International Journal of Clinical Pharmacy最新文献

Background: Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing.

Aim: This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques.

Method: We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted.

Results: A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively.

Conclusion: We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.

Background: Medicines reviews by general practice pharmacists improve patient outcomes, but little is known about the associated economic outcomes, particularly in patients at higher risk of medicines-related harm.

Aim: To conduct an economic cost-benefit analysis of pharmacists providing person-centred medicines reviews to patients with hyperpolypharmacy (prescribed ≥ 10 regular medicines) and/or at high risk of medicines-related harm across multiple general practice settings.

Method: Service delivery costs were calculated based on the pharmacist's salary, recorded timings, and a general practitioner fee. Direct cost savings were calculated from the cost change of patients' medicines post review, projected over 1 year. Indirect savings were calculated using two models, a population-based model for avoidance of hospital admissions due to adverse drug reactions and an intervention-based model applying a probability of adverse drug reaction avoidance. Sensitivity analyses were performed using varying workday scenarios.

Results: Based on 1471 patients (88.4% with hyperpolypharmacy), the cost of service delivery was €153 per review. Using the population-based model, net cost savings ranging from €198 to €288 per patient review and from €73,317 to €177,696 per annum per pharmacist were calculated. Using the intervention-based model, net cost savings of €651-€741 per review, with corresponding annual savings of €240,870-€457,197 per annum per pharmacist, were calculated. Savings ratios ranged from 181 to 584% across all models and inputs.

Conclusion: Person-centred medicines reviews by general practice pharmacists for patients at high risk of medicines-related harm result in substantial cost savings. Wider investment in general practice pharmacists will be beneficial to minimise both patient harm and healthcare system expenditure.

Background: Polypharmacy is a growing concern, impacting patient safety and healthcare costs. Monitoring its prevalence and temporal trends is essential for effective healthcare management.

Aim: This study aimed to determine prevalence and trends of polypharmacy and excessive polypharmacy in Belgium.

Method: Utilizing a federal claims database, medication data were analyzed from 2012 to 2021. Polypharmacy (≥ 5 medications) and excessive polypharmacy (≥ 10 medications) were evaluated, with prevalence calculated per 1000 inhabitants, and reported per year, age group and region. Linear regression estimated the impact of age and year on polypharmacy prevalence.

Results: In 2021, polypharmacy and excessive polypharmacy were reported in 135/1000 and 31/1000 Belgians respectively. Prevalence of both increased steadily from 2012 to 2021, with excessive polypharmacy rising more prominently. Among adults aged ≥ 65 years, prevalence rates were higher, with polypharmacy at 434/1000 and excessive polypharmacy at 106/1000. Regional variations were observed, with prevalence highest in the Walloons region. Patient age and year (2012-2021) were associated with both polypharmacy and excessive polypharmacy (p < 0.001).

Conclusion: We observed increases in polypharmacy and excessive polypharmacy over a decade in Belgium, particularly among older adults. Efforts to monitor, manage, and optimize medication use are imperative to ensure safe and effective healthcare delivery.

Background: Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindered by researchers' unfamiliarity with evaluation criteria for inappropriate drug use. While traditional machine learning algorithms can enhance evaluation quality, they struggle with the multilabel nature of prescription data.

Aim: This study aimed to combine six machine learning algorithms and three multilabel classification models to identify correlations in prescription information and develop an optimal model to identify PIMs in older adults with dementia.

Method: This study was conducted from January 1, 2020, to December 31, 2020. We used cluster sampling to obtain prescription data from patients 65 years and older with dementia. We assessed PIMs using the 2019 Beers criteria, the most authoritative and widely recognized standard for PIM detection. Our modeling process used three problem transformation methods (binary relevance, label powerset, and classifier chain) and six classification algorithms.

Results: We identified 18,338 older dementia patients and 36 PIMs types. The classifier chain + categorical boosting (CatBoost) model demonstrated superior performance, with the highest accuracy (97.93%), precision (95.39%), recall (94.07%), F1 score (95.69%), and subset accuracy values (97.41%), along with the lowest Hamming loss value (0.0011) and an acceptable duration of the operation (371s).

Conclusion: This research introduces a pioneering CC + CatBoost warning model for PIMs in older dementia patients, utilizing machine-learning techniques. This model enables a quick and precise identification of PIMs, simplifying the manual evaluation process.

Background: There is good evidence describing pharmacy workforce and service provision in general critical care units. However, no data exist from adult extracorporeal membrane oxygenation (ECMO) centres.

Aim: To describe workforce characteristics, pharmacy service provision, and pharmaceutical care activities in critical care units (CCUs) providing an adult ECMO service in the United Kingdom (UK) and compare to national staffing standards for CCUs.

Method: We conducted a multicentre, cross-sectional electronic survey inviting one pharmacy professional response per UK ECMO centre. We collated information on workforce, service provision, and pharmaceutical care activities provided by pharmacy teams in adult CCUs with an ECMO service.

