Seguimiento de niños con diagnóstico de asma grave antes y durante la pandemia por COVID-19 / Follow-up of children diagnosed with severe asthma before and during the COVID-19 pandemic

En la pandemia por COVID-19 se exploraron estrategias de atención para garantizar el seguimiento de niños con asma grave. Estudio prospectivo, observacional, comparativo. Se incluyeron pacientes del programa de asma grave de un hospital pediátrico de tercer nivel (n 74). Se evaluó el grado de control, exacerbaciones y hospitalizaciones durante un período presencial (PP), marzo 2019-2020, y uno virtual (PV), abril 2020-2021. En el PP, se incluyeron 74 pacientes vs. 68 (92 %) del PV. En el PP, el 68 % (46) de los pacientes presentaron exacerbaciones vs. el 46 % (31) de los pacientes en el PV (p 0,003). En el PP, se registraron 135 exacerbaciones totales vs. 79 en el PV (p 0,001); hubo una reducción del 41 %. En el PP, el 47 % (32) de los pacientes tuvieron exacerbaciones graves vs. el 32 % (22) de los pacientes en el PV (p 0,048). Hubo 91 exacerbaciones graves en el PP vs. 49 en el PV (p 0,029), reducción del 46 %. No hubo diferencias en las hospitalizaciones (PP 10, PV 6; p 0,9). La telemedicina fue efectiva para el seguimiento de pacientes con asma grave

During the COVID-19 pandemic, health care strategies were explored to ensure the follow-up of children with severe asthma. This was a prospective, observational, and comparative study. Patients in the severe asthma program of a tertiary care children's hospital were included (n: 74). The extent of control, exacerbations, and hospitalizations during an in-person period (IPP) (March 2019­2020) and an online period (OP) (April 2020­2021) was assessed. A total of 74 patients were enrolled in the IPP compared to 68 (92%) in the OP. During the IPP, 68% (46) of patients had exacerbations versus 46% (31) during the OP (p = 0.003). During the IPP, 135 total exacerbations were recorded compared to 79 during the OP (p = 0.001); this accounted for a 41% reduction. During the IPP, 47% (32) of patients had severe exacerbations versus 32% (22) during the OP (p = 0.048). A total of 91 severe exacerbations were recorded during the IPP compared to 49 during the OP (p = 0.029); the reduction was 46%. No differences were observed in terms of hospitalization (IPP: 10, OP: 6; p = 0,9). Telemedicine was effective for the follow-up of patients with severe asthma.

Predictive factors of mortality in patients admitted to the emergency department for SARS-Cov2 pneumonia.

INTRODUCTION: The overcrowding of intensive care units during the corona virus pandemic increased the number of patients managed in the emergency department (ED). The detection timely of the predictive factors of mortality and bad outcomes improve the triage of those patients. AIM: To define the predictive factors of mortality at 30 days among patients admitted on ED for covid-19 pneumonia. METHODS: This was a prospective, monocentric, observational study for 6 months. Patients over the age of 16 years admitted on the ED for hypoxemic pneumonia due to confirmed SARS-COV 2 infection by real-time reverse-transcription polymerase chain reaction (rRT-PCR) were included. Multivariate logistic regression was performed to investigate the predictive factors of mortality at 30 days. RESULTS: 463 patients were included. Mean age was 65±14 years, Sex-ratio=1.1. Main comorbidities were hypertension (49%) and diabetes (38%). Mortality rate was 33%. Patients who died were older (70±13 vs. 61±14;p<0.001), and had more comorbidities: hypertension (57% vs. 43%, p=0.018), chronic heart failure (8% vs. 3%, p=0.017), and coronary artery disease (12% vs. 6%, p=0.030). By multivariable analysis, factors independently associated with 30-day mortality were age ≥65 years aOR: 6.9, 95%CI 1.09-44.01;p=0.04) SpO2<80% (aOR: 26.6, 95%CI 3.5-197.53;p=0.001) and percentage of lung changes on CT scan>70% (aOR: 5.6% 95%CI .01-31.29;p=0.04). CONCLUSION: Mortality rate was high among patients admitted in the ED for covid-19 pneumonia. The identification of predictive factors of mortality would allow better patient management.

Predicting COVID-19 outcomes with the Edmonton Obesity Staging System.

