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9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%.
Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources.
Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources.
High prescribers of antibiotics and opioids are an important target for stewardship interventions. The goal of this study was to assess the association between high antibiotic and high opioid prescribing by provider type.
A national cross-sectional study.
2015-2017 Department of Veterans Affairs (VA) electronic health record data.
Prescribers were identified as dentists (2017 n=1346) and medical providers (physicians n=23,072; advanced practice providers [APP] n=7705; and other providers [pharmacists/chiropractors] n=3674) (2017 n=34,451).
High prescribing was defined as being in the top 25% of visit-based rates of antibiotic or opioid prescribing (number of prescriptions/number of dental or medical visits). Multivariable random effects logistic regression with clustering by facility was used to assess the adjusted association between high antibiotic and opioid prescribing.
Medical providers prescribed 4,348,670 antibiotic and 10,256,706 opioid prescriptions; dentists prescribed 277,170 antibioticip interventions targeting both medication classes may have higher impact to efficiently reduce prescribing of medications with high public health impact. Provider-targeted interventions are needed to improve antibiotic and opioid prescribing in both dentists and medical providers.
High antibiotic prescribing was associated with high opioid prescribing. Thus, stewardship interventions targeting both medication classes may have higher impact to efficiently reduce prescribing of medications with high public health impact. Provider-targeted interventions are needed to improve antibiotic and opioid prescribing in both dentists and medical providers.
The comparative safety and efficacy of maintenance mycophenolate mofetil (MMF) and cyclosporine (CYC) following rituximab (RTX) in children with steroid-resistance nephrotic syndrome are uncertain.
Multicenter randomized controlled trial.
Sixty-six children between 2 and 6 years of age with SRNS.
Patients were randomized to receive either MMF 1000 mg/m
/day (n=32) or CYC 5mg/kg/day (n=34) for 12months following RTX induction therapy (375 mg/m
) given as needed for B-cell count.
Complete remission and adverse events (AEs).
Complete remission was observed in 26 patients (83.1%) in the MMF group compared with 21 patients (61.7%) in the CYC group (p=0.02). The median time to remission was shorter in the MMF group than in the CYC group (2.64 vs. 3.4 months; hazard ratio [HR], 0.61; 95% CI, 0.74-0.90, p=0.03). The median time to first relapse was longer in the MMF group compared with the CYC group (10.8 vs. 8.0 months; HR, 1.12; 95% CI, 1.31-1.54, p=0.01), and this was significantly correlated with the median time to B-cell recovery in the two groups (8.6 vs. 5.2 months in MMF and CYC, respectively, p=0.02). The overall incidence of adverse drug events was lower in the MMF group compared with the CYC group (59.3% vs. 76.4%, p=0.03).
MMF-RTX is superior to CYC-RTX in maintaining remission with fewer AEs in children with initial SRNS. Additional high-quality randomized control trials with long-term follow-up are needed to identify the long-term potential complications.
MMF-RTX is superior to CYC-RTX in maintaining remission with fewer AEs in children with initial SRNS. Additional high-quality randomized control trials with long-term follow-up are needed to identify the long-term potential complications.Vancomycin is commonly used to treat methicillin-resistant Staphylococcus aureus infections and is known to cause nephrotoxicity. Previous Vancomycin Consensus Guidelines recommended targeting trough concentrations but the 2020 Guidelines suggest monitoring vancomycin area under the curve (AUC) given the reduced risk of acute kidney injury (AKI) at similar levels of efficacy. This meta-analysis compares vancomycin-induced AKI incidence using AUC-guided dosing strategies versus trough-based monitoring. Literature was queried from Medline (Ovid), Web of Science, and Google Scholar from database inception through November 5, 2021. Interventional or observational studies reporting the incidence of vancomycin-induced AKI between AUC- and trough-guided dosing strategies were included. In the primary analysis, the Vancomycin Consensus Guidelines definition for AKI was used if reported; otherwise, the Risk, Injury, and Failure; and Loss, and End-stage kidney disease (RIFLE) or Kidney Disease Improving Global Outcomes with a lower incidence of vancomycin-induced AKI versus trough-guided dosing strategies (GRADE, low). Limitations included the variety of AKI definitions and the potential for confounding bias.Plant defense against herbivores is multidimensional, and investment into different defense traits is intertwined due to genetic, physiological, and ecological costs. This relationship is expected to generate a trade-off between direct defense and tolerance that is underlain by resource availability, with increasing resources being associated with increased investment in tolerance and decreased investment in direct resistance. We tested these predictions across populations of the shrub Artemisia californica by growing plants sourced from a latitudinal aridity gradient within common gardens located at the southern (xeric) and northern (mesic) portions of its distribution. We measured plant growth rate, resistance via a damage survey, and tolerance to herbivory by experimentally simulating vertebrate herbivory. Plants from more northern (vs. southern) environments were less resistant (received higher percent damage by vertebrate herbivores) and tended to be more tolerant (marginally significant) with respect to change in biomass measured 12 months after simulated vertebrate herbivory. Also, putative growth and defense traits paralleled patterns of resistance and tolerance, such that leaves from northern populations contained lower concentrations of terpenes and increased N, specific leaf area, and % water. Last, plant growth rate did not demonstrate clear clinal patterns, as northern populations (vs. southern populations) grew more slowly in the southern (xeric) garden, but there was no clinal relationship detected in the northern (mesic) garden. Overall, our findings support the prediction of lower resistance and higher tolerance in plant populations adapted to more resource-rich, mesic environments, but this trade-off was not associated with concomitant trade-offs in growth rate. These findings ultimately suggest that plant adaptation to resource availability and herbivory can shape intraspecific variation in multivariate plant defenses.
Dentistry professionals may experience significantly higher occupational stress than other health professionals and dentistry academics may have specific work content and context sources of stress.
The aim of this study is to identify common sources of occupational stress, and how these are associated with wellbeing, in dentistry academics.
A cross-sectional online survey with staff in Dentistry departments in Australia and New Zealand. Assessment included 23 items from five general domains of occupational stress from the NIOSH-Generic Job Stress Questionnaire, a 23-item list of sources of stress and the 22-item Psychological General Well-Being Index. Analyses used descriptive statistics and multiple linear regression.
A total of 107 respondents (average age 50 ± 11.7 years, 56.8% men) completed the survey. Leading sources of occupational stress were job future, time pressure at work, work overload, and administration demands. A multiple linear regression model significantly predicted wellbeing, F(8,77)=13.141, p =.000, adj.R
=.53, but there were no significant associations for any of the specific sources of stress.
The combination of time pressure, workload and responsibility, job dissatisfaction, low social support, and uncertain job future was inversely associated with wellbeing amongst these dentistry academics. Future studies should consider the development and evaluation of interventions to address these concerns.
The combination of time pressure, workload and responsibility, job dissatisfaction, low social support, and uncertain job future was inversely associated with wellbeing amongst these dentistry academics. Future studies should consider the development and evaluation of interventions to address these concerns.
Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings.
Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier.
The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI] 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. signaling pathway The regression model that predicted ARDS with 71% (95% CI 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator.
We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.
We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.
