ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized

COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.

Objective

To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.

Methodology

Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression.

Results

Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were - 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289.

Conclusion

The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (https://pedrobrasil.shinyapps.io/INDWELL/). The clinical benefit and the performance in different scenarios are yet to be known.

Neurological manifestations of acute SARS-CoV-2 infection in a reference hospital in Bahia, Brazil

BACKGROUND

Neurologic manifestations of Coronavirus Disease-19 (COVID-19) have been associated with patients' disease severity and outcome. This study aimed to describe the frequency and characteristics of the neurological manifestations in a group of hospitalized individuals with COVID-19 and their associations with patient outcomes.

METHODS

Patients aged 18 years or older admitted to a local hospital between April and June 2020 with SARS-CoV-2 detected by RT-PCR were included in this retrospective observational study. The characteristics of participants were collected from electronic medical records using a structured questionnaire. A Poisson regression model was used to examine the influence of neurological manifestations on mortality.

RESULTS

A total of 305 participants with COVID-19 were included, with 57.7 % of them presenting neurological symptoms. There were 62 (20.3 %) individuals with acute encephalopathy, with a mean age of 65.5 ± 15.9 years. In this group, higher Prevalence Ratios (PR) of comorbidities (1.6) and severe disease (3.6) were present, predisposing factors for acute encephalopathy. They were also more likely to be admitted to the intensive care unit (3.1) and to die (2.4). The median Neutrophil-Lymphocyte Ratio (NLR) was 7 (Interquartile Range [IQR: 4‒12]). Fifty-two (17 %) participants presented chemosensory dysfunction, with a mean age 53.3 ± 14 years and a lower PR of comorbidity (0.8) than those without. The severe diseases' PR was slightly higher (1.1), but the PR of ICU admission (0.7), and deaths (0.4) was lower. The LNR was 3.8 (IQR: 2.2-7.8). Poisson regression analysis revealed that severe illness (PR = 3.13), cardiopathy (PR = 1.65), acute encephalopathy (PR = 1.49), diabetes (PR = 1.46), and neutrophil-lymphocyte ratio (PR = 1.04) were associated with death. Conversely, having chemosensory disorders (PR = 0.44) and a prolonged hospital stay (PR = 0.96) were associated with survival.

CONCLUSION

Patients with acute encephalopathy had more severe forms of COVID-19 and higher mortality. In contrast, chemosensory dysfunction was associated with milder disease manifestations and a better prognosis.

Prevalence of phenotypic drug resistance profiles and multi-drug-resistant Pseudomonas and Acinetobacter species recovered from clinical specimens in Ethiopia: a systematic review and meta-analysis

BACKGROUND

Antimicrobial-resistant Pseudomonas and Acinetobacter species are emerging as serious public health risks, both globally and in resource-limited countries such as Ethiopia. These microorganisms cause serious, life-threatening infections and are becoming increasingly resistant to commonly prescribed antibiotics. The high prevalence and resistance patterns of these bacteria need immediate action to inform treatment guidelines, increase infection control measure, and develop effective public health policies. This systematic review and meta-analysis aimed to assess the prevalence of phenotypic drug resistance profiles and multi-drug-resistant Pseudomonas and Acinetobacter species recovered from clinical specimens in Ethiopia.

METHODS

This systematic review and meta-analysis, which followed PRISMA principles, analyzed data from PubMed, Scopus, and Google Scholar to determine the prevalence and antibiotic resistance trends of Pseudomonas and Acinetobacter species in Ethiopia. Eligible studies were extracted by using Microsoft Excel and exported to STATA version 17 for analysis. The pooled prevalence was estimated using a random-effects model, and heterogeneity was examined using the I2 statistic. Publication bias was investigated using funnel plot analysis and Egger's test, and sensitivity analysis was used to assess the impact of individual studies on the total pooled findings.

RESULT

Of the 1,375 studies identified, 187 were eligible for qualitative analysis, leading to the inclusion of 65 studies in the meta-analysis. This analysis encompassed a total of 1,264 isolates, with 364 identified as Pseudomonas and Acinetobacter species. The systematic review revealed a pooled prevalence of 19.12% (95% CI: 14.86-23.38) for Pseudomonas species and 12.46% (95% CI: 5.82-19.10) for Acinetobacter species. The combined prevalence of both pathogens was 25.31 (95% CI: 18.61-32.00) with substantial heterogeneity (I2 = 93.6%, p < 0.001). across the studies. Pseudomonas exhibited high resistance rates to amoxicillin-clavulanic Acid (83.73%) and tetracycline (89.15%), while Acinetobacter showed 87.21% resistance to tetracycline and 79.72% to ceftriaxone. The overall pooled prevalence of MDR Pseudomonas species was 72.73% (95% CI: 67.02-78.44), and for Acinetobacter species, it reached 84.69% (95% CI: 78.78-90.59), respectively. Moreover, the pooled prevalence of MDR for both species isolated from clinical samples in Ethiopia was 74.79% (95% CI: 70.14-79.43), with significant heterogeneity (I2 = 99.7%, p < 0.001) across the studies.

CONCLUSION

The pooled prevalence of Pseudomonas and Acinetobacter species and their antibiotic resistance were alarmingly high in clinical samples in Ethiopia. These findings highlight the crucial need for more antimicrobial surveillance, stronger stewardship programs, and targeted research to combat the growing threat of resistance. Strategic public health policies are required to decrease these pathogens.

COVID-19 case fatality ratio and survival among hospitalized adults in Goiás, 2020: a cohort study

OBJECTIVE

To describe clinical-epidemiological and therapeutic aspects and to estimate case fatality ratio and risk factors for lower in-hospital survival due to COVID-19.

METHODS

This is a retrospective cohort study conducted in the state of Goiás, Brazil, in 2020, with data obtained from the Influenza Epidemiological Surveillance Information System and through a review of clinical records and hospital. Relative risk for in-hospital death was estimated and Poisson multiple regression and Cox regression analyses were performed. Survival functions were compared using the log-rank test and represented by Kaplan-Meier curves.

RESULTS

The sample consisted of 651 adults, whose median age was 59 years, 57.0% were admitted to public hospitals, 61.1% had severe acute respiratory syndrome on admission and 72.0% had at least one comorbidity, the most frequent being hypertension , diabetes and obesity. The overall case fatality ratio was 17.5% (95% confidence interval, 95%CI 14.7; 20.6), with no significant difference between public and private hospitals. The case fatality ratio was higher in the ≥60 years age group (prevalence ratio, PR 1.26; 95%CI 1.01; 1.58), in hypertensive patients (PR 1.41; 95%CI 1.14; 1 .74) and in those undergoing intensive care (PR 2.68; 95%CI 1.13; 6.32) and mechanical ventilation (PR 11.15; 95%CI 5.53; 22.46). The median time between hospital admission and death was 10 days (interquartile range, 6-18). Survival was lower in the ≥60 years age group (adjusted hazard ratio, HR 1.93; 95%CI 1.26; 2.95) and in those undergoing mechanical ventilation (HR 10.13; 95%CI 6.03; 17. 02).

CONCLUSION

Factors related to comorbidities and severity were independent predictors of shorter in-hospital survival among patients with COVID-19.

Minimally Invasive Tricuspid Valve Surgery: An Alternative Surgical Approach in the Era of Transcatheter Interventions

There has been significant growth in minimally invasive valve surgery over the past 2 decades, with novel approaches including video-assisted minithoracotomy, totally endoscopic, and robotic assisted. Outcomes of these techniques suggest that they can improve patient-reported outcomes, enhance mobility, reduce blood loss, and facilitate earlier discharge and return to work. Minimally invasive tricuspid valve surgery now provides surgeons and patients with an alternative option for treating tricuspid valve disease. These operations can be conducted safely and yield favorable outcomes with carefully selected patients. Herein, we provide a comprehensive overview of minimally invasive tricuspid valve surgery, including a summary of the current literature on minithoracotomy valve repair or replacement, beating heart, redo, and multiple-valve surgery. Finally, we contextualize how minimally invasive tricuspid valve surgery can play an important role in the era of emerging transcatheter strategies.

Factors Associated with Prolonged Mechanical Ventilation and 30-Day Mortality in Intubated COVID-19 Patients with Invasive Fungal Infections: A Retrospective Observational Study

COVID-19-associated invasive fungal infections (CAIFIs) contribute to increased mortality and morbidity rates. This study explores the epidemiology, laboratory parameters, radiological characteristics, treatments, and 30-day mortality risks of CAIFI in critically ill intubated patients while also evaluating factors associated with prolonged mechanical ventilation (PMV) in this population. Adults admitted to a tertiary hospital from 1 April 2021 to 31 March 2022 who were diagnosed with severe COVID-19, required invasive mechanical ventilation, and developed invasive fungal infection (IFI) during hospitalization were analyzed in this retrospective cohort study. Among 150 patients, 65 (43.3%) required PMV, with an in-hospital mortality rate of 64%. Candida albicans (47%) and Aspergillus fumigatus (27%) were the most prevalent pathogens. Multivariate analysis revealed that COVID-19 vaccination (adjusted odds ratio, aOR = 0.155, 95% confidence interval, 95% CI = 0.029-0.835, p = 0.030) and higher serum protein levels (aOR = 0.900, 95% CI = 0.819-0.989, p = 0.028) were significantly associated with a reduced risk of PMV. Meanwhile, elevated glucose levels (hazard ratio, HR = 1.047, 95% CI = 1.003-1.093, p = 0.036) and an increased neutrophil-to-lymphocyte ratio (HR = 1.024, 95% CI = 1.009-1.039, p = 0.002) were correlated with a greater 30-day mortality risk. Tracheostomy emerged as a protective factor, significantly reducing the risk of 30-day mortality (HR = 0.273, 95% CI = 0.127-0.589, p = 0.001). In this single-center study, patients with CAIFI exhibit a high mortality rate. Clinicians should maintain vigilance for IFI in critically ill COVID-19 patients with mechanical ventilation.

