents of the Spanish Asthma Management Guidelines (GEMA), which include versions 5.0 and 5.1, have undergone a thorough review. The aim here is to set the main changes in context. These could be summarized as follows: DIAGNOSIS: new FENO cut-off and severity classification based on treatment needed to maintain control; INTERMITTENT ASTHMA: a more restrictive concept and treatment extended to include a glucocorticoid/adrenergic combination as needed; MILD ASTHMA: glucocorticoid/adrenergic therapy as needed as an alternative in case of low therapeutic adherence to conventional fixed-dose steroids; SEVERE ASTHMA: readjustment of phenotypes, incorporation of triple therapy in a single inhaler, and criteria for selection of a biologic in severe uncontrolled asthma; OTHERS: specific scoring in childhood asthma, incorporation of certain organizational aspects (care circuits, asthma units, telemedicine), new sections on COVID-19 and nasal polyposis.

Spanish Asthma Management Guidelines (GEMA) v.5.1. Highlights and Controversies

In this fifth phase of development, the contents of the Spanish Asthma Management Guidelines (GEMA), which include versions 5.0 and 5.1, have undergone a thorough review. The aim here is to set the main changes in context. These could be summarized as follows: DIAGNOSIS: new FENO cut-off and severity classification based on treatment needed to maintain control;INTERMITTENT ASTHMA: a more restrictive concept and treatment extended to include a glucocorticoid/adrenergic combination as needed;MILD ASTHMA: glucocorticoid/adrenergic therapy as needed as an alternative in case of low therapeutic adherence to conventional fixed-dose steroids;SEVERE ASTHMA: readjustment of phenotypes, incorporation of triple therapy in a single inhaler, and criteria for selection of a biologic in severe uncontrolled asthma;OTHERS: specific scoring in childhood asthma, incorporation of certain organizational aspects (care circuits, asthma units, telemedicine), new sections on COVID-19 and nasal polyposis. Resumen La quinta fase de la Guía Española para el Manejo del Asma (GEMA) que incluye las versiones 5.0 y 5.1, ha efectuado una profunda revisión de su contenido. El presente texto tiene como objetivo contextualizar los principales cambios. Estos se podrían resumir en: DIAGNÓSTICO: nuevo punto de corte de FENO y clasificación de gravedad basada en el tratamiento necesario para mantener el control;ASMA INTERMITENTE: concepto más restrictivo y tratamiento ampliado a combinación de glucocorticoide/adrenérgico a demanda;ASMA LEVE: tratamiento con glucocorticoide/adrenérgico a demanda como alternativa si baja adhesión terapéutica a esteroide fijo clásico;ASMA GRAVE: reajuste de los fenotipos, incorporación de la triple terapia en un solo inhalador y criterios para la selección del fármaco biológico en asma grave no controlada;OTROS: puntualizaciones concretas en asma infantil, incorporación de determinados aspectos organizativos (flujos entre niveles asistenciales, unidades de asma, telemedicina), nuevas secciones de COVID-19 y de poliposis nasal.

Match running performance in Brazilian professional soccer players: comparisons between successful and unsuccessful teams.

BACKGROUND: This study aimed to compare the match running performance between bottom- and top-ranked teams in professional soccer players over the 2020 season of the Brazilian National 2nd Division League. In addition, this study verified the independent and interactive effects of playing position and contextual factors on running outputs between these teams. METHODS: Forty-eight professional male outfield soccer players participated in this study (top-ranked team, n = 24; bottom-ranked team, n = 24). The distance- and accelerometry-based measures were recorded during 69 matches using a global positioning system (10 Hz) integrated with an accelerometer (400 Hz). RESULTS: The top-ranked team covered greater total distance [median (interquartile range); 10,330.0 m (1430.0)] and high-acceleration [97.0 m (32.0)] than the bottom-ranked team, in home and away matches [p < 0.05, effect size (ES) = small]. The midfielders of the top-ranked team covered higher total distance, high-speed running (> 18 km h-1), high acceleration (≥ 3 m s-2), high-deceleration (≤ -3 m s-2), and performed more sprints [(> 25 km h-1) compared to midfielders of the bottom-ranked team (p < 0.05, η2 = small-moderate]. The matches against top-level opponents required high values of high-acceleration and number of sprints only for the top-ranked team (p < 0.05, ES = small). Independent analysis showed that match outcome (loss vs. draw vs. win) was not influenced by running performance for both bottom- and top-ranked teams (p > 0.05; η2 = small). However, the top-ranked team covered greater total distance, high-acceleration/deceleration than bottom-ranked team in loss matches (p < 0.05, η2 = small). CONCLUSIONS: These findings should be considered when the coaches and practitioners interpret the match running outputs and when evaluating the effects of training intervention on these performance indicators.