BACKGROUND: Multiple studies have demonstrated a correlation between a high body mass index and discriminatory COVID-19 outcomes. Studies appear to indicate that there is a correlation between obesity-related comorbidities and less favorable outcomes. OBJECTIVES: The primary aim of the current investigation is to conduct a thorough assessment of the correlation between BMI and comorbidities associated with obesity, and their potential impact on the severity and consequences of COVID-19 infection among patients receiving care in a tertiary healthcare setting. DESIGN: Retrospective cohort. SETTINGS: Tertiary rehabilitation center, Riyadh, Saudi Arabia. PATIENTS AND METHODS: The study included all individuals who received medical treatment and tested positive for COVID-19 by means of RT-PCR during the period from March to September 2020. COVID-19 patients were classified using Edmonton Obesity Staging System (EOSS). MAIN OUTCOME MEASURES: COVID-19-related complications, including pneumonia and cytokine release syndrome, as well as the time length to COVID-19 negativization. SAMPLE SIZE: 315 patients. RESULTS: The median (25th-75th percentiles) age of the patients was 38 (31.5-49) years old. Males outnumbered females, and 66% of patients were non-Saudis. Forty-eight patients (15.2%) had obesity class I, whereas 13 patients (4.1%) had class II. Thirty-two patients (10.2%) were classified as EOSS stage 1, 105 patients (33.3%) were classified as EOSS stage 2, and 25 patients (7.9%) were assigned to EOSS stage 3. Males predominated in EOSS stages 1 and 2, whereas females predominated in stage 3. In EOSS stage 3, 52% of cases had moderate severity and 48% had severe illness. CONCLUSIONS: EOSS distinguishes the COVID-19 risks of poor outcomes beyond BMI. Patients who were overweight or obese but remained in the stage 1 of the EOSS had a lower risk of a poor COVID-19 outome than normal-weight patients. The health status of obese patients is a more precise indicator of the progression of COVID-19 during hospitalization than BMI alone. LIMITATIONS: Given the limited capacity of urgent care facilities to conduct a comprehensive evaluation of comorbidities and other relevant outcomes in all patients, it is plausible that certain patients may have been erroneously classified with an EOSS stage 2 diagnosis, when in fact they ought to have been assigned a stage 3 diagnosis.

Longitudinal course and predictors of health-related quality of life, mental health, and fatigue, in non-hospitalized individuals with or without post COVID-19 syndrome.

BACKGROUND: Long-term information on health-related quality of life (HRQOL) and mental health of non-hospitalized individuals with "post COVID-19 syndrome" (PCS) is scarce. Thus, the objectives of the present study were to compare HRQOL and mental health of individuals with and without PCS in a German sample of non-hospitalized persons after SARS-CoV-2 infection, to characterize the long-term course up to 2 years and to identify predictors for post COVID-19 impairments. METHODS: Individuals with past SARS-CoV-2 infection were examined at the University Hospital of Augsburg from November 2020 to May 2021 and completed a postal questionnaire between June and November 2022. Participants who self-reported the presence of fatigue, dyspnea on exertion, memory problems or concentration problems were classified as having PCS. HRQOL was assessed using the Veterans RAND 12-Item Health Survey, mental health was measured by the Patient Health Questionnaire and the Fatigue Asessment Scale was used to assess fatigue severity. Multivariable linear regression models with inverse probability weighting were used to determine the association between PCS and health outcomes. RESULTS: From the 304 participants (58.2% women, median age 52 years), 210 (69.1%) were classified as having PCS in median 26 months after SARS-CoV-2 infection. Persons with PCS showed significantly more often depressive and anxiety disorders. PCS was independently and significantly associated with higher levels of depression, post-traumatic stress and fatigue, as well as poorer physical and mental HRQOL in median 9 months as well as 26 months after SARS-CoV-2 infection. A large number of acute symptoms and a prior diagnosis of depression were independently associated with poor mental health and HRQOL. While post-traumatic stress and mental HRQOL improved from 9 months to 26 months post infection onset, depressiveness, fatigue and physical HRQOL remained stable in both, persons with and without PCS. CONCLUSIONS: PCS in non-hospitalized persons after SARS-CoV-2 infection is often associated with long-term impairments of mental health and HRQOL outcomes.

Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study.

BACKGROUND: COVID-19 and bacterial/fungal coinfections have posed significant challenges to human health. However, there is a lack of good tools for predicting coinfection risk to aid clinical work. OBJECTIVE: We aimed to investigate the risk factors for bacterial/fungal coinfection among COVID-19 patients and to develop machine learning models to estimate the risk of coinfection. METHODS: In this retrospective cohort study, we enrolled adult inpatients confirmed with COVID-19 in a tertiary hospital between January 1 and July 31, 2023, in China and collected baseline information at admission. All the data were randomly divided into a training set and a testing set at a ratio of 7:3. We developed the generalized linear and random forest models for coinfections in the training set and assessed the performance of the models in the testing set. Decision curve analysis was performed to evaluate the clinical applicability. RESULTS: A total of 1244 patients were included in the training cohort with 62 healthcare-associated bacterial/fungal infections, while 534 were included in the testing cohort with 22 infections. We found that patients with comorbidities (diabetes, neurological disease) were at greater risk for coinfections than were those without comorbidities (OR = 2.78, 95%CI = 1.61-4.86; OR = 1.93, 95%CI = 1.11-3.35). An indwelling central venous catheter or urinary catheter was also associated with an increased risk (OR = 2.53, 95%CI = 1.39-4.64; OR = 2.28, 95%CI = 1.24-4.27) of coinfections. Patients with PCT > 0.5 ng/ml were 2.03 times (95%CI = 1.41-3.82) more likely to be infected. Interestingly, the risk of coinfection was also greater in patients with an IL-6 concentration < 10 pg/ml (OR = 1.69, 95%CI = 0.97-2.94). Patients with low baseline creatinine levels had a decreased risk of bacterial/fungal coinfections(OR = 0.40, 95%CI = 0.22-0.71). The generalized linear and random forest models demonstrated favorable receiver operating characteristic curves (ROC = 0.87, 95%CI = 0.80-0.94; ROC = 0.88, 95%CI = 0.82-0.93) with high accuracy, sensitivity and specificity of 0.86vs0.75, 0.82vs0.86, 0.87vs0.74, respectively. The corresponding calibration evaluation P statistics were 0.883 and 0.769. CONCLUSIONS: Our machine learning models achieved strong predictive ability and may be effective clinical decision-support tools for identifying COVID-19 patients at risk for bacterial/fungal coinfection and guiding antibiotic administration. The levels of cytokines, such as IL-6, may affect the status of bacterial/fungal coinfection.

[Follow-up outpatient clinics for patients with post COVID: a survey to assess and characterize aspects of care]. / Nachsorgeambulanzen für Patient*innen mit Post-COVID: eine Befragung zur Bestandsaufnahme und Charakterisierung von Versorgungsaspekten.

After acute infection with the SARS-CoV-2 virus, up to 10 % of affected individuals suffer from long-term health impairments, also referred to as "Post-COVID". In Germany, specialized outpatient clinics have been established to care for patients with Post-COVID. A structured survey of the care situation is not yet available, but essential for a demand-oriented care. The present study aimed to systematically assess and describe structural and process-related aspects of care, and to perform an inventory and needs analysis of Post-COVID outpatient clinics in Germany.An online survey was developed assessing the structure and organization of the outpatient clinics, service offerings and networking of care from the perspective of the outpatient clinic directors. A total of 95 outpatient clinics were identified, and an invitation to participate in the online survey was sent via e-mail to the directors of the outpatient clinics. Data were collected between February and May 2022. Descriptive data analysis was performed.A total of 28 outpatient clinic managers (29 %) took part in the survey. Participants were between 32 and 66 years old, and 61 % (n = 17) were male. The outpatient clinics were most frequently affiliated with the specialties of pneumology (n = 10; 36 %), internal medicine, psychiatric and psychosomatic medicine, and neurology (n = 8; 29 %, respectively). Among the outpatient clinic directors, 64 % (n = 18) stated that the time spent waiting for an appointment was more than one month. Utilization (n = 25; 89 %), appointment demand (n = 26; 93 %), and the need for more Post-COVID outpatient clinics (n = 20; 71 %) were rated as high by the outpatient clinic directors. Nearly all directors reported networking with in-clinic facilities (n = 27; 96 %), with primary care physicians and with specialists in private practice (n = 21; 75 %, respectively).The main focus of care is pneumology. Internal medicine, psychiatry/psychosomatics and neurology are also equally represented. Our data further suggest a high demand for Post-COVID outpatient clinics and the need to expand this care offer.