Evaluation of the performance of disease severity indices (SOFA, SAPS III, and MPM II) for the prediction of mortality rate in COVID-19 patients admitted to the intensive care units: a retrospective cross-sectional study

BACKGROUND

The COVID-19 pandemic created a high demand for hospital beds and intensive care, which led to the collapse of healthcare systems. Therefore, it is critical to identify factors associated with increased mortality in patients to prioritize high-risk individuals.

AIM

Given the potential for similar pandemics in the future, this study investigated predictors of mortality in ICU patients with COVID-19 and their correlation with mortality rates.

STUDY DESIGN

In this retrospective study, all patients admitted to the ICUs of Ghaem and Imam Reza tertiary hospitals in Mashhad with a definitive diagnosis of COVID-19 from March 21, 2020, to December 1, 2021, were evaluated for disease severity assessment criteria, including SOFA, SAPS III, and MPM II.

RESULTS

One hundred seventy-two patients with COVID-19 who were admitted to the ICU were evaluated (147 patients in the non-survivor group and 25 patients in the survivor group. The median SAPS, SOFA, and MPM scores were significantly higher in non-survivors (p<0.001 in all cases). Higher SAPS, SOFA, and MPM were associated with an increased risk of mortality in COVID-19 patients in the ICU. The best cut-off points for the three scoring systems, SAPS, SOFA, and MPM, were 39, 6, and 13.7, respectively. The AUC value of the SOFA score was significantly higher than the SAPS (P = 0.0274) and MPM (P = 0.0209) scores.

CONCLUSION

COVID-19 patients admitted to the ICU with higher SOFA, MPM, and SAPS scores suffered from higher mortality rates. Because the SOFA score showed high predictive accuracy, this scoring system should be considered a priority as an essential tool in triaging and managing critically ill COVID-19 patients.

Efficacy of tocilizumab for hospitalized patients with COVID-19 pneumonia and high IL-6 levels: A randomized controlled trial

BACKGROUND

The objective of this clinical trial is to evaluate the efficacy and safety of IL-6 driven personalized treatment strategy with tocilizumab in patients with severe COVID-19 pneumonia.

TRIAL DESIGN

Randomized, controlled, open-label, single-center trial of a tocilizumab treatment strategy in adult patients hospitalized with severe COVID-19 pneumonia and IL-6 serum levels > 40 pg/mL.

METHODS

Patients were randomized 1:1 to receive standard of care (SOC) or SOC plus one dose of tocilizumab. The primary outcome was death or need for invasive mechanical ventilation (IMV) within 28 days after randomization. Secondary outcomes included ICU admission, days on IMV and hospital stay. A meta-analysis of clinical trials to evaluate the effect of tocilizumab on mortality and need of IMV in patients with COVID-19 pneumonia was performed.

RESULTS

Sixty-two patients were included: 30 in the SOC arm and 32 in the standard-treatment plus tocilizumab arm. The primary outcome occurred in 12.9% in the tocilizumab arm and 32.3% in the SOC arm(p = 0.068). There was a trend towards fewer days on IMV (7.5 vs 19.5 days, p = 0.073) and a shorter hospital stay (4 vs 8 days, p = 0.134) in the tocilizumab group. No serious adverse events were reported. The meta-analysis revealed a RR for death or IMV of 0.83 (95% CI: 0.77-0.89) in patients receiving tocilizumab, compared to patients receiving SOC.

CONCLUSION

Tocilizumab could be effective to prevent death or IMV in patients with severe COVID-19 pneumonia and high IL-6 serum levels. Safety profile of tocilizumab does not arise major concern in patients with severe COVID19.

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods

BACKGROUND

Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. This study aims to evaluate factors related to patients and hospitals in the prediction of in-hospital mortality, need for invasive mechanical ventilation (IMV), and intensive care unit (ICU) transfer throughout the pandemic waves.

METHODS

This multicenter retrospective cohort included COVID-19 patients from 39 hospitals, from March/2020 to August/2022. The pandemic was divided into waves: 10/03/2020-14/11/2020 (first), 15/11/2020-25/12/2021 (second), 26/12/2021-03/08/2022 (third). Patient-related factors included clinical, demographic, and laboratory data, while hospital-related factors covered funding sources, accreditation, academic status, and socioeconomic characteristics. Shapley additive explanation (SHAP) values derived from the predictions of a light gradient-boosting machine (LightGBM) model were used to assess potential risk factors for death, IMV and ICU.

RESULTS

Overall, 16,958 adult patients were included (median age 59 years, 54.7% men). LightGBM achieved competitive effectiveness metrics across all periods. Temporal drifts were observed due to a decrease in various metrics, such as the recall for the positive class [ICU: 0.4211 (wave 1) to 0.1951 (wave 3); IMV: 0.2089 (wave 1) to 0.0438 (wave 3); death: 0.2711 (wave 1) to 0.1175 (wave 3)]. Peripheral arterial oxygen saturation to the fraction of inspired oxygen ratio (SatO2/FiO2) at admission had great predictive capacity for all outcomes, with an optimal cut-off value for death prediction of 227.78. Lymphopenia had its association strength increased over time for all outcomes, optimal threshold for death prediction of 643 × 109/L. Thrombocytopenia was the most important feature in wave 2 (ICU); overall, values below 143,000 × 109/L were more related to death.

CONCLUSION

Data drifts were observed in all scenarios, affecting potential predictive capabilities of explainable machine learning methods. Upon admission, SatO2/FiO2 values, platelet and lymphocyte count were significant predictors of adverse outcomes in COVID-19 patients. Overall, inflammatory response markers were more important than clinical characteristics. Limitations included sample representativeness and confounding factors. Integrating the drift's knowledge into models to improve effectiveness is a challenge, requiring continuous updates and monitoring of performance in real-world applications.

CLINICAL TRIAL NUMBER

Not applicable.

Novel biomarkers of mitochondrial dysfunction in Long COVID patients

Coronavirus disease 2019 (COVID-19) can lead to severe acute respiratory syndrome, and while most individuals recover within weeks, approximately 30-40% experience persistent symptoms collectively known as Long COVID, post-COVID-19 syndrome, or post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC). These enduring symptoms, including fatigue, respiratory difficulties, body pain, short-term memory loss, concentration issues, and sleep disturbances, can persist for months. According to recent studies, SARS-CoV-2 infection causes prolonged disruptions in mitochondrial function, significantly altering cellular energy metabolism. Our research employed transmission electron microscopy to reveal distinct mitochondrial structural abnormalities in Long COVID patients, notably including significant swelling, disrupted cristae, and an overall irregular morphology, which collectively indicates severe mitochondrial distress. We noted increased levels of superoxide dismutase 1 which signals oxidative stress and elevated autophagy-related 4B cysteine peptidase levels, indicating disruptions in mitophagy. Importantly, our analysis also identified reduced levels of circulating cell-free mitochondrial DNA (ccf-mtDNA) in these patients, serving as a novel biomarker for the condition. These findings underscore the crucial role of persistent mitochondrial dysfunction in the pathogenesis of Long COVID. Further exploration of the cellular and molecular mechanisms underlying post-viral mitochondrial dysfunction is critical, particularly to understand the roles of autoimmune reactions and the reactivation of latent viruses in perpetuating these conditions. This comprehensive understanding could pave the way for targeted therapeutic interventions designed to alleviate the chronic impacts of Long COVID. By utilizing circulating ccf-mtDNA and other novel mitochondrial biomarkers, we can enhance our diagnostic capabilities and improve the management of this complex syndrome.

Predictors of venous thromboembolic events in hospitalized patients with COVID-19

COVID-19 is associated with an increased risk of venous thromboembolism (VTE) in hospitalized patients. Although prior studies have attempted to identify predictors of VTE, restricted sample size and use of administrative claims data have limited such analyses. We utilized data from hospitalized patients in the CORONA-VTE Network, a United States multicenter registry of adult patients with PCR-confirmed COVID-19 (N = 3,844). The primary outcome was time-to-first event for a composite of adjudicated pulmonary embolism or deep vein thrombosis during 90-day follow-up. The candidate variables were selected by a priori clinical consensus. We conducted cause-specific Cox regression analysis adjusted for the selected variables for each imputed dataset and pooled the estimated HRs for reporting (p < 0.05 for significance). VTE occurred in 206 patients, with a cumulative incidence of 5.3% at 90 days. The covariates associated with increased risk of VTE were history of VTE (HR: 1.71; 95% CI: 1.11-2.63), corticosteroid therapy (HR: 1.76; 95% CI: 1.32-2.33) and known thrombophilia (HR: 3.56; 95% CI: 1.54-8.21) while therapeutic anticoagulation at baseline (HR: 0.42; 95% CI: 0.26-0.69), antecedent use of statins (HR: 0.67; 95% CI: 0.50-0.90), and prophylactic anticoagulation during hospitalization (HR: 0.52; 95% CI: 0.38-0.71) were associated with reduced risk of VTE. While prior VTE, corticosteroid therapy, and known thrombophilia were associated with an increased risk of VTE, prescriptions of prophylactic and therapeutic anticoagulation, and statins were associated with a decreased risk. Once externally validated, these findings may inform risk assessment in hospitalized patients with COVID-19.

Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification

Key Points

Background

Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established.

Methods

PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model.

Results

Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I2>90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool.

Conclusions

Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.

