International Journal of Clinical Pharmacy最新文献

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: 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: 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: 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.

Background: People living with severe and persistent mental illness (SPMI) often take multiple medications and are at risk of experiencing medication related problems. Medication review services have the potential to reduce inappropriate use of psychotropic medications and improve adherence. However, there is limited research regarding pharmacists' perspectives when providing such services.

Aim: To explore community pharmacists' views and experiences of providing an in-pharmacy medication review (MedsCheck) for people living with SPMI.

Method: Semi-structured interviews were conducted between November 2021 and May 2022 with community pharmacists participating in the comparator group of the PharMIbridge Randomised Controlled Trial (RCT), which aimed to improve medication adherence and manage physical health concerns for people living with SPMI. Interviews were recorded, transcribed, and analysed using inductive thematic analysis.

Results: Fifteen semi-structured interviews were conducted with community pharmacists including pharmacy owners, managers and employee pharmacists. Most pharmacist participants who were interviewed (n = 10) were aged under 39 and more than half (n = 8) had 10 or more years of pharmacy experience. Five key themes were identified: 1) Pharmacists' roles in the management of SPMI in community pharmacy; 2) Mental health education and training; 3) Pharmacy resources; 4) Challenges with interprofessional collaboration and 5) Impact on professional relationships and consumer outcomes.

Conclusion: Pharmacists are motivated to support people living with SPMI. Mental health training, as well as arrangements regarding pharmacy workflow and appropriate remuneration are needed to enable pharmacists to better support people living with SPMI. Referral pathways should be directly accessible by community pharmacists to assist interprofessional collaboration.

Background: Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary.

Aim: Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques.

Method: Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations.

Results: A total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R² = 0.65, mean absolute error = 77.92, root mean square error = 93.58). In the testing cohort, the accuracy of the predicted concentration within ± 30% of the actual concentration was 73.49%. In the subgroup analysis, the prediction accuracy was 69.39% within the recommended therapeutic range of venlafaxine concentration within ± 30% of the actual value.

Conclusion: The XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.

Background: Medication use in older adults is increasing, therefore, reducing the risk of suboptimal medicine use is imperative in achieving optimal therapeutic outcomes. Research suggests that factors such as personal beliefs and beliefs about medicines may be associated with non-adherence and inappropriate medicine use.

Aim: To systematically review and identify quantitative research on the influence of beliefs about medicines and the relationship with suboptimal medicine use in older adults.

Method: Searches were conducted on PubMed, EMBASE, CINAHL, and PsycINFO for quantitative studies (inception to March 2023).

Inclusion criteria: (1) exposure: participants' beliefs (personal, cultural, and medication-related), (2) outcomes: polypharmacy, potentially inappropriate medicines use, or non-adherence, and (3) participants: community-dwelling adults 65 years or above. Study selection, data extraction and quality appraisal (Joanna Briggs Institute critical appraisal checklist) were completed independently by two investigators. Data were combined in a narrative synthesis and presented in a summary of findings table.

Results: Nineteen articles were included: 15 cross-sectional and four cohort studies. Outcomes of included papers were as follows; adherence (n = 18) and potentially inappropriate medicine use (n = 1). Ten studies found stronger beliefs in the necessity of medicines and/or fewer concerns led to better adherence, with one paper contradicting these findings. Three studies did not find associations between adherence and beliefs. One study confirmed an association between unnecessary drug use and a lack of belief in a "powerful other" (e.g. doctor).

Conclusion: Further investigation is necessary to (1) ascertain the importance of necessity or concern beliefs in fostering adherence and, (2) examine the influence of beliefs on polypharmacy and inappropriate medicine use.