Neutrophil-Lymphocyte Ratio as a Predictor of the Risk of Death in Severe Cases of COVID-19.

BACKGROUND: Identifying clinical characteristics and risk factors, comorbid conditions, and complications arising from SARS-CoV-2 infection is important to predict the progression to more severe forms of the disease among hospitalized individuals to enable timely intervention and to prevent fatal outcomes. The aim of the study is to assess the possible role of the neutrophil/lymphocyte ratio (NLR) as a biomarker of the risk of death in patients with comorbidities hospitalized with COVID-19 in a tertiary hospital in southern Brazil. METHODS: This is a prospective cohort study on patients with SARS-CoV-2 infection admitted to a hospital in the metropolitan region of Porto Alegre from September 2020 to March 2022. RESULTS: The sample consisted of 185 patients with associated comorbidities, namely, hypertension, diabetes mellitus, obesity, cardiovascular, pulmonary, and renal diseases, hospitalized with COVID-19. Of these, 78 died and 107 were discharged alive. The mean age was 66.5 years for the group that died and 60.1 years for the group discharged. Statistical analysis revealed that a difference greater than or equal to 1.55 in the NLR, from hospitalization to the 5th day, was associated with a relative risk of death greater than 2. CONCLUSIONS: Measuring a simple inflammatory marker such as NLR may improve the risk stratification of comorbid patients with COVID-19 and can be considered a useful biomarker.

Predictive Role of Platelet and Inflammation Markers for Severe COVID-19 by Propensity Score Matching.

BACKGROUND: This study aims to ascertain the predictive value of platelet and inflammation markers in severe cases of COVID-19. METHODS: A retrospective real-world cohort study was conducted using propensity score matching (PSM). Patients were classified into severe and non-severe COVID-19 groups based on the severity of the disease, and the correlation between severe COVID-19 and laboratory parameters at admission was analyzed. RESULTS: The study included 397 adult patients, comprising 212 (53%) males and 185 (47%) females. Among these, 309 were non-severe and 88 were severe cases. The severe group had a higher median age than the non-severe group (60 vs. 42 years, p < 0.001). Independent risk factors for severe COVID-19 included age, diabetes comorbidity, fever, respiratory symptoms, platelet count, high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and the ratio of arterial oxygen partial pressure (PaO2) to the fraction of inspired oxygen (FiO2) (P/F ratio). After one-to-one PSM, adjusted for age, diabetes comorbidities, fever, and respiratory symptoms, significant differences in laboratory parameters at admission were observed. Compared to the non-severe group (n = 71), in the severe group (n = 71), elevated levels of hsCRP (median: 27.1 mg/L vs. 14.6 mg/L, p = 0.005) and IL-6 (median: 16.2 pg/mL vs. 15.3 pg/mL, p = 0.005) were observed, while platelet count (164 ± 36 × 109 vs. 180 ± 50 × 109, p = 0.02) and P/F ratio (median: 351 vs. 397, p = 0.001) were reduced. CONCLUSIONS: Elevated levels of hsCRP and IL-6, along with reduced platelet count and P/F ratio at admission, were significantly associated with severe COVID-19 and may serve as predictive indicators.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

BACKGROUND: Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. OBJECTIVE: This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. METHODS: We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. RESULTS: Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. CONCLUSIONS: The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies.

BACKGROUND: Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. OBJECTIVE: This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. METHODS: A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. RESULTS: Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. CONCLUSION: The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Factors influencing Thai university students' decisions to take COVID-19 vaccine booster doses: a cross-sectional survey.