Assessment of mortality due to severe SARS-CoV-2 infection in public and private intensive care units in Brazil: a multicenter retrospective cohort study

BACKGROUND

This retrospective multicenter cohort study compared characteristics and outcomes of 5,790 critically ill patients with COVID-19 in Brazil's public and private intensive care units. Patients in public intensive care units exhibited greater disease severity, more frequent use of organ support, and higher mortality rates compared to those in private intensive care units. The risk of in-hospital death was more than twice as high in public intensive care units. ■ Public intensive care unit COVID-19 patients presented with more comorbidities and higher severity at admission. ■ Public intensive care units required more invasive organ support (e.g., mechanical ventilation, vasopressors, and renal replacement therapy) but less non-invasive ventilation and high-flow nasal cannula than private intensive care units. ■ In-hospital mortality was higher in public intensive care units, with an increased risk of death even after adjusting for patient characteristics and illness severity at intensive care unit admission.

OBJECTIVE

To compare the clinical characteristics, use of organ support, and outcomes of critically ill patients with COVID-19 admitted to public and private intensive care units.

METHODS

This multicenter retrospective cohort study included patients admitted to four intensive care units from March 1, 2020, to December 31, 2021. Patients with COVID-19 admitted to public and private intensive care units were compared. The primary outcome of interest, in-hospital mortality, was assessed using a hierarchical logistic regression (multilevel) model adjusted for study site and patient characteristics.

RESULTS

A total of 5,790 patients with COVID-19 were admitted to the participating intensive care units, with 3,321 (57.3%) admitted to private hospitals and 2,469 (42.6%) admitted to public hospitals. Patients in public intensive care units were less likely to be male and had higher median SAPS III scores, Charlson Comorbidity Index values, and SOFA scores. They also required mechanical ventilation (53.1% versus 40.0%, p<0.001), vasopressors (43.1% versus 33.9%, p<0.001), and renal replacement therapy (20.3% versus. 14.5%, p<0.001) more frequently than those in private intensive care units. In contrast, patients in private intensive care units were more frequently managed with non-invasive ventilation (38.0% versus 66.8%; p<0.001) and high-flow nasal cannulas (18.3% versus 48.1%; p<0.001). The in-hospital mortality rate was significantly higher in public intensive care units (40.3%) compared to private intensive care units (16.4%) (adjusted OR=2.96; 95%CI=1.94-4.51; p<0.001).

CONCLUSION

We observed significant differences in resource utilization and mortality rates between patients with COVID-19 admitted to public and private intensive care units. Patients with COVID-19 in public care units face a higher risk of in-hospital mortality compared to those in private care units.

Subjective smell and taste dysfunctions and prognosis in patients with COVID-19 admitted to a major public hospital in Southern Brazil: A retrospective cohort study (NUTRICOVID19)

The literature notes that coronavirus disease 2019 (COVID-19) patients with olfactory disturbances tend to have lower disease severity and that olfactory disturbances may act as a protective factor against mortality. So, this study aimed to explore the association of smell and/or taste disturbance with disease severity and all-cause mortality among patients who were hospitalized with COVID-19. A retrospective cohort study (Nutrition and COVID-19 Study [NUTRICOVID19]) was conducted with 1331 patients (men and women, age ≥ 18 years) hospitalized with COVID-19 between June and December 2020. Poisson and Cox regressions were used to investigate the unadjusted and adjusted associations between smell and/or taste disturbance and the following prognostic indicators: length of stay (LOS), intensive care unit admission, need for invasive mechanical ventilation (IMV), and mortality. Patients with altered smell and/or taste had a shorter LOS (9 vs 11 days), were less likely to require IMV (22% vs 35.1%), and experienced lower mortality (17.1% vs 29.2%) compared to those without such symptoms. After multivariable adjustment, patients with smell and/or taste disturbances were 33% less likely to need IMV compared to those without such symptoms (RR = 0.67; 95% CI = 0.50-0.88), but the relationship between these symptoms and mortality lost statistical significance. In this population of patients with COVID-19, the presence of smell and/or taste disturbances was associated to lower rates of IMV.

Vaccine-Preventable Conditions: Disparities in Hospitalizations Affecting Rural Communities in the Southeast United States

Vaccinations are among the most effective means of preventing hospitalizations related to infections. Despite this, high hospitalization rates for vaccine-preventable diseases strain available healthcare resources and imply deficiencies in primary care. Barriers to vaccinations exist, such as the recent pandemic, vaccine hesitancy, misinformation, and access to care. This study analyzes hospitalization rates due to vaccine-preventable conditions and identifies factors contributing to an increase in these rates in the southeast United States. This study used data from four different data sources. The data covers four pre-pandemic years (2016 to 2019) and the pandemic period (2020 to 2022). The analysis categorized the numbers and rates of hospitalizations for conditions with an available preventative vaccine across three age groups: pre-school aged children, school-aged children, and adults. Comparisons between school- versus non-school-mandated vaccines and a focus on differences between rural versus urban communities, as well as demographic characteristics (i.e., gender, race, and ethnicity), are included. Chi-squared tests were used to assess differences in this descriptive part of the analysis. Linear multiple regression was used to examine the independent influence of geographic location while accounting for potential longitudinal trends and the dimensions of the SVI, including socioeconomic status, household composition, disability, minority status and language, and household type and transportation. The dataset included data from 22,797,826 inpatient episodes, including 32,358 for which the principal reason for hospitalization was a vaccine-preventable condition, not including COVID-19. The analysis shows a consistent pattern characterized by higher rates of hospitalization for counties classified as rural. The pattern holds for preschool age (p < 0.001), school age (p = 0.004), and adults (p = 0.009). The differences are statistically significant in the white population (p = 0.008); in pre-school children, school-age children, and adults (p < 0.001); in females (p = 0.08 in pre-school, and p = 0.013 in adults); and black adults (p = 0.02). The regression results confirmed the findings of the descriptive analysis, indicating significantly higher rates in rural communities. Finally, the regression analysis also showed significantly higher rates associated with greater social vulnerability. This study highlights gaps in vaccination opportunities. These gaps can be seen geographically and in terms of social vulnerability, affected by factors such as poverty, language barriers, household composition, and access to care. Hospitalizations due to immunizable diseases were found to be higher in rural areas, particularly among adults. Communities with a high SVI show a significant increase in hospitalization rates. Community-engaged vaccination outreach programs and state policies could improve vaccination rates, and therefore, public health in rural areas, reducing hospitalizations, and lowering infectious disease risks in these areas.

Analysis of the implementation of pre-hospital and hospital care for cases of accidents and violence in Brazil

The implementation of two guidelines of the National Policy for Reducing Morbidity and Mortality from Accidents and Violence (PNRMAV) regarding pre-hospital care (APH) and hospital care (AH) was analyzed. APH and AH managers from Brazilian municipalities participated in a cross-sectional study (2020-2022), answering a questionnaire via the Redcap platform. Descriptive and comparative analyses were performed. The response rate was 2.3% of the municipalities (n=128), of which 9 were capitals. Capitals presented better results in all items evaluated. In mobile APH, 82.8% of the municipalities and 100% of the capitals use their own transportation for patients. In the capitals, the services receive and refer more cases intersectoral (p-value<0.05). In AH, the capitals make greater use of protocols (p=0.008) and 40% of managers consider the beds adequate for their needs. The implementation of the Systematization of pre-hospital care guideline was good for 59.4% of the municipalities and 77.8% of the capitals, while the Interdisciplinary Assistance to Victims guideline was regular in 45.1% of the municipalities and good in 75% of the capitals. Despite the advances in the implementation of the PNRMAV, it is necessary to strengthen the emergency care networks and optimize the available resources.

Comorbidities of hypothyroidism

Hypothyroidism is a relatively common condition that may affect as many as 10% of the population worldwide when its overt and subclinical presentations are considered. Important clinical comorbidities are highly prevalent in people with hypothyroidism and diminish quality of life and functional status in a manner that is proportional to the number of comorbidities present and their severity. This article reviews the common comorbidities of hypothyroidism, as reported in the literature. The comorbidities of hypothyroidism include clinical conditions commonly associated with hypothyroidism, such as dyslipidaemia, hypertension, fatigue or (possibly) cardiovascular disease, and can appear whether or not intervention with LT4 is applied appropriately to ensure biochemical euthyroidism. Other comorbidities may share some pathogenetic background with hypothyroidism, including depression or anxiety, or autoimmune conditions. Hypothyroidism may arise as a comorbidity of some other conditions, e.g. following the application of targeted cancer therapies or some disease-modfying treatments for multiple sclerosis. Other common treatments, including metformin, glucocorticoids or proton pump inhibitors, among others, may alter levels of thyrotropin, thus impacting on the monitoring of thyroid dysfunction and the diagnosis of thyroid dysfunction. Ensuring good control of hypothyroidism is a necessary first step in managing any patient with hypothyroidism. Then, physicians should be aware of the possibility of other comorbid conditions that must be addressed to achieve an optimal patient outcome.

Enhancing safety monitoring in post-stroke rehabilitation through wearable technologies

ObjectiveCurrent clinical practice guidelines support structured, progressive protocols for improving walking after stroke. Technology enables monitoring of exercise and therapy intensity, but safety concerns could also be addressed. This study explores functional mobility in post-stroke individuals using wearable technology to quantify movement smoothness-an indicator of safe mobility.DesignObservational cohort study.SettingA movement analysis and rehabilitation laboratory.ParticipantsA total of 56 chronic post-stroke individuals and 51 healthy controls.InterventionParticipants performed the mobility test while wearing an inertial measurement unit attached to their waist. Thirty-two healthy participants also engaged in a steady-state walking task.Main measuresFunctional mobility smoothness by examining angular velocities in the yaw, pitch, and roll axes, employing the spectral arc length metrics.ResultsOur findings reveal that post-stroke individuals extend the duration of the timed-up-and-go test (≈9 s and 23 s longer compared to the controls) to ensure safe mobility-greater mobility smoothness (p < 0.001). Notably, for mild and severe impairments, post-stroke mobility demonstrated ≈8% and ≈11% greater smoothness in pitch movements, respectively (p = 0.025 and p = 0.002). In the roll direction, mobility was ≈12% smoother in cases of severe strokes (p = 0.006).ConclusionThis study addresses a crucial gap in the understanding of mobility smoothness in chronic stroke survivors using wearable technology. Our study suggests the potential utility of spectral arc length to predict challenging mobility situations in real-world situations. We highlight the potential for automated monitoring of safety offering promising avenues for real-time, real-life monitoring.