Prolonged SARS-CoV-2 RNA virus shedding and lymphopenia are hallmarks of COVID-19 in cancer patients with poor prognosis.

Patients with cancer are at higher risk of severe coronavirus infectious disease 2019 (COVID-19), but the mechanisms underlying virus-host interactions during cancer therapies remain elusive. When comparing nasopharyngeal swabs from cancer and noncancer patients for RT-qPCR cycle thresholds measuring acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 1063 patients (58% with cancer), we found that malignant disease favors the magnitude and duration of viral RNA shedding concomitant with prolonged serum elevations of type 1 IFN that anticorrelated with anti-RBD IgG antibodies. Cancer patients with a prolonged SARS-CoV-2 RNA detection exhibited the typical immunopathology of severe COVID-19 at the early phase of infection including circulation of immature neutrophils, depletion of nonconventional monocytes, and a general lymphopenia that, however, was accompanied by a rise in plasmablasts, activated follicular T-helper cells, and non-naive Granzyme B+FasL+, EomeshighTCF-1high, PD-1+CD8+ Tc1 cells. Virus-induced lymphopenia worsened cancer-associated lymphocyte loss, and low lymphocyte counts correlated with chronic SARS-CoV-2 RNA shedding, COVID-19 severity, and a higher risk of cancer-related death in the first and second surge of the pandemic. Lymphocyte loss correlated with significant changes in metabolites from the polyamine and biliary salt pathways as well as increased blood DNA from Enterobacteriaceae and Micrococcaceae gut family members in long-term viral carriers. We surmise that cancer therapies may exacerbate the paradoxical association between lymphopenia and COVID-19-related immunopathology, and that the prevention of COVID-19-induced lymphocyte loss may reduce cancer-associated death.

Spanish Asthma Management Guidelines (GEMA) VERSION 5.1. Highlights and Controversies. / Guía española para el manejo del asma (GEMA) versión 5.1. Aspectos destacados y controversias.

In this fifth phase of development, the contents of the Spanish Asthma Management Guidelines (GEMA), which include versions 5.0 and 5.1, have undergone a thorough review. The aim here is to set the main changes in context. These could be summarized as follows: DIAGNOSIS: new FENO cut-off and severity classification based on treatment needed to maintain control; INTERMITTENT ASTHMA: a more restrictive concept and treatment extended to include a glucocorticoid/adrenergic combination as needed; MILD ASTHMA: glucocorticoid/adrenergic therapy as needed as an alternative in case of low therapeutic adherence to conventional fixed-dose steroids; SEVERE ASTHMA: readjustment of phenotypes, incorporation of triple therapy in a single inhaler, and criteria for selection of a biologic in severe uncontrolled asthma; OTHERS: specific scoring in childhood asthma, incorporation of certain organizational aspects (care circuits, asthma units, telemedicine), new sections on COVID-19 and nasal polyposis.

Evolutionary analysis of SARS-CoV-2 spike protein for its different clades.

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become the main target for antiviral and vaccine development. Despite its relevance, e information is scarse about its evolutionary traces. The aim of this study was to investigate the diversification patterns of the spike for each clade of SARS-CoV-2 through different approaches. Two thousand and one hundred sequences representing the seven clades of the SARS-CoV-2 were included. Patterns of genetic diversifications and nucleotide evolutionary rate were estimated for the spike genomic region. The haplotype networks showed a star shape, where multiple haplotypes with few nucleotide differences diverge from a common ancestor. Four hundred seventy-nine different haplotypes were defined in the seven analyzed clades. The main haplotype, named Hap-1, was the most frequent for clades G (54%), GH (54%), and GR (56%) and a different haplotype (named Hap-252) was the most important for clades L (63.3%), O (39.7%), S (51.7%), and V (70%). The evolutionary rate for the spike protein was estimated as 1.08 × 10-3 nucleotide substitutions/site/year. Moreover, the nucleotide evolutionary rate after nine months of the pandemic was similar for each clade. In conclusion, the present evolutionary analysis is relevant as the spike protein of SARS-CoV-2 is the target for most therapeutic candidates; besides, changes in this protein could have consequences on viral transmission, response to antivirals and efficacy of vaccines. Moreover, the evolutionary characterization of clades improves knowledge of SARS-CoV-2 and deserves to be assessed in more detail as re-infection by different phylogenetic clades has been reported.