BACKGROUND: We aimed to describe the acceptance of COVID-19 vaccine booster doses and factors influencing this among Thai university students. METHODS: A cross-sectional survey was conducted between July and September 2022. All university students in Thailand were eligible to participate. We explored the acceptance rate of COVID-19 vaccine booster doses and regular vaccines (if available) among university students. Associations between factors influencing the acceptance of vaccination were analyzed by multiple logistic regression analysis. RESULTS: A total of 322 participants were surveyed (78.9% female, age 18 to 49 years (mean = 22.6, standard deviation = 5.47)). Most participants (85.7%) were undergraduate students (Bachelor level), and a proportion (84.8%) had a background in health sciences studies. The proportions who accepted booster doses and regular vaccines were 52.8% and 69.3%, respectively. Vaccine accessibility was found to be significantly associated with the acceptance of booster doses (adjusted odds ratio (AOR) = 2.77, 95% confidence interval (CI) = 1.10-6.97), while the availability of scientific evidence (AOR = 3.44, 95% CI = 1.21-9.77) was significantly associated with the acceptance of regular vaccines. CONCLUSIONS: This study contributes to addressing the knowledge gap regarding acceptance of COVID-19 vaccine booster doses among university students in Thailand. Our findings revealed that vaccine accessibility and the availability of scientific evidence, as well as vaccination costs, influenced individuals' decisions around accepting vaccine booster doses. Further research should focus on the dynamics of vaccine acceptance to facilitate the development of targeted strategies and support vaccination policymaking in Thailand.

Infection Risk Prediction in Healthcare Settings: Lessons from COVID-19 Contact Tracing.

Objective: Contact tracing aids epidemic control by enabling early detection and isolation without overburdening healthcare systems despite potential challenges. This study aimed to evaluate the practical application of contact and risk assessment-based models in predicting SARS-CoV-2 infection following exposure among healthcare workers in a large tertiary public university hospital in Türkiye. Materials and Methods: The study was designed as a retrospective cohort study, including contact tracing data from 3389 exposed healthcare workers from March 23, 2020, to October 22, 2021. Contact-based (mask use, contact duration and distance) and exposure risk-assessment-based (low, medium, high-risk) models with and without having symptoms were generated using logistic regression. SARS-CoV-2 infection was defined as having a positive SARS-CoV-2 RT-PCR test result. Adjustments were made to the models for demographic and occupational variables, previous infection, and vaccination. Model parameters were compared. Results: Of 3389 exposed healthcare workers, 2451 underwent RT-PCR testing. Among those tested, RT-PCR positivity was 5.9% (144/2451). Lack of personal protective equipment use (odds ratio [OR]=1.64, 95% confidence interval [CI]=1.03-2.66) and ≥15 minutes of contact duration (1.89, 1.21-3.09) were significantly associated with RT-PCR positivity. In the risk-assessment model, being a high-risk contact increased the odds of RT-PCR positivity (OR=2.76, 95% CI=1.61-5.03). Adding the presence of symptoms to contact-based and risk assessment models improved model parameters (Akaike information criterion [AIC]: from 1086.1 to 1083.1; Tjur's R2: from 0.016 to 0.019, respectively). Conclusion: The inclusion of being symptomatic improved the contact-based and risk assessment-based models. Institutions should be encouraged to incorporate symptom inquiries into risk assessment protocols in response to newly emerging respiratory virus epidemics. Institutions lacking the capacity for extensive contact tracing are recommended, at minimum, to track symptomatic exposed workers for epidemic control.

Is Metformin Use Associated with a More Favorable COVID-19 Course in People with Diabetes?

Background: Diabetes Mellitus (DM) has been associated with a higher Coronavirus disease-19 (COVID-19) mortality, both in hospitalized patients and in the general population. A possible beneficial effect of metformin on the prognosis of COVID-19 has been reported in some observational studies, whereas other studies disagree. Methods: To investigate the possible effect of metformin on COVID-19 in-hospital mortality, we performed a retrospective study that included all SARS-CoV-2-positive patients with DM who were admitted to two Italian hospitals. In order to adjust for possible confounders accounting for the observed reduction of mortality in metformin users, we adopted the COVID-19 Mortality Risk Score (COVID-19 MRS) as a covariate. Results: Out of the 524 included patients, 33.4% died. A binomial logistic regression showed that metformin use was associated with a significant reduction in case fatality (OR 0.67 [0.45-0.98], p = 0.039), with no significant effect on the need for ventilation (OR 0.75 [0.5-1.11], p = 0.146). After adjusting for COVID-19 MRS, metformin did not retain a significant association with in-hospital mortality [OR 0.795 (0.495-1.277), p = 0.342]. Conclusions: A beneficial effect of metformin on COVID-19 was not proven after adjusting for confounding factors. The use of validated tools to stratify the risk for COVID-19 severe disease and death, such as COVID-19 MRS, may be useful to better explore the potential association of medications and comorbidities with COVID-19 prognosis.

