Background: Candesartan cilexetil is a widely used angiotensin II receptor blocker with minimal adverse effects and high tolerability for the treatment of hypertension. Candesartan is administered orally as the prodrug candesartan cilexetil, which is wholly and swiftly converted to the active metabolite candesartan by carboxylesterase during absorption in the intestinal tract. In populations with renal or hepatic impairment, candesartan's pharmacokinetic (PK) behavior may be altered, necessitating dosage adjustments.
Objectives: This study was conducted to examine how the physiologically based PK (PBPK) model characterizes the PKs of candesartan in adult and geriatric populations and to predict the PKs of candesartan in elderly populations with renal and hepatic impairment.
Design: After developing PBPK models using the reported physicochemical properties of candesartan and clinical data, these models were validated using data from clinical investigations involving various dose ranges.
Methods: Comparing predicted and observed blood concentration data and PK parameters was used to assess the fit performance of the models.
Results: Doses should be reduced to approximately 94% of Chinese healthy adults for the Chinese healthy elderly population; approximately 92%, 68%, and 64% of that of the Chinese healthy adult dose in elderly populations with mild, moderate, and severe renal impairment, respectively; and approximately 72%, 71%, and 52% of that of the Chinese healthy adult dose in elderly populations with Child-Pugh-A, Child-Pugh-B, and Child-Pugh-C hepatic impairment, respectively.
Conclusion: The results suggest that the PBPK model of candesartan can be utilized to optimize dosage regimens for special populations.
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.
Background and objective: Drug-related problems (DRPs) are often seen when a patient is transitioning from one healthcare sector to another, for example, when a patient moves from the hospital to a General Practice (GP) setting. This transition creates an opportunity for information on medication changes and follow-up plans to be lost. A cross-sectoral hospital pharmacist intervention was developed and pilot-tested in a large GP clinic. The intervention included medication history, medication reconciliation, medication review, follow-up telephone calls, identification of possible DRPs and communication with the GP. It is unknown whether the intervention is transferable to other GP clinics. The aim of the study was to explore similarities and differences between GP clinics in descriptive data and intervention acceptability.
Methods: A convergent mixed methods study design was used. The intervention was tested in four GP clinics with differing characteristics. Quantitative data on the GP clinics, patients and pharmacist activities were collected. Qualitative data on the acceptability were collected through focus group interviews with general practitioners, nurses and pharmacists. The Theoretical Framework of Acceptability was used.
Results: Overall, the intervention was found acceptable and relevant by all. There were differences between the GP clinics in terms of size, daily physician work form and their use of pharmacists for ad hoc tasks. There were similarities in patient characteristics across GP clinics. Therefore, the intervention was found equally relevant for all of the clinics. Shared employment with unique access to health records in both sectors was important in the identification and resolution of DRPs. Economy was a barrier for further implementation.
Conclusions: The intervention was found acceptable and relevant by all; therefore, it was considered transferable to other GP clinics. Hospital pharmacists were perceived to be relevant healthcare professionals to be utilized in GP, in hospitals and in the cross-sectoral transition of patients.
Background: The circulation of falsified medical products is a global threat and is expected to be higher in low- and middle-income countries.
Objective: This study was conducted to assess the understanding, readiness, and response of Eritrea's healthcare professionals (HCPs), and identify potential areas of intervention to combat circulation of falsified medical products.
Design: This was a nationwide population-based cross-sectional survey, conducted in December 2021.
Methods: This study enrolled representative samples of HCPs working in public and private health facilities. Two-stage stratified cluster sampling was used to select study participants and data were collected through face-to-face interviews. Descriptive statistics, Mann-Whitney U test, Kruskal-Wallis test along with their post hoc tests, Jonckheere-Terpstra, and logistic regression analyses were performed as appropriate.
Results: The study enrolled 707 HCPs, and 96.6% were successfully surveyed. The majority of the participants (62.5%) encountered products with suspected quality defects and 63.8% claimed that they had reported the incident(s) at least once. About 85% reported that complaints should be submitted to the Eritrean Pharmacovigilance Centre and 74.0% indicated that it should be reported at the earliest time possible even if the reporter lacks details. The standard reporting form for suspected product quality issues was correctly recognized by 13.8%. Overall, the median knowledge and attitude scores were found to be 9 out of 17 (interquartile range, IQR: 4.0) and 30 out of 35 (IQR: 4.0), respectively. Not knowing how to report (55.6%) and what to report (34.9%), no/delayed feedback from the regulatory authority (30.0%), and unavailability of reporting forms (29.0%) were the frequently reported barriers to reporting. In addition, profession (p = 0.027), no/delayed feedback (adjusted odds ratio [AOR]: 4.70; 95% CI: 2.17-10.18; p < 0.001), and not knowing how to report (AOR: 0.12; 95% CI: 0.05-0.28; p < 0.001) were found to be determinants of reporting suspected product quality defects.
Conclusion: The readiness and response of Eritrea's HCPs in detecting and reporting falsified medical products seems promising, although a significant knowledge gap was observed.
Background: Propofol combined with alfentanil is suitable for intravenous anesthesia for day-case hysteroscopy.
Objective: To investigate the median effective dose (ED50) and 95% effective dose (ED95) of alfentanil compounded with propofol for day-case hysteroscopy.
Design: In all, 29 patients who volunteered for painless hysteroscopy in 2022 were recruited. 1.5 mg/kg propofol was given as a sedative to all patients. The trial was conducted using the modified Dixon sequential method, with an initial dose of 10 μg/kg of alfentanil, and the subject's alfentanil dose depended on whether the prior hysteroscopy had failed, which was defined as inadequate cervical dilatation and hysteroscope placement with the patient exhibiting body movement, frowning, or a MOAA/S score >1. If the hysteroscopy failed (i.e. a positive response), the subsequent subject's alfentanil dosage was raised, and conversely (i.e. a negative response), the dose was decreased, with the adjacent dose ratio always being 1:1.2. The formal test begins with the first crossover wave and lasts until seven crossover waves materialize.
Methods: The probit method was used to calculate the ED50, ED95, and corresponding 95% confidence intervals (CIs) of alfentanil compounded with propofol for hysteroscopy.
Results: The ED50 and ED95 of alfentanil combined with propofol for day-case hysteroscopy were 5.701 (95% CI: 3.841-7.069) μg/kg and 8.817 (95% CI: 7.307-20.868) μg/kg, respectively.
Conclusion: Alfentanil at 8.817 μg/kg in conjunction with propofol is a successful and safe approach for day-case painless hysteroscopy.
Trial registration: The trial registry name: Modified sequential method to determine the half-effective dose of alfentanil compounded with propofol for ambulatory hysteroscopy. The URL of registration is https://www.chictr.org.cn/showproj.html?proj=171786, where the full trial protocol can be accessed. Registration number: ChiCTR2200061619.
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