Phylogenetic analysis of SARS-CoV-2 in the first few months since its emergence.

During the first few months of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution in a new host, contrasting hypotheses have been proposed about the way the virus has evolved and diversified worldwide. The aim of this study was to perform a comprehensive evolutionary analysis to describe the human outbreak and the evolutionary rate of different genomic regions of SARS-CoV-2. The molecular evolution in nine genomic regions of SARS-CoV-2 was analyzed using three different approaches: phylogenetic signal assessment, emergence of amino acid substitutions, and Bayesian evolutionary rate estimation in eight successive fortnights since the virus emergence. All observed phylogenetic signals were very low and tree topologies were in agreement with those signals. However, after 4 months of evolution, it was possible to identify regions revealing an incipient viral lineage formation, despite the low phylogenetic signal since fortnight 3. Finally, the SARS-CoV-2 evolutionary rate for regions nsp3 and S, the ones presenting greater variability, was estimated as 1.37 × 10-3 and 2.19 × 10-3 substitution/site/year, respectively. In conclusion, results from this study about the variable diversity of crucial viral regions and determination of the evolutionary rate are consequently decisive to understand essential features of viral emergence. In turn, findings may allow the first-time characterization of the evolutionary rate of S protein, crucial for vaccine development.

Improving indoor air quality through an air purifier able to reduce aerosol particulate matter (PM) and volatile organic compounds (VOCs): Experimental results.

The adverse effects of fine particulate matter (PM) and many volatile organic compounds (VOCs) on human health are well known. Fine particles are, in fact, those most capable of penetrating in depth into the respiratory system. People spend most of their time indoors where concentrations of some pollutants are sometimes higher than outdoors. Therefore, there is the need to ensure a healthy indoor environment and for this purpose the use of an air purifier can be a valuable aid especially now since it was demonstrated that indoor air quality has a high impact on spreading of viral infections such as that due to SARS-COVID19. In this study, we tested a commercial system that can be used as an air purifier. In particular it was verified its efficiency in reducing concentrations of PM10 (particles with aerodynamic diameter less than 10 µm), PM2.5 (particles with aerodynamic diameter less than 2.5 µm), PM1 (particles with aerodynamic diameter less than 1 µm), and particles number in the range 0.3 µm-10 µm. Furthermore, its capacity in reducing VOCs concentration was also checked. PM measurements were carried out by means of a portable optical particle counter (OPC) instrument simulating the working conditions typical of a household environment. In particular we showed that the tested air purifier significantly reduced both PM10 and PM2.5 by 16.8 and 7.25 times respectively that corresponds to a reduction of about 90% and 80%. A clear reduction of VOCs concentrations was also observed since a decrease of over 50% of these gaseous substances was achieved.

Cancer surgery during the COVID-19 pandemic: The experience of a comprehensive cancer center performing preoperative screening by RT-PCR and chest CT scan.

BACKGROUND AND OBJECTIVES: During the worldwide pandemic of coronavirus disease 2019 (COVID-19), oncological procedures considered to be urgent could not be delayed, and a specific procedure was required to continue surgical activity. The objective was to assess the efficacy of our preoperative screening algorithm. METHODS: This observational retrospective study was performed between the 25th of March and the 12th of May 2020 in a comprehensive cancer center in France. Patients undergoing elective oncologic surgery were tested by preoperative nasopharyngeal reverse-transcription polymerase chain reaction (RT-PCR) that could be associated with a chest computerized tomography (CT) scan. RESULTS: Of the 510 screening tests (in 477 patients), only 5% (15/477) were positive for COVID-19 in 24 patients (18 RT-PCR+ and 7 CT scan+/RT-PCR-). Four patients were ultimately false positives based on the CT scan. In total, only 4.2% (20/477) of the patients were COVID-19+. The positivity rate decreased with time after the containment measures were implemented (from 7.4% to 0.8%). In the COVID-19+ group, 20% of the patients had postoperative pulmonary complications, whereas this was the case for 5% of the patients in the COVID-19 group. CONCLUSIONS: Maintaining secure surgical activity is achievable and paramount in oncology care, even during the COVID-19 pandemic, with appropriate screening based on preoperative RT-PCR.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world"s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country"s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.