Detecting drug-related problems (DRPs) is important in pharmaceutical care in for better therapeutic outcomes. Clinical pharmacists-led comprehensive medication management plays a crucial role in the rational use of drugs by preventing, identifying, and resolving DRPs. In this review, we aimed to determine the effect of interventions on patient outcomes performed by clinical pharmacists in Turkey. A systematic literature search was performed on PubMed, Google Scholar, EMBASE, Cochrane Library, and Turkish databases (ULAKBIM, Dergipark). The main categories were “clinical pharmacist”, “intervention”, and “Turkey”. Two reviewers reviewed each article independently. Two independent reviewers screened all records and extracted data; disagreements were resolved through a consensus. Randomized controlled studies, pre- to post-intervention comparison studies, and cross-sectional studies including pharmacist-led interventions were included in the review. This review included 15 articles evaluating clinical pharmacist interventions. Ten studies (66.7%) focused on DRPs and pharmacist interventions to these problems, while the remaining 5 (33.3%) studies focused on patient education and adherence issues. Studies were conducted in oncology (33.3%), geriatrics (20.0%), chest diseases (13.3%), psychiatry (6.7%), cardiology (6.7%), and infectious diseases (6.7%) clinics. When results of studies are reviewed, most of the interventions were made at the prescriber level followed by the drug level and patient level. Problems were solved in 54.2-93.2% of DRPs, and adherence, patient knowledge, or skills were improved in most of the studies. Most of the studies were carried out within the scope of a postgraduate or doctorate thesis and yet various positive outcomes such as the prevention of side effects, increased quality of life, and decreased duration of hospital stay were observed with high positive rates of interventions, which indicate that other healthcare workers are ready to collaborate with the clinical pharmacists in Turkey.

Clinical nutrition,drug,malnutrition,nutritional disorder

Purpose: This study aimed to develop and validate a new machine learning (ML)-based screening tool for a two-step prediction of the need for and type of nutritional therapy (enteral, parenteral, or combined) using Nutrition Risk Screening 2002 (NRS-2002) and other demographic parameters from the Optimal Nutrition Care for All (ONCA) national cohort data. Methods: This multicenter retrospective cohort study included 191,028 patients, with data on age, gender, body mass index (BMI), NRS-2002 score, presence of cancer, and hospital unit type. In the first step, classification models estimated whether patients required nutritional therapy, while the second step predicted the type of therapy. The dataset was divided into 60% training, 20% validation, and 20% test sets. Random Forest (RF), Artificial Neural Network (ANN), deep learning (DL), Elastic Net (EN), and Naive Bayes (NB) algorithms were used for classification. Performance was evaluated using AUC, accuracy, balanced accuracy, MCC, sensitivity, specificity, PPV, NPV, and F₁-score. Results: Of the patients, 54.6% were male, 9.2% had cancer, and 49.9% were hospitalized in internal medicine units. According to NRS-2002, 11.6% were at risk of malnutrition (≥3 points). The DL algorithm performed best in both classification steps. The top three variables for determining the need for nutritional therapy were severe illness, reduced dietary intake in the last week, and mild impaired nutritional status (AUC = 0.933). For determining the type of nutritional therapy, the most important variables were severe illness, severely impaired nutritional status, and ICU admission (AUC = 0.741). Adding gender, cancer status, and ward type to NRS-2002 improved AUC by 0.6% and 3.27% for steps 1 and 2, respectively. Conclusions: Incorporating gender, cancer status, and ward type into the widely used and validated NRS-2002 led to the development of a new scale that accurately classifies nutritional therapy type. This ML-enhanced model has the potential to be integrated into clinical workflows as a decision support system to guide nutritional therapy, although further external validation with larger multinational cohorts is needed.

Appropriateness of the geriatric outpatients’ medications needs special attention due to risks of falls, fractures, depression, hospital admissions and mortality. This study aimed to identify current practice on medication usage by using the 2^nd version of “Screening Tool of Older People’s Potentially Inappropriate Prescriptions” and “Screening Tool to Alert Doctors to Right Treatment” criteria and affecting factors for the Turkish population. This cross-sectional study was conducted between September 2015 and May 2016 at a university research and training hospital’s geriatric outpatient clinic. Patients aged ≥65 years and had ≥5 different prescribed medications (considered as polypharmacy) were recruited. The main outcome measure was the frequency of inappropriate medications identified by clinical pharmacist in the outpatient clinic according to the 2^nd version of the criterion sets. A total of 700 patients (440 female) were included in this study. According to the results, 316 patients (45.1%) with at least one potentially inappropriate medication and 668 patients (98.3%) with at least one potential prescription omission were detected. Potentially inappropriate medications were associated with the number of medications used per patient [odds ratio (OR): 1.20 p<0.001], living alone (OR: 4.12 p=0.02), and having congestive heart failure (OR: 2.41 p<0.001). Twenty-two (27.5%) out of 80 criteria and 4 (11.8%) out of 34 criteria did not apply to the study population. Detecting inappropriate medications to maintain treatment effectiveness is necessary to provide the optimum therapy. Despite the awareness of polypharmacy in outpatient clinics it is still one of the important causes of inappropriate prescription followed by vaccination rate. Therefore, with the contribution of clinical pharmacist using these available criteria is important, moreover modification of these criteria according to the local needs to be considered to achieve better outcomes.

Objectives Optimal perioperative success in cardiac surgery requires precise management of drug treatment. This study aimed to determine the prevalence, types and associated factors of drug-related problems (DRPs) during the entire hospital stay. Methods A prospective observational study was conducted at the department of cardiovascular surgery in a university hospital between November 2019 and March 2020. Patients with planned elective cardiac surgery, aged ≥18 years, were included. A clinical pharmacist collaboratively reviewed medications on a daily basis and identified DRPs. Results A total of 100 patients (60 male) were included; median (range) age was 62 (19–86) years, and median (IQR) length of stay in hospital was 15 (9) days. A total of 275 DRPs were identified (median (IQR) 3 (2–4)). The number of patients who had at least one DRP was 47 preoperatively, 55 in the postoperative intensive care unit, 100 in the postoperative ward, and 16 at discharge. In order to reduce bias because of the small sample size, Firth’s logistic regression analysis was conducted. Statistically significant variables according to univariate analysis were included into a logistic regression model. Therefore the length of hospital stay (OR 1.14, 95% CI 1.03 to 1.26, p=0.008), living arrangements (living alone) (OR 4.24, 95% CI 1.41 to 12.73, p=0.009), number of medications at admission (OR 1.32, 95% CI 1.09 to 1.59, p=0.002), and having coronary artery bypass graft surgery (OR 2.87, 95% CI 1.07 to 7.70, p=0.03) were associated with an increased risk for DRPs in the final model. Conclusion Hospital stay carries an increased risk for DRPs, especially at the postoperative stage. Modifiable risk factors for DRPs can be managed by required interventions performed by a multidisciplinary healthcare team.