Rivers at risks: The interplay of "COVID kit" medication misuse and urban waterway contaminants

This study investigates the environmental impact of the widespread use of "COVID Kit" drugs-azithromycin (AZI), ivermectin (IVE), and hydroxychloroquine (HCQ)-in urban rivers of Curitiba in Brazil, during and after the COVID-19 pandemic. The research focuses on the occurrence and concentrations of these pharmaceuticals in water and sediment samples collected from key urban rivers. Concentrations of AZI, IVE, and HCQ in water ranged from 326 to 3340 ng/L, 130-3340 ng/L, and 304-3314 ng/L, respectively, while in sediment, they ranged from 18 to 249 ng/g, 21-480 ng/g, and 38-673 ng/g, respectively. Results indicate a significant increase in AZI, IVE, and HCQ concentrations during the pandemic. Concentrations of these drugs peaked in September 2020 and March 2021, declining after the start of Brazil's vaccination campaign. However, the levels of these pharmaceuticals remained elevated in some areas even after the decline in their usage. Environmental risk assessments were conducted to evaluate the potential ecological hazards posed by these pharmaceuticals, revealing the long-term persistence of these drugs in aquatic environments and their potential to contribute to antimicrobial resistance. The findings of this study underscore the critical need for robust regulatory measures and improved wastewater treatment processes to prevent pharmaceutical contamination in urban water systems, particularly during global health crises.

Expression patterns of Galectin-3 and NLRP3 in Chagas reactivation and graft damage in heart transplants

OBJECTIVE

This study aimed to assess the expression patterns of galectin-3 (Gal-3) and NLRP3 in heart transplant recipients according to the presence of reactivated Trypanosoma cruzi infection or allograft rejection in Chagas and non-Chagas heart transplant recipients.

METHODS

Gal-3 and NLRP3 expression levels were analyzed in endomyocardial biopsies from 31 heart transplant recipients, including 16 patients with chronic Chagas disease (ChD) and 15 without ChD. Samples were evaluated during periods of graft rejection or ChD reactivation, characterized by intense myocardial cellular infiltrate, and after remission of the infiltrate, classified by histopathological severity. The transcriptional levels of genes encoding Gal-3, NLRP3, Asc, caspase-1, and IL-1β were identified using the GEO2T tool across different experimental conditions.

RESULTS

Gal-3 expression was lower in the myocardial infiltrate of ChD patients compared to non-ChD patients (p < 0.0001), whereas NLRP3 positivity was higher in ChD patients (p < 0.05). In a murine model of T. cruzi infection, elevated Gal-3 mRNA and NLRP3 inflammasome levels were observed in myocardial interstitial cells (p < 0.05). Peripheral blood mononuclear cells and cells from rodent cardiac allografts showed increased Gal-3 mRNA and NLRP3 levels compared to non-transplanted and rodent cardiac isografts (p < 0.001).

CONCLUSIONS

Our findings suggest that Gal-3 and NLRP3 may be important biomarkers for differentiating heart transplant recipients with and without ChD regarding the myocardial immunological processes.

Managing strategies of chemotherapy and radiotherapy-induced oral mucositis

Radiotherapy and chemotherapy are widely employed as primary non-surgical cancer treatments; however, their non-selective cytotoxicity often leads to adverse events such as oral mucositis (OM), particularly in head and neck cancer therapies. International guidelines provide recommendations for managing chemoradiotherapy-induced OM in various clinical contexts. Subsequently, emerging researches have introduced evidence supporting novel approaches or existing regimens for OM prevention and treatment. The repurposing of established drugs has garnered significant interest due to its shorter development timeline, improved safety profiles, and lower costs compared to new drug development. For example, clinical trials assessing established drugs such as melatonin, clonidine, and pentoxifylline indicate promising potential for managing OM. Additionally, several emerging pharmacological interventions have demonstrated considerable efficacy; SAMITAL and rhIL-11 are supported by phase II clinical trials and prospective studies, while probiotics like Streptococcus salivarius K12 and curcumin have shown effectiveness in randomized clinical trials. Furthermore, recent high-level studies have reinforced the efficacy of non-pharmacological interventions, such as photobiomodulation (PBM) and cryotherapy, over the past two years. In all, given the evidence supporting different strategies, PBM and oral cryotherapy are highly recommended for managing OM when feasible. Topical clonidine, melatonin, oral pentoxifylline, topical SAMITAL or rhIL-11, oral SsK12, and curcumin may also be utilized but would benefit from validation in larger trials. Besides, Verbascoside, Palifermin, Amifostine, and Avasopasem manganese can be suggested for OM management, while the side effects should be monitored. The accessibility and cost/effectiveness of specific managing strategies of OM should be considered when selecting appropriate options.

Is COVID-19 Still a Threat? An Expert Opinion Review on the Continued Healthcare Burden in Immunocompromised Individuals

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a profound global impact. The emergence of several variants during the pandemic has presented numerous challenges in preventing and managing this disease. The development of vaccines has played a pivotal role in controlling the pandemic, with a significant portion of the global population being vaccinated. This, along with the emergence of less virulent SARS-CoV-2 variants, has led to a reduction in the severity of COVID-19 outcomes for the overall population. Nevertheless, individuals with immunocompromising conditions continue to face challenges given their suboptimal response to vaccination and vulnerability to severe COVID-19. This expert review synthesizes recent published evidence regarding the economic and human impact of COVID-19 on such individuals. The literature suggests that rates of hospitalization, intensive care unit admission, and mechanical ventilation use were high during the pre-Omicron era, and remained high during Omicron and later, despite vaccination for this population. Moreover, studies indicated that these individuals experienced a negative impact on their mental health and health-related quality of life (HRQoL) compared to those without immunocompromising conditions, with elevated levels of anxiety, depression, and distress reported. Further, these individuals with immunocompromising conditions experienced substantial costs associated with COVID-19 and loss of income during the pandemic, though the evidence on the economic burden of COVID-19 in such individuals is limited. Generally, COVID-19 has increased healthcare resource use and costs, impaired mental health, and reduced HRQoL in those with varied immunocompromising conditions compared to both those without COVID-19 and the general population-underscoring the importance of continued real-world studies. Ongoing research is crucial to assess the ongoing burden of COVID-19 in vaccinated individuals with immunocompromising conditions who are still at risk of severe COVID-19 outcomes to ensure their needs are not disproportionately worse than the general population.

Development of a machine learning model and a web application for predicting neurological outcome at hospital discharge in spinal cord injury patients

BACKGROUND

Spinal cord injury (SCI) is a devastating condition with profound physical, psychological, and socioeconomic consequences. Despite advances in SCI treatment, accurately predicting functional recovery remains a significant challenge. Conventional prognostic methods often fall short in capturing the complex interplay of factors influencing SCI outcomes. There is an urgent demand for more precise and comprehensive prognostic tools that can guide clinical decision-making and improve patient care in SCI.

PURPOSE

This study aims to develop and validate a machine learning (ML) model for predicting American Spinal Injury Association (ASIA) Impairment Scale (AIS) at discharge in SCI patients. We also aim to convert this model into an open-access web application.

STUDY DESIGN/SETTING

This was a retrospective cohort study enrolling traumatic SCI patients from 1991 to 2015, analyzed in 2023. Data were obtained from the Japan Rehabilitation Database (JARD), a comprehensive nationwide database that includes SCI patients from specialized SCI centers and rehabilitation hospitals across Japan.

PATIENTS SAMPLE

4,108 SCI cases from JARD were reviewed, excluding 405 cases, patients caused by nontraumatic injuries, patients who were graded as AIS E at admission, and patients without data of AIS at discharge, resulting in 3,703 cases being included in the study. Patient demographics and specific SCI injury characteristics at admission were utilized for model training and prediction.

OUTCOME MEASURES

Model performance was evaluated based on R2, accuracy, and the weighted Kappa coefficient. Shapley additive explanations (SHAP) values highlighted significant features influencing the model's output.

METHODS

The primary outcome was AIS at discharge, treated as a continuous variable (0-4) to capture the ordinal nature and clinical significance of potential misclassifications. Data preprocessing included multicollinearity removal, feature selection using the Boruta algorithm, and iterative imputation for missing data. The dataset was split using the hold-out method with a 7:3 ratio resulting in 2,592 cases for training and 1,111 cases for testing the regression model. A best performing model was defined as the highest R2 using PyCaret's automated model comparison. Final predictions of regression model were discretized to the original AIS categories for clinical interpretation.

RESULTS

The Gradient Boosting Regressor (GBR) was identified as the optimal model. The GBR model showed an R² of 0.869, accuracy of 0.814, and weighted Kappa of 0.940. Eleven key variables, including AIS at admission, the day from injury to admission, and the motor score of L3, were identified as significant based on SHAP values. This model was then adapted into a web application via Streamlit.

CONCLUSIONS

We developed a high-accuracy ML model for predicting the AIS at discharge, which effectively captures the ordinal nature of the AIS scale, using 11 key variables. This model demonstrated its performance to provide reliable prognostic information. The model has been integrated into a user-friendly, open-access web application (http://3.138.174.54:8502/). This tool has the potential to aid in resource allocation and enhance treatment for each patient.