Results: The survey response rate was 90.9%: representatives of 10/11 tertiary hospitals providing ECMO services responded. Median critical care pharmacist to critical care bed was 1:12.1 (IQR: 1:9.4-1:14.9). Most centres (90.0%) did not meet national standards for pharmacy professionals to critical care bed staffing ratios for weekday services. Total critical care beds covered by the critical care pharmacy team varied across the UK: median (IQR) - 45 (37-80) beds. Two centres funded pharmacist time for ECMO activity, and one centre funded a pharmacy technician post. Median peak ECMO activity was 4 ECMO patients in a single day (IQR: 3-5). Most respondents reported reduced pharmacy service at weekends compared to weekday, with limited on-site support.

Conclusion: Most responding ECMO centres in the UK reported pharmacy staffing ratios below nationally agreed critical care standards. There was high variability in clinical pharmacy services to ECMO patients over 7 days.

Background: Medication reconciliation (MedRec) in hospitals is an important tool to enhance the continuity of care, but completing MedRec is challenging.

Aim: The aim of this study was to investigate whether queueing theory could be used to compare various interventions to optimise the MedRec process to ultimately reduce the number of patients discharged prior to MedRec being completed. Queueing theory, the mathematical study of waiting lines or queues, has not been previously applied in hospital pharmacies but enables comparisons without interfering with the baseline workflow.

Method: Possible interventions to enhance the MedRec process (replacing in-person conversations with telephone conversations, reallocating pharmacy technicians (PTs) or adjusting their working schedule) were compared in a computer experiment. The primary outcome was the percentage of patients with an incomplete discharge MedRec. Due to the COVID-19 pandemic, it was possible to add a real-life post hoc intervention (PTs starting their shift later) to the theoretical interventions. Descriptive analysis was performed.

Results: The queueing model showed that the number of patients with an incomplete discharge MedRec decreased from 37.2% in the original scenario to approximately 16% when the PTs started their shift 2 h earlier and 1 PT was reassigned to prepare the discharge MedRec. The number increased with the real-life post hoc intervention (PTs starting later), which matches a decrease in the computer experiment when started earlier.

Conclusion: Using queueing theory in a computer experiment could identify the most promising theoretical intervention to decrease the percentage of patients discharged prior to MedRec being completed.

Background: Falls are a significant public health problem and constitute a major cause of injuries and mortality. Risk factors for falls are multifactorial and include medication use.

Aim: To develop and investigate the content validity of the Medication-Related fall (MRF) screening and scoring tool.

Method: The MRF tool was developed from clinical practice guidelines addressing medication-related problems, and additional medications identified by specialist pharmacists across a region of the United Kingdom (Northern Ireland). Medication classes were categorised according to their 'potential to cause falls' as: high-risk (three points), moderate-risk (two points) or low-risk (one point). The overall medication-related falls risk for the patient was determined by summing the scores for all medications. The MRF was validated using Delphi consensus methodology, whereby three iterative rounds of surveys were conducted using SurveyMonkey^®. Twenty-two experts from 10 countries determined their agreement with the falls risk associated with each medication on a 5-point Likert scale. Only medications with at least 75% of respondents agreeing or strongly agreeing were retained in the next round.

Results: Consensus was reached for 19 medications/medication classes to be included in the final version of the MRF tool; ten were classified as high-risk, eight as moderate-risk and one as low-risk.

Conclusion: The MRF tool is simple and has the potential to be integrated into medicines optimisation to reduce falls risk and negative fall-related outcomes. The score from the MRF tool can be used as a clinical parameter to assess the need for medication review and clinical interventions.

Background: Tacrolimus is a critical component of immunosuppressive therapy for kidney transplant recipients. Intra-patient variation (IPV) of tacrolimus levels affects the function of transplanted kidney.

Aim: This study aimed to investigate the impact of tacrolimus IPV on kidney function, examine its association with post-transplant duration, and assess its effect on the immune status of transplant recipients.

Method: This retrospective study was conducted from January 2016 to February 2022. IPV was evaluated using the coefficient of variation (CV) of tacrolimus trough levels from 6 to 48 months after transplantation. Patients were divided into low- and high-IPV groups based on the median CV. Significant differences in kidney function, CD4 + /CD8 + ratio, and post-transplant duration between these groups were analyzed.

Results: Among 189 patients, tacrolimus IPV showed a strong correlation with serum creatinine clearance rate (Ccr) and estimated glomerular filtration rate (eGFR) (p < 0.05). Tacrolimus IPV was significantly correlated with post-transplant duration in only two patients (p < 0.05). Using a median CV of 15.4% to categorize patients, the high IPV group, compared to the low IPV group, exhibited significantly higher eGFR at 6-9 months (p < 0.05), lower Ccr at 9-12 months (p < 0.05), and reduced Ccr and eGFR at 15-18 months (p < 0.05). Six months after transplantation, the high IPV group had a significantly lower CD4 + /CD8 + ratio than the low IPV group (p < 0.05).

Conclusion: This study highlights the significant impact of tacrolimus IPV on transplant kidney function and immune status in transplant patients at various post-transplantation intervals.