Effectiveness of Copeptin, MR-proADM and MR-proANP in Predicting Adverse Outcomes, Alone and in Combination with Traditional Severity Scores, a Secondary Analysis in COVID-19 Patients Requiring Intensive Care Admission.

Objective: To investigate whether copeptin, MR-proADM and MR-proANP, alone or integrated with the SOFA, MuLBSTA and SAPS II scores, are capable of early recognition of COVID-19 ICU patients at increased risk of adverse outcomes. Methods: For this predefined secondary analysis of a larger cohort previously described, all consecutive COVID-19 adult patients admitted between March and December 2020 to the ICU of a referral, university hospital in Northern Italy were screened, and clinical severity scores were calculated upon admission. A blood sample for copeptin, MR-proADM and MR-proANP was collected within 48 h (T1), on day 3 (T3) and 7 (T7). Outcomes considered were ICU and in-hospital mortality, bacterial superinfection, recourse to renal replacement therapy (RRT) or veno-venous extracorporeal membrane oxygenation, need for invasive mechanical ventilation (IMV) and pronation. Results: Sixty-eight patients were enrolled, and in-hospital mortality was 69.1%. ICU mortality was predicted by MR-proANP measured at T1 (HR 1.005, 95% CI 1.001-1.010, p = 0.049), although significance was lost if the analysis was adjusted for procalcitonin and steroid treatment (p = 0.056). Non-survivors showed higher MR-proADM levels than survivors at all time points, and an increase in the ratio between values at baseline and at T7 > 4.9% resulted in a more than four-fold greater risk of in-hospital mortality (HR 4.417, p < 0.001). Finally, when considering patients with any reduction in glomerular filtration, an early copeptin level > 23.4 pmol/L correlated with a more than five-fold higher risk of requiring RRT during hospitalization (HR 5.305, p = 0.044). Conclusion: Timely evaluation of MR-proADM, MR-proANP and copeptin, as well as changes in the former over time, might predict mortality and other adverse outcomes in ICU patients suffering from severe COVID-19.

Evaluating Mortality Predictors in COVID-19 Intensive Care Unit Patients: Insights into Age, Procalcitonin, Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, and Ferritin Lactate Index.

INTRODUCTION: Numerous studies suggest that alterations in blood parameters, such as changes in platelet, lymphocyte, hemoglobin, eosinophil, and basophil counts; increased neutrophil counts; and elevated neutrophil/lymphocyte and platelet/lymphocyte ratios, signal COVID-19 infection and predict worse outcomes. Leveraging these insights, our study seeks to create a predictive mortality model by assessing age and crucial laboratory markers. MATERIALS AND METHODS: Patients were categorized into two groups based on their hospital outcomes: 130 survivors who recovered from their Intensive Care Unit (ICU) stay (Group 1) and 74 who died (Group 2). We then developed a predictive mortality model using patients' age, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), procalcitonin levels, and ferritin lactate (FL) index results. RESULTS: A total of 204 patients were included. Patients in Group 2 had a notably higher mean age compared to those in Group 1 (76 ± 11 vs. 66 ± 15 years) (p < 0.001). Using specific cut-off values, our analysis revealed varying effectiveness in predicting COVID-19 mortality: Those aged over 73 years showed 74% sensitivity and 60% specificity, with an area under the curve (AUC) of 0.701. Procalcitonin levels above 0.35 ng/mL balanced true-positive and -negative identifications well, achieving an AUC of 0.752. The FL index, with a threshold of 1228 mg/dL, had 68% sensitivity and 65% specificity with an AUC of 0.707. A PLR higher than 212 resulted in 48% sensitivity and 69% specificity, with an AUC of 0.582. An NLR higher than 5.8 resulted in 55% sensitivity and 63% specificity, with an AUC of 0.640, showcasing diverse predictive accuracies across parameters. The statistical analysis evaluated the effects of age (>73), procalcitonin levels (>0.35), FL > 1228, PLR > 212, and NLR > 5.8 on mortality variables using logistic regression. Ages over 73 significantly increased event odds by 2.1 times (p = 0.05), procalcitonin levels above 0.35 nearly quintupled the odds (OR = 5.6, p < 0.001), high FL index levels more than tripled the odds (OR = 3.5, p = 0.003), a PLR > 212 significantly increased event odds by 3.5 (p = 0.030), and an NLR > 5.8 significantly increased event odds by 1.6 (p = 0.043). CONCLUSIONS: Our study highlights significant predictors of mortality in COVID-19 ICU patients, including advanced age, elevated procalcitonin, FL index levels, the PLR, and the NLR.