Preventive Photobiomodulation for Chemotherapy-Induced Oral Mucositis: A Systematic Review of Randomized Clinical Trials

Background/Objectives: Oral mucositis (OM) is the most common acute complication among cancer patients. It initially manifests as an inflammatory process, beginning with erythema and edema of the oral mucosa, progressing to erosive lesions, and ultimately leading to highly painful ulcers. This systematic review seeks to evaluate the efficacy of preventive PBM protocols in mitigating chemotherapy-induced OM. Methods: The PRISMA guidelines were followed. The search was conducted in August 2024 in the following databases: Pubmed/Medline, Scopus, WoS, Cochrane, SciELO, BDTD, and BVS/IBECS. Only randomized clinical trials that utilized preventive photobiomodulation protocols in chemotherapy patients were included. All studies involving patients previously treated with radiation therapy were excluded. The Joanna Briggs Institute tool was employed for risk of bias analysis. Results: The total sample size consisted of 828 patients aged between 1 and 84 years. There was no predisposition based on gender or age. When the patients were evaluated under preventive protocols, some cases of mucositis manifested in a total of 339 cases. Of the total number of patients in the 13 selected studies (n = 828), 40.94% developed oral mucositis over the course of chemotherapy cycles. Comparing the experimental and control groups, 211 patients who did not receive preventive laser treatment developed oral mucositis; in contrast, only 128 in the experimental group did. Eighty-five percent of the studies exhibited a low risk of bias. Conclusions: Preventively applied photobiomodulation proves effective in minimizing or even preventing the manifestation of oral mucositis and reducing the severity of lesions that arise during oncological treatment. Registration PROSPERO (CRD42023465329).

Genetic determinants of COVID-19 severity and mortality: ACE1 Alu 287 bp polymorphism and ACE1, ACE2, TMPRSS2 expression in hospitalized patients

Background

The angiotensin-converting enzyme 2 (ACE2) and the transmembrane serine protease 2 (TMPRSS2) are central human molecules in the SARS-CoV-2 virus-host interaction. Evidence indicates that ACE1 may influence ACE2 expression. This study aims to determine whether ACE1, ACE2, and TMPRSS2 mRNA expression levels, along with the ACE1 Alu 287 bp polymorphism (rs4646994), contribute to the severity and mortality of COVID-19.

Methods

Swabs were collected in two Brazilian cities in 2020: Belo Horizonte (n = 134) and Rio de Janeiro (n = 41). A swab of mild patients in Rio de Janeiro who were not hospitalized (n = 172) was also collected. All analyzed biological material was obtained from residual diagnostic samples in 2020, prior to the emergence of SARS-CoV-2 variants of concern. ACE1, ACE2, TMPRSS2, and B2M (reference gene) expression levels were evaluated in 40 cycles of quantitative PCR. ACE1 Alu 287 bp polymorphism was genotyped using the FastStart Universal SYBR Green Master kit.

Results

The median age differed between clinical sites (p = 0.016), but no difference in median days of hospitalization was observed (p = 0.329). Age was associated with severity (p = 0.014) and mortality (p = 0.014) in the Belo Horizonte cohort. No alteration in ACE1, ACE2 and TMPRSS2 expression was associated with severity or mortality. ACE1 polymorphism rs4646994 did not influence the likelihood of either outcome. A meta-analysis including available data from the literature showed significant effects: the D-allele conferred risk (OR = 1.39; 95% CI [1.12-1.72]).

Liquid biopsy to identify Barrett's oesophagus, dysplasia and oesophageal adenocarcinoma: the EMERALD multicentre study

BACKGROUND

There is no clinically relevant serological marker for the early detection of oesophageal adenocarcinoma (EAC) and its precursor lesion, Barrett's oesophagus (BE).

OBJECTIVE

To develop and test a blood-based assay for EAC and BE.

DESIGN

Oesophageal MicroRNAs of BaRRett, Adenocarcinoma and Dysplasia (EMERALD) was a large, international, multicentre biomarker cohort study involving 792 patient samples from 4 countries (NCT06381583) to develop and validate a circulating miRNA signature for the early detection of EAC and high-risk BE. Tissue-based miRNA sequencing and microarray datasets (n=134) were used to identify candidate miRNAs of diagnostic potential, followed by validation using 42 pairs of matched cancer and normal tissues. The usefulness of the candidate miRNAs was initially assessed using 108 sera (44 EAC, 34 EAC precursors and 30 non-disease controls). We finally trained a machine learning model (XGBoost+AdaBoost) on RT-qPCR results from circulating miRNAs from a training cohort (n=160) and independently tested it in an external cohort (n=295).

RESULTS

After a strict process of biomarker discovery and selection, we identified six miRNAs that were overexpressed in all sera of patients compared with non-disease controls from three independent cohorts of different nationalities (miR-106b, miR-146a, miR-15a, miR-18a, miR-21 and miR-93). We established a six-miRNA diagnostic signature using the training cohort (area under the receiver operating characteristic curve (AUROC): 97.6%) and tested it in an independent cohort (AUROC: 91.9%). This assay could also identify patients with BE among patients with gastro-oesophageal reflux disease (AUROC: 94.8%, sensitivity: 92.8%, specificity: 85.1%).

CONCLUSION

Using a comprehensive approach integrating unbiased genome-wide biomarker discovery and several independent experimental validations, we have developed and validated a novel blood test that might complement screening options for BE/EAC.

TRIAL REGISTRATION NUMBER

NCT06381583.

Clinical outcomes of COVID-19 in patients with liver cirrhosis - a propensity-matched analysis from a multicentric Brazilian cohort

BACKGROUND

Cirrhosis has been pointed out as a clinical entity that leads to worse clinical prognosis in COVID-19 patients. However, this concept is controversial in the literature. We aimed to evaluate clinical outcomes by comparing patients with cirrhosis to those without cirrhosis in a Brazilian cohort.

METHODS

Data from 20,164 COVID-19 inpatients were collected from 41 hospitals in Brazil between March to September 2020 and March 2021 to August 2022. We compared 117 patients with cirrhosis to 632 matched controls. A propensity score model was used to adjust for potential confounding variables, incorporating some predictors: age, sex at birth, number of comorbidities, hospital of admission, whether it was an in-hospital clinical manifestation of COVID-19, and admission year. Closeness was defined as being within 0.16 standard deviations of the logit of the propensity score.

RESULTS

The median age was 61 (IQR 50-70) years old, and 63.4% were men. There were no significant differences in the self-reported symptoms. Patients with cirrhosis had lower median hemoglobin levels (10.8 vs. 13.1 g/dl), lower platelets (127,000 vs. 200,000 cells/mm3), and leukocyte counts, as well as lower median C-reactive protein (63.0 vs. 76.0 p = 0.044) when compared to controls. They also had higher mortality compared to matched controls (51.3% vs. 21.7%, p < 0.001). They also had higher frequencies of admission in an intensive care unit (51.3% vs. 38.0%, p = 0.007), invasive mechanical ventilation (43.9% vs. 26.6%, p < 0.001), dialysis (17.9% vs. 11.1%, p = 0.038), septic shock (23.9% vs. 14.9%; p = 0.015) and institution of palliative care (19.7% vs. 7.4%; p < 0.001).

CONCLUSIONS

This study has shown that COVID-19 inpatients with cirrhosis had significantly higher incidence of severe outcomes, as well as higher frequency of institution of palliative care when compared to matched controls. Our findings underscore the need for these patients to receive particular attention from healthcare teams and allocated resources.

Signaling Pathways (TNF-α-NF-κB, TLR2-TLR4 as well as ROS-MDA) and Cardiac Damages during Cardiac Surgeries (Coronary Stenting, Permanent Pacemaker Implantations, Radiofrequency Ablations)

INTRODUCTION

The mutual activations of multiple signaling pathways are the key factors in the development and progression of myocardial cell injuries.

OBJECTIVES

This research aimed to compare the different degrees of myocardial injury after coronary stenting, permanent pacemaker implantations, or cardiac radiofrequency ablation and to investigate the effects of the mutual activation of TNF-α/NF-κB, TLR2/TLR4, and ROS/MDA signaling pathways on myocardial injury in elderly patients after coronary stents or permanent pacemakers or radiofrequency ablation.

METHODS

We determined reactive oxygen species (ROS), malondialdehyde (MDA), toll-like receptor 2 (TLR2), toll-like receptor 4 (TLR4), nuclear factor kappa B (NF-κB), tumor necrosis factor- α (TNF-α) and high-sensitive cardiac troponin T (hs-cTnT) as markers of myocardial injury in patients.

RESULTS

The levels of ROS, MDA, TLR2, TLR4, NF-κB, TNF-α, and hs-cTnT were increased in patients with permanent pacemaker implantations when compared to patients with cardiac radiofrequency ablation (P < 0.01) at 6 months and were further increased in patients with coronary stenting compared to patients with cardiac radiofrequency ablation and permanent pacemaker implantations at 6 months, respectively (P < 0.01). This research confirmed that ROS, MDA, TLR2, TLR4, NF-κB, and TNF-α predicted myocardial injury severity.

CONCLUSION

Oxidative stress (ROS/MDA signaling pathway) may be linked to immune response (TLR2/TLR4 signaling pathway) and pro-inflammatory response (TNF-α/NF-κB signaling pathway) in myocardial injury, and ROS/MDA signaling may play a dominant role.

Early identification of high-risk patients admitted to emergency departments using vital signs and machine learning

BACKGROUND

Rapid and accurate identification of high-risk patients in the emergency departments (EDs) is crucial for optimizing resource allocation and improving patient outcomes. This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.

