Given the increased use of hydroxychloroquine (HCQ), chloroquine (CQ), and azithromycin (AZM) during the early months of the coronavirus disease 2019 (COVID-19) pandemic, there is a need to evaluate the associated safety concerns. The objective of this study was to summarize the adverse drug events (ADEs) associated with HCQ, CQ, and AZM use during the national COVID-19 emergency and compare the results with known adverse reactions listed in the drugs' package inserts.
Background: Logistic regression-based signal detection algorithms have benefits over disproportionality analysis due to their ability to handle potential confounders and masking factors. Feature exploration and developing alternative machine learning algorithms can further strengthen signal detection. Objectives: Our objective was to compare the signal detection performance of logistic regression, gradient-boosted trees, random forest and support vector machine models utilizing Food and Drug Administration adverse event reporting system data. Design: Cross-sectional study. Methods: The quarterly data extract files from 1 October 2017 through 31 December 2020 were downloaded. Due to an imbalanced outcome, two training sets were used: one stratified on the outcome variable and another using Synthetic Minority Oversampling Technique (SMOTE). A crude model and a model with tuned hyperparameters were developed for each algorithm. Model performance was compared against a reference set using accuracy, precision, F1 score, recall, the receiver operating characteristic area under the curve (ROCAUC), and the precision-recall curve area under the curve (PRCAUC). Results: Models trained on the balanced training set had higher accuracy, F1 score and recall compared to models trained on the SMOTE training set. When using the balanced training set, logistic regression, gradient-boosted trees, random forest and support vector machine models obtained similar performance evaluation metrics. The gradient-boosted trees hyperparameter tuned model had the highest ROCAUC (0.646) and the random forest crude model had the highest PRCAUC (0.839) when using the balanced training set. Conclusion: All models trained on the balanced training set performed similarly. Logistic regression models had higher accuracy, precision and recall. Logistic regression, random forest and gradient-boosted trees hyperparameter tuned models had a PRCAUC ⩾ 0.8. All models had an ROCAUC ⩾ 0.5. Including both disproportionality analysis results and additional case report information in models resulted in higher performance evaluation metrics than disproportionality analysis alone.
In 2003, the South Carolina Department of Health and Environmental Control established the Carolina Antibiotic Resistance Surveillance System (CARSS), an active sentinel surveillance system for antibiotic-resistant Streptococcus pneumoniae.CARSS includes twelve hospitals. Each hospital was assigned a weighted sample size. Minimum inhibitory concentrations were determined using the E-test method.A total of 452 isolates were collected. The prevalence of penicillin nonsusceptibility in the study was 44.9%. Penicillin intermediate resistance (PCN-I) was 33.2%, and penicillin high-level resistance (PCN-R) was 11.7%. One hundred six (23.5%) isolates were nonsusceptible to one antibiotic. One hundred twenty-four (27.4%) isolates were nonsusceptible to three or more antibiotics.CARSS confirmed the prevalences of antibiotic nonsusceptibility previously reported for South Carolina. However, CARSS suggests resistance is shifting from PCN-R to PCN-I in South Carolina. There is a high prevalence of multidrug nonsusceptibility in South Carolina. CARSS will continue to monitor these trends.
In 1998, the South Carolina Department of Health and Environmental Control surveyed clinical microbiology laboratories statewide to determine the prevalence of antibiotic nonsusceptibility among isolates of Streptococcus pneumoniae. A follow-up study was conducted in 2001.A cross-sectional study was conducted to estimate the prevalence of penicillin nonsusceptibility (PCN-N), extended-spectrum cephalosporin nonsusceptibility (ESC-N), and levofloxacin nonsusceptibility (LEV-N) in South Carolina. A standardized questionnaire was mailed to 89 laboratories.The prevalence of penicillin intermediate resistance increased from 1998 (17.6%) to 2000 (20.9%, chi2 P = 0.008). Furthermore, the prevalence of PCN-N increased from 1998 (34.5%) to 2000 (38.4%, chi2 P = 0.01). The prevalence of ECN-N decreased from 1998 (19.1%) to 2000 (17.7%), but the difference was not significant (chi2 P = 0.25).The laboratory survey was a low-cost method of estimating the change in prevalence of antibiotic nonsusceptibility, and it emphasizes regional surveillance because the prevalence of antibiotic nonsusceptibility varied geographically.
Many signal detection algorithms give the same weight to information from all products and patients, which may result in signals being masked or false positives being flagged as potential signals. Subgrouped analysis can be used to help correct for this.
The pharmacology, antimicrobial activity, pharmacokinetics, pharmacodynamics, clinical efficacy, safety, and place in therapy of ceftobiprole are reviewed.Ceftobiprole, a novel, broad-spectrum, parenteral cephalosporin, inhibits the cell-wall synthesis of penicillin-binding proteins (PBPs) PBP2a and PBP2x, responsible for the resistance in staphylococci and pneumococci, respectively. Ceftobiprole has good activity against gram-positive aerobes and anaerobes, and its activity against gram-negative aerobes and anaerobes is species dependent. Ceftobiprole is relatively inactive against Acinetobacter species. Its ability to bind relevant PBPs of resistant gram-positive and gram-negative bacteria indicates its potential use in the treatment of hospital-acquired pneumonia and complicated skin and skin-structure infections (cSSSIs). Ceftobiprole is primarily excreted unchanged by the kidneys and exhibits linear pharmacokinetics. The half-life of the drug is approximately 3-4 hours. It exhibits minimal plasma protein binding (16%). Ceftobiprole does not inhibit the cytochrome P-450 isoenzyme system, so the possibility of drug-drug interactions is low. The drug has not been approved for use in the United States but has been approved in Canada and elsewhere. Ceftobiprole is currently undergoing Phase III clinical trials and has demonstrated activity against methicillin-resistant Staphylococcus aureus, penicillin-resistant Streptococcus pneumoniae, and Pseudomonas aeruginosa. Completed Phase III trials used i.v. dosages of 500 mg every 8-12 hours. The most commonly observed adverse effects of ceftobiprole included headache and gastrointestinal upset.Ceftobiprole is a novel, broad-spectrum, parenteral cephalosporin undergoing Phase III clinical trials. Its broad spectrum of activity makes it a candidate for monotherapy of cSSSIs and pneumonias that have required combination therapy in the past.
Background: The SUSTAIN-6 trial showed significantly higher rates of retinopathy complications in the semaglutide group compared to placebo. Observational studies have not consistently corroborated this finding, raising questions about the appropriateness of composite variables and whether the relationship exists across the entire drug class or is limited to individual glucagon-like peptide 1 agonists (GLP-1RAs). The study objective was to evaluate the difference between using individual and composite terms to assess associations between GLP-1RAs and diabetic retinopathy events.Research Design and Methods: Reports from the US Food and Drug Administration Adverse Event Reporting System were utilized to examine relationships between GLP-1RAs and diabetic retinopathy events. A disproportionality analysis was conducted using the proportional reporting ratio.Results: Four GLP-1RAs demonstrated signals for diabetic retinopathy events. The GLP-1RA drug class had four diabetic retinopathy signals. Only semaglutide had a signal for the composite diabetic retinopathy outcome. The GLP-1RA drug class and the composite diabetic retinopathy outcome did not meet the PRR signal thresholds.Conclusions: The use of drug class level and composite outcome variables may mask diabetic retinopathy signals in comparison to individual drug assessments. Our results support the SUSTAIN-6 trial findings and suggest an association between four GLP-1RAs and diabetic retinopathy events.
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