COVID-19 trends at the University of Tennessee: predictive insights from raw sewage SARS-CoV-2 detection and evaluation and PMMoV as an indicator for human waste.

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it's not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.

Electrophysiological properties and structural prediction of the SARS-CoV-2 viroprotein E.

COVID-19, the infectious disease caused by the most recently discovered coronavirus SARS- CoV-2, has caused millions of sick people and thousands of deaths all over the world. The viral positive-sense single-stranded RNA encodes 31 proteins among which the spike (S) is undoubtedly the best known. Recently, protein E has been reputed as a potential pharmacological target as well. It is essential for the assembly and release of the virions in the cell. Literature describes protein E as a voltage-dependent channel with preference towards monovalent cations whose intracellular expression, though, alters Ca2+ homeostasis and promotes the activation of the proinflammatory cascades. Due to the extremely high sequence identity of SARS-CoV-2 protein E (E-2) with the previously characterized E-1 (i.e., protein E from SARS-CoV) many data obtained for E-1 were simply adapted to the other. Recent solid state NMR structure revealed that the transmembrane domain (TMD) of E-2 self-assembles into a homo-pentamer, albeit the oligomeric status has not been validated with the full-length protein. Prompted by the lack of a common agreement on the proper structural and functional features of E-2, we investigated the specific mechanism/s of pore-gating and the detailed molecular structure of the most cryptic protein of SARS-CoV-2 by means of MD simulations of the E-2 structure and by expressing, refolding and analyzing the electrophysiological activity of the transmembrane moiety of the protein E-2, in its full length. Our results show a clear agreement between experimental and predictive studies and foresee a mechanism of activity based on Ca2+ affinity.

Positive school climate and strong school identification as protective factors of adolescent mental health and learning engagement: A longitudinal investigation before and during COVID-19.

The COVID-19 pandemic resulted in significant disruptions for children and youth around the world, especially given school closures and shifts in teaching modes (on-line and hybrid). However, the impact of these disruptions remains unclear given data limitations such as a reliance on cross-sectional and/or short-interval surveys as well as a lack of broad indicators of key outcomes of interest. The current research employs a quasi-experimental design by using an Australian four-year longitudinal survey with student responses from Grade 7 to 10 (aged 12-15 years old) (N = 8,735 from 20 schools) in one education jurisdiction. Responses are available pre-pandemic (2018 and 2019) and during the pandemic (2020 and 2021). Importantly the survey included measures of well-being, mental health and learning engagement as well as potential known school-environment factors that could buffer against adversity: school climate and school identification. The findings were generally in line with key hypotheses; 1) during COVID-19 students' learning engagement and well-being significantly declined and 2) students with more positive school climate or stronger school identification pre-COVID-19 fared better through the disruption of the pandemic. However, these same students suffered from a steeper decline in well-being and engagement which may be explained through the impact of losing meaningful social or group connections. This decline was evident after controlling for gender, academic grade (as a proxy of age), parental education, and socioeconomic status. It is concluded that investing in the social environment of schools is important in crisis preparedness and can facilitate better crisis response among youth.

Who develops long COVID? Longitudinal pre-pandemic predictors of long COVID and symptom clusters in a representative Dutch population.

OBJECTIVES: Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering. METHODS: 3022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged to 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group. RESULTS: Predictors of long COVID included Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC=0.80, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism. CONCLUSIONS: A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID presentation.

Correction: Glycated hemoglobin level dynamics in COVID-19 survivors: 12 months follow-up study after discharge from hospital.

[This corrects the article DOI: 10.1371/journal.pone.0275381.].