METHODS

This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage, Assessment, and Treatment (CETAT) database, which was collected between January 1st, 2020, and June 25th, 2023. The primary outcome was the identification of high-risk patients needing immediate treatment. Various machine learning methods, including a deep-learning-based multilayer perceptron (MLP) classifier were evaluated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). AUC- ROC values were reported for three scenarios: a default case, a scenario requiring sensitivity greater than 0.8 (Scenario I), and a scenario requiring specificity greater than 0.8 (Scenario II). SHAP values were calculated to determine the importance of each predictor within the MLP model.

RESULTS

A total of 38,797 patients were analyzed, of whom 18.2% were identified as high-risk. Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738, with the MLP model outperforming logistic regression (LR), Gaussian Naive Bayes (GNB), and the National Early Warning Score (NEWS). SHAP value analysis identified coma state, peripheral capillary oxygen saturation (SpO2), and systolic blood pressure as the top three predictive factors in the MLP model, with coma state exerting the most contribution.

CONCLUSION

Compared with other methods, the MLP model with initial vital signs demonstrated optimal prediction accuracy, highlighting its potential to enhance clinical decision-making in triage in the EDs.

Characteristics of Oral Adverse Effects following COVID-19 Vaccination and Similarities with Oral Symptoms in COVID-19 Patients: Taste and Saliva Secretory Disorders

Although coronavirus disease 2019 (COVID-19) vaccines exhibit diverse side effects, taste and saliva secretory disorders have remained poorly understood despite their negative impact on the overall quality of life. The present study aimed to characterize oral adverse effects following COVID-19 vaccination and assess their similarities with oral symptoms in COVID-19 patients. A literature search was conducted in databases, including PubMed, LitCovid, and Google Scholar, to retrieve relevant studies. The narrative review indicated that a certain number of vaccinated people develop ageusia, dysgeusia, hypogeusia, xerostomia, and dry mouth, while they are rare compared with COVID-19 oral symptoms. The prevalence of oral adverse effects varies by country/region and such geographical differences may be related to the type of vaccine used. Similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 vaccination adversely affects taste perception and salivary secretion in females and older subjects more frequently than in males and younger subjects. Their impairments mostly appear within 3 days of vaccination, and bitter taste is specifically impaired in some cases. Considering that oral adverse effects following COVID-19 vaccination share some characteristics with oral symptoms in COVID-19 patients, it is speculated that the spike protein derived from COVID-19 vaccination and SARS-CoV-2 infection may be pathophysiologically responsible for taste and saliva secretory disorders. This is because such spike protein has the potential to interact with ACE2 expressed on the relevant cells, produce proinflammatory cytokines, and form antiphospholipid antibodies. Our results do not deny the advantages of COVID-19 vaccination, but attention should be paid to post-vaccination oral effects in addition to COVID-19 oral symptoms. Although coronavirus disease 2019 (COVID-19) vaccines exhibit diverse side effects, taste and saliva secretory disorders have remained poorly understood despite their negative impact on the overall quality of life. The present study aimed to characterize oral adverse effects following COVID-19 vaccination and assess their similarities with oral symptoms in COVID-19 patients. A literature search was conducted in databases, including PubMed, LitCovid, and Google Scholar, to retrieve relevant studies. The narrative review indicated that a certain number of vaccinated people develop ageusia, dysgeusia, hypogeusia, xerostomia, and dry mouth, while they are rare compared with COVID-19 oral symptoms. The prevalence of oral adverse effects varies by country/region and such geographical differences may be related to the type of vaccine used. Similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 vaccination adversely affects taste perception and salivary secretion in females and older subjects more frequently than in males and younger subjects. Their impairments mostly appear within 3 days of vaccination, and bitter taste is specifically impaired in some cases. Considering that oral adverse effects following COVID-19 vaccination share some characteristics with oral symptoms in COVID-19 patients, it is speculated that the spike protein derived from COVID-19 vaccination and SARS-CoV-2 infection may be pathophysiologically responsible for taste and saliva secretory disorders. This is because such spike protein has the potential to interact with ACE2 expressed on the relevant cells, produce proinflammatory cytokines, and form antiphospholipid antibodies. Our results do not deny the advantages of COVID-19 vaccination, but attention should be paid to post-vaccination oral effects in addition to COVID-19 oral symptoms.

Characteristics and outcomes of in-hospital patients with Covid-19 and history of tuberculosis: a matched case-control from the Brazilian Covid-19 Registry

BACKGROUND

The Covid-19 pandemic caused a negative impact on other infectious diseases control, prevention, and treatment. Consequently, low and middle-income countries suffer from other endemic diseases, such as tuberculosis. This study was designed to compare Covid-19 manifestations and outcomes between patients with previously treated tuberculosis and controls without this condition.

METHODS

We performed a matched case-control study drawn from the Brazilian Covid-19 Registry data, including in-hospital patients aged 18 and over with laboratory-confirmed Covid-19 from March 1, 2020, to March 31, 2022. Cases were patients with a past history of tuberculosis. Controls were Covid-19 patients without a tuberculosis history. Patients were matched by hospital, sex, presence of HIV, and number of comorbidities, with a 1:4 ratio.

RESULTS

Of 13,636 patients with laboratory-confirmed diagnoses of Covid-19 enrolled in this study, 80 had a history of tuberculosis. Statistical differences in history of chronic pulmonary obstructive disease (15% vs. 3.2%), psychiatric disease (10% vs. 3.5%,), chronic kidney disease (11.2% vs. 2.8%), and solid-organ transplantation; (5% vs. 0.9%, p < 0.05 for all) were higher in patients with a past history of tuberculosis. Prior use of inhalatory medications (5% vs. 0.6%,), oral corticoids (8.8% vs. 1.9%), immunosuppressants (8.8% vs. 1.9%,) and the use of illicit drugs were more common in the case group (6.2% vs. 0.3% p < 0.05for all). There were no significant differences in in-hospital mortality, mechanical ventilation, need for dialysis, and ICU admission.

CONCLUSIONS

Patients with a history of tuberculosis infection presented a higher frequency of use of illicit drugs, chronic pulmonary obstructive disease, psychiatric disease, chronic kidney disease, solid-organ transplantation, prior use of inhalatory medications, oral corticoids, and immunosuppressants. The outcomes were similar between cases and controls.

Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning

Machine learning (ML) is a promising tool in assisting clinical decision-making for improving diagnosis and prognosis, especially in developing regions. It is often used with large samples, aggregating data from different regions and hospitals. However, it is unclear how this affects predictions in local centers. This study aims to compare data aggregation strategies of several hospitals in Brazil with a local training strategy in each hospital to predict two COVID-19 outcomes: Intensive Care Unit admission (ICU) and mechanical ventilation use (MV). The study included 6,046 patients from 14 hospitals, with local sample sizes ranging from 47 to 1500 patients. Machine learning models were trained using extreme gradient boosting, lightGBM, and catboost for structured data. Seven data aggregation strategies based on hospital geographic regions were compared with local training, and the best strategy was determined by analyzing the area under the ROC curve (AUROC). SHAP (Shapley Additive exPlanations) values were used to assess the contribution of variables to predictions. Additionally, a metafeatures analysis examined how hospital characteristics influence the selection of the best strategy. The study found that the local training strategy was the most effective approach, in the case of ICU outcomes, for 11 of the 14 hospitals (79%), and, in the case of MV, for 10 hospitals (71%). Metafeatures analysis suggested that hospitals with smaller sample sizes generally performed better using an aggregated data strategy compared to local training. Our study brings to light an important concern about the impact of grouping data from different hospitals in predictive machine learning models. These findings contribute to the ongoing debate about the trade-off between increasing sample size and bringing together heterogeneous scenarios.

Prediction of COVID-19 Severity at Delivery after Asymptomatic or Mild COVID-19 during Pregnancy

OBJECTIVE

 This study aimed to develop a prediction model that estimates the probability that a pregnant person who has had asymptomatic or mild coronavirus disease 2019 (COVID-19) prior to delivery admission will progress in severity to moderate, severe, or critical COVID-19.

STUDY DESIGN

 This was a secondary analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients who delivered from March through December 2020 at hospitals across the United States. Those eligible for this analysis presented for delivery with a current or previous asymptomatic or mild SARS-CoV-2 infection. The primary outcome was moderate, severe, or critical COVID-19 during the delivery admission through 42 days postpartum. The prediction model was developed and internally validated using stratified cross-validation with stepwise backward elimination, incorporating only variables that were known on the day of hospital admission.

RESULTS

 Of the 2,818 patients included, 26 (0.9%; 95% confidence interval [CI], 0.6-1.3%) developed moderate-severe-critical COVID-19 during the study period. Variables in the prediction model were gestational age at delivery admission (adjusted odds ratio [aOR], 1.15; 95% CI, 1.08-1.22 per 1-week decrease), a hypertensive disorder in a prior pregnancy (aOR 3.05; 95% CI, 1.25-7.46), and systolic blood pressure at admission (aOR, 1.04; 95% CI, 1.02-1.05 per mm Hg increase). This model yielded an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.72-0.91).

CONCLUSION

 Among individuals presenting for delivery who had asymptomatic-mild COVID-19, gestational age at delivery admission, a hypertensive disorder in a prior pregnancy, and systolic blood pressure at admission were predictive of delivering with moderate, severe, or critical COVID-19. This prediction model may be a useful tool to optimize resources for SARS-CoV-2-infected pregnant individuals admitted for delivery.

KEY POINTS

· Three factors were associated with delivery with more severe COVID-19.. · The developed model yielded an area under the receiver operating characteristic curve of 0.82 and model fit was good.. · The model may be useful tool for SARS-CoV-2 infected pregnancies admitted for delivery..

Midterm Outcomes of Right Anterior Mini Thoracotomy Aortic Valve Replacement

Background

Contemporary surgical approaches for aortic valve replacement (AVR) include full median sternotomy, hemi-sternotomy, and a right anterior mini thoracotomy (RAMT) approach. We report the midterm outcomes of RAMT for isolated AVR.

Methods

A retrospective study was conducted, reporting the midterm outcomes of patients who underwent isolated RAMT AVR. The primary outcomes were death and disabling stroke within 30-days of surgery. The secondary outcomes were survival at latest follow-up assessment, hospital readmission for aortic valve disease, prosthetic valve function, and incidence of structural valve deterioration requiring reintervention on the aortic valve.

Results

Seventy patients underwent isolated RAMT AVR between February 2016 and February 2018. One patient died from a cardiac cause within 30 days of surgery, whereas none experienced disabling postoperative strokes. The mean follow-up period for the cohort was 74.46 ± 7.54 months. At 95 months, a total of 49 patients were alive. During the follow-up period, 2 patients underwent median sternotomy, 1 for mitral valve replacement and tricuspid repair, and 1 for coronary artery bypass grafting. At last follow-up assessment, the average mean transvalvular gradient was 12.11 ± 9.15 mm Hg. One patient developed prosthetic valve infective endocarditis, and 1 patient was found to have prosthetic valve thrombosis. Prosthetic valve function was normal in 66 patients. At 95 months, freedom from aortic valve reintervention was 98.6%, as 1 patient required redo aortic root surgery.

Conclusions

RAMT AVR can be done safely in the appropriate patient population. Midterm outcomes at our centre are promising, and they suggest that this approach is a good option for managing aortic stenosis.

Application of artificial intelligence in triage in emergencies and disasters: a systematic review

INTRODUCTION AND OBJECTIVE

Modern and intelligent triage systems are used today due to the growing trend of disasters and emergencies worldwide and the increase in the number of injured people facing the challenge of using traditional triage methods. The main objective of this study is to investigate the application of artificial intelligence and Technology in the triage of patients injured by disasters and emergencies and the challenges of the implementation of intelligent triage systems.

METHOD

The present study is a systematic review and follows PRISMA guidelines. The protocol of this study was registered in PROSPERO with the code CRD42023471415. To find relevant studies, the databases PubMed, Scopus and Web of Science (ISI) were searched without a time limit until September 2024. The scientific search engine Google Scholar and the references of the final articles were read manually for the final review.

RESULTS

The search identified 2,630 articles, narrowing down to 19 high-quality studies on AI in triage, which improved patient care through optimized resource management and real-time data transmission. AI algorithms like OpenPose and YOLO enhanced efficiency in mass casualty incidents, while e-triage systems allowed for continuous vital sign monitoring and faster triaging. AI tools demonstrated high accuracy in diagnosing COVID-19 (94.57%). Implementing intelligent triage systems faced challenges such as trust issues, training needs, equipment shortages, and data privacy concerns.

CONCLUSION

Developing assessment systems using artificial intelligence enables timely treatment and better resuscitation services for people injured in disasters. For future studies, we recommend designing intelligent triage systems to remove the obstacles in triaging children and disabled people in disasters.

Clinical epidemiological and laboratory investigation in co-infection with COVID-19 and tuberculosis

Currently, COVID-19 and tuberculosis (TB) are the deadliest infectious diseases worldwide. Their synergy, form of presentation, morbidity, and mortality are data that have been scarcely explored. Thus, this study aimed to characterize the clinical, epidemiological, and laboratory factors of this co-infection and to analyze the factors associated with the active TB among COVID-19 cases. A case-control study was conducted with a retrospective survey of 21 laboratory-confirmed COVID-19/TB co-infected patients (case group) and 21 COVID-19 patients (control group). The study included participants from eight hospitals in Campo Grande city, capital of Mato Grosso do Sul State, Brazil, from March 2020 to March 2022. Association analysis and binomial logistic regression were employed with statistical significance set at p≤0.05. From the 21 identified cases of COVID-19/TB co-infection, we found a more frequent association with HIV infection than the control-group, without worsening the outcome. COVID-19/TB patients had less dyspnea and less need for mechanical ventilation compared to the cases with COVID-19 only. On the other hand, COVID-19/TB patients had higher levels of C-reactive protein and lower hemoglobin levels, the latter variable was independently associated with COVID-19/TB. Among the clinical differences presented among COVID-19/TB co-infected patients, despite the association with HIV and lower clinical repercussions, only lower hemoglobin levels were associated with COVID-19/TB.

Hospital costs of COVID-19, post-COVID-19 condition and other viral pneumonias: a cost comparison analysis

OBJECTIVES

To compare hospital admission costs for coronavirus disease 2019 (COVID-19) cases to hospital admission costs for other viral pneumonia cases in Australia, and to describe hospital admission costs for post-COVID-19 condition.

DESIGN, SETTING, PARTICIPANTS

A cost comparison analysis of hospital admissions due to COVID-19 or other viral pneumonias between 1 January 2020 and 30 June 2021 at Victorian public health acute and subacute services.

MAIN OUTCOME MEASURES

Demographic characteristics, clinical outcomes (including diagnoses, impairment, subacute admission, intensive care unit admissions, ventilation, and length of stay) and cost data (including diagnostic-related groups, and total, direct and indirect costs).

RESULTS

During the study period, 3197 patients were admitted to hospital due to COVID-19 and 15 761 were admitted for other viral pneumonias. Admissions for COVID-19 cost 29% more than admissions for other viral pneumonias. Admissions for COVID-19 requiring intensive care unit admission incurred significantly higher mean costs (A$120 504 or US$90 595) compared with those not requiring intensive care unit admission (A$19 634 or US$14 761). The adjusted cost of admissions related to post-COVID-19 condition was A$11 090 or US$8 337, and these admissions were significantly more likely to be elective. Direct costs accounted for most of the costs for all groups, and admissions for post-COVID-19 condition used less allied health services than other groups.

CONCLUSIONS

Given its recent emergence, cases of acute COVID-19 and post-COVID-19 condition have had a significant additional financial impact on Australian hospitals. Further studies are required to understand long term costs and identify trends over time in the context of increased vaccination rates and subsequent variants of severe acute respiratory syndrome coronavirus 2.

Early Elevated Inflammatory Markers in SARS-CoV-2 Vaccinated Patients Are Associated with Reduced Mortality, Morbidity, and Lung Injury

Background The development of vaccines against SARS-CoV-2 has proved to be a highly successful strategy. In this work, the aim is to study the effects of the SARS-CoV-2 vaccine on the production of inflammatory markers and how this affect morbidity and mortality. Electronic medical record (EMR) data from 210 patients diagnosed with COVID-19 from November 2020 to June 2021 were collected. The admitted patients were divided into three groups, the one-dose vaccinated, two-dose vaccinated, and the non-vaccinated. All patients were moderate or severe in disease level as defined by the WHO classification. The results show that CRP was 101 ± 5.3, 97 ± 10.8, and 145 ± 17.3 (p < 0.05), fibrinogen 529 ± 16.3, 397 ± 33.8, and 610 ± 15 (p < 0.05), D-dimer 1244 ± 89, 1279 ± 297, and 1615 ± 224 (p < 0.05), ferritin was 1170 ± 122, 999 ± 202, and 1663 ± 409 (p < 0.05), IL-6 was 196 ± 12, 96 ± 5, and 580 ± 402 (NS), for the non-vaccinated, one-dose vaccinated, and two-dose vaccinated groups, respectively. The high level of CRP up to 150-200 mg/dL was more common among the surviving vaccinated patients. Oxygen supplementation, mechanical ventilation, and mortality were higher in the non-vaccinated group. Blood urea nitrogen (BUN) level was higher in the vaccinated patients, 25 ± 0.14 vs. 33 ± 6.15, respectively (p < 0.05). Inflammation markers were significantly higher in the vaccinated groups compared to non-vaccinated groups. On the other hand, extremely high levels of CRP (>200 mg/dL) were correlated with high mortality incidence.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

BACKGROUND

 Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

METHODS

 Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

RESULTS

 Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

CONCLUSION

 ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Photobiomodulation for the prevention and treatment of oral mucositis in patients submitted to hematopoietic stem cell transplantation: health quality evaluation

OBJECTIVE

To evaluate the quality of oral health care through indicators in patients undergoing hematopoietic stem cell transplantation for the management of oral mucositis.

METHODS

Thirty-five patients were evaluated. Photobiomodulation was performed during the conditioning regimen, 1 day, 5 days, and 10 days after transplantation. Four process indicators and 13 outcome indicators were used to evaluate the effectiveness of the intervention, according to SQUIRE 2.0.

RESULTS

All process indicators demonstrated a compliance rate of 100% to the desired standard. Outcome indicators revealed that 66.6% of patients experienced mucositis during at least one follow-up period. A statistically significant increase was observed between periods of 1 and 5 days post-transplant, as well as between 1 and 10 days post-transplant (p < 0.05), with a predominance of grade I mucositis (p = 0.014). Four patients (16.7%) reported feeling pain, occurring between 5 and 10 days after transplantation, with moderate pain being the most prevalent. Oral mucositis did not show a statistically significant association with pain, associated treatments, leukopenia, comorbidities, or type of transplant.

CONCLUSIONS

The indicators demonstrated their suitability for evaluating oral health in both the prevention and treatment of oral mucositis in these patients. Furthermore, the effectiveness of photobiomodulation in improving the quality of oral health in the patients studied was confirmed.

Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study

COVID-19 is an infectious disease that caused a global pandemic in 2020. In the critical moments of this healthcare emergencies, the medical staff needs to take important decisions in a context of limited resources that must be carefully managed. To this end, the computer-aided diagnosis methods are extremely powerful and help them to better recognize the evidences of high-risk patients. This can be done with the support of relevant information extracted from electronic health records, lab tests and imaging studies. In this work, we present a novel fully-automatic efficient method to help the clinical decision-making process in the context of COVID-19 risk estimation, using multimodal data fusion of clinical features and deep features extracted from chest X-ray images. The risk estimation is studied in two of the most relevant and critical encountered scenarios: the risk of hospitalization and mortality. This study shows which are the most important features for each scenario, the ratio of clinical and imaging features present in the top ranking and the performance of the used machine learning models. The results demonstrate a great performance by the classifiers, estimating the risk of hospitalization with an AUC-ROC of 0.8452 ± 0.0133 and the risk of death with an AUC-ROC of 0.8285 ± 0.0210, only using a subset of the original features, and highlight the significant contribution of imaging features to hospitalization risk assessment, while clinical features become more crucial for mortality risk evaluation. Furthermore, multimodal data fusion can outperform the approaches that use one data source. Despite the model's complexity, it requires fewer features, an advantage in scenarios with limited computational resources. This streamlined, fully-automated method shows promising potential to improve the clinical decision-making process and better manage medical resources, not only in the context of COVID-19, but also in other clinical scenarios.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study

BACKGROUND

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes.

OBJECTIVE

This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data.

METHODS

The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets.

RESULTS

Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period.

CONCLUSIONS

We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

The SpO2/FiO2 Ratio Combined with Prognostic Scores for Pneumonia and COVID-19 Increases Their Accuracy in Predicting Mortality of COVID-19 Patients

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study's primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p < 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p < 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy.

Mitochondrial dysfunction in long COVID: mechanisms, consequences, and potential therapeutic approaches

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has introduced the medical community to the phenomenon of long COVID, a condition characterized by persistent symptoms following the resolution of the acute phase of infection. Among the myriad of symptoms reported by long COVID sufferers, chronic fatigue, cognitive disturbances, and exercise intolerance are predominant, suggesting systemic alterations beyond the initial viral pathology. Emerging evidence has pointed to mitochondrial dysfunction as a potential underpinning mechanism contributing to the persistence and diversity of long COVID symptoms. This review aims to synthesize current findings related to mitochondrial dysfunction in long COVID, exploring its implications for cellular energy deficits, oxidative stress, immune dysregulation, metabolic disturbances, and endothelial dysfunction. Through a comprehensive analysis of the literature, we highlight the significance of mitochondrial health in the pathophysiology of long COVID, drawing parallels with similar clinical syndromes linked to post-infectious states in other diseases where mitochondrial impairment has been implicated. We discuss potential therapeutic strategies targeting mitochondrial function, including pharmacological interventions, lifestyle modifications, exercise, and dietary approaches, and emphasize the need for further research and collaborative efforts to advance our understanding and management of long COVID. This review underscores the critical role of mitochondrial dysfunction in long COVID and calls for a multidisciplinary approach to address the gaps in our knowledge and treatment options for those affected by this condition.

COVID-19 Breakthrough Infections Among People With HIV: A Statewide Cohort Analysis

OBJECTIVES

This study aims to identify COVID-19 breakthrough infections among people with HIV (PWH) across different phases of the pandemic and explore whether differential immune dysfunctions are associated with breakthrough infections.

DESIGN AND METHODS

This retrospective population-based cohort study used data from an integrated electronic health record (EHR) database in South Carolina (SC). Breakthrough infection was defined as the first COVID-19 diagnosis documented in the state agency after the date an individual was fully vaccinated (ie, 2 doses of Pfizer/BNT162b2 or Moderna/mRNA-1273, or 1 dose of Janssen/Ad26.COV2.S) through June 14, 2022. We analyzed the risk and associated factors of the outcome using Cox proportional hazards models.

RESULTS

Among 7596 fully vaccinated PWH, the overall rate of breakthrough infections was 118.95 cases per 1000 person-years. When compared with the alpha-dominant period, the breakthrough infection rate was higher during both delta-dominant (HR: 1.50; 95% CI: 1.25 to 1.81) and omicron-dominant (HR: 2.86; 95% CI: 1.73 to 4.73) periods. Individuals who received a booster dose had a lower likelihood of breakthrough infections (HR: 0.19; 95% CI: 0.15 to 0.24). There was no association of breakthrough infections with degree of HIV viral suppression, but a higher CD4 count was significantly associated with fewer breakthroughs among PWH (>500 vs <200 cells/mm3: HR: 0.68; 95% CI: 0.49 to 0.94).

CONCLUSIONS

In our PWH population, the incidence of breakthrough infections was high (during both delta-dominant and omicron-dominant periods) and mainly associated with the absence of a booster dose in patients older than 50 years, with comorbidities and low CD4 count.

Experience of primary care nurses applying nurse-led management of patients with acute minor illnesses

AIM

The aim of this study is to determine the experience of primary health care nurses regarding the application of nurse-led management in patients with acute minor illnesses.

BACKGROUND

Nursing leadership of care for acute minor illnesses is a new challenge faced by nurses in Spain.

DESIGN

Qualitative, hermeneutical, interpretive phenomenological approach is used. The Consolidated criteria for reporting qualitative research guidelines were applied.

METHODS

Twenty primary care nurses participated; three focus group discussions and nine semi-structured interviews were conducted between November 2019 and October 2020. All the focus group discussions and interviews were recorded, transcribed verbatim and analysed using content analysis.

RESULTS

Seven main themes emerged from the focus group discussions and interviews: concept, perception of the other actors, practice, history and social context, competencies, training, and legality.

CONCLUSION

The study shows the diversity and complexity of nurses' experience when applying nurse-led management in acute minor illnesses. This work has helped to show the gaps perceived by nurses, including the lack of training in the treatment of conditions historically attended by physicians, the lack of definition of the legal framework and the limitations on nurse prescribing. It also highlighted the power of the nursing profession in terms of autonomy, competencies and role expansion.

Comprehensive statistical analysis reveals significant benefits of COVID-19 vaccination in hospitalized patients: propensity score, covariate adjustment, and feature importance by permutation

BACKGROUND

COVID-19 vaccines effectively prevent infection and hospitalization. However, few population-based studies have compared the clinical characteristics and outcomes of patients hospitalized for COVID-19 using advanced statistical methods. Our objective is to address this evidence gap by comparing vaccinated and unvaccinated patients hospitalized for COVID-19.

METHODS

This retrospective cohort included adult COVID-19 patients admitted from March 2021 to August 2022 from 27 hospitals. Clinical characteristics, vaccination status, and outcomes were extracted from medical records. Vaccinated and unvaccinated patients were compared using propensity score analyses, calculated based on variables associated with vaccination status and/or outcomes, including waves. The vaccination effect was also assessed by covariate adjustment and feature importance by permutation.

RESULTS

From the 3,188 patients, 1,963 (61.6%) were unvaccinated and 1,225 (38.4%) were fully vaccinated. Among these, 558 vaccinated individuals were matched with 558 unvaccinated ones. Vaccinated patients had lower rates of mortality (19.4% vs. 33.3%), invasive mechanical ventilation (IMV-18.3% vs. 34.6%), noninvasive mechanical ventilation (NIMV-10.6% vs. 22.0%), intensive care unit admission (ICU-32.0% vs. 44.1%) vasoactive drug use (21.1% vs. 32.6%), dialysis (8.2% vs. 14.7%) hospital length of stay (7.0 vs. 9.0 days), and thromboembolic events (3.9% vs.7.7%), p < 0.05 for all. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and in-hospital mortality (adjusted odds ratio [aOR] = 0.42, 95% confidence interval [CI]: 0.31-0.56; p < 0.001) as well as IMV (aOR = 0.40, 95% CI: 0.30-0.53; p < 0.001). These results were consistent in all analyses, including feature importance by permutation.

CONCLUSION

Vaccinated patients admitted to hospital with COVID-19 had significantly lower mortality and other severe outcomes than unvaccinated ones during the Delta and Omicron waves. These findings have important implications for public health strategies and support the critical importance of vaccination efforts, particularly in low-income countries, where vaccination coverage remains suboptimal.

Photobiomodulation therapy on chemo- and radiotherapy induced oral conditions: an umbrella review

OBJECTIVES

Photobiomodulation (PBM) is a laser-based therapy used to promote tissue repair, reduce inflammation and pain, and has been extensively studied in chemo- and radiotherapy-induced oral mucositis (OM). This review examines the level of evidence of systematic reviews (SRs) that have investigated PBM in such cases of OM.

MATERIALS AND METHODS

SRs evaluating PBM for both the treatment and prevention of OM in patients undergoing chemotherapy and/or radiotherapy and published before November 30, 2023, on PubMed, Cochrane, Embase, Web of Science, LILACS, TRIP and Open Grey databases were eligible for inclusion. We assessed the level of methodological and meta-analytic procedures.

RESULTS

Of the 1201 SRs, 21 that met the inclusion criteria were included. The quality of evidence was assessed using the Assessing the Measurement Tool to Assess Systematic Reviews (AMSTAR2), and the majority was of critically low quality (n = 15, 71.4%) with only 28.5% of low quality. A total of 40 meta-analytic estimates were obtained and analyzed. Approximately 87.5% of the meta-analysis were significant (n = 33), but only one meta-analyses had a strength of "highly suggestive", while the rest were classified as "weak". When analyzing the overlap values, the covered area was 12.14% and the corrected covered area was 7.75%, indicating a moderate overlap. Only 4 SRs had a very high overlap and one had a high overlap.

CONCLUSION

The efficacy of PBM in the treatment of chemotherapy-induced OM is supported by low to critically low quality SRs and meta-analysis of low strength. This review highlights important areas that need to be addressed in future research on this topic.

REGISTRATION

CRD42023484013 (PROSPERO).