Prescribing Research Articles

Real world evidence: patients with alopecia areata in Italy.

This real-world analysis aimed at characterizing patients hospitalized for alopecia areata (AA) in Italy, focusing on comorbidities, treatment patterns and the economic burden for disease management. Administrative databases of healthcare entities covering 8.9 million residents were retrospectively browsed to include patients of all ages with hospitalization discharge diagnosis for AA from 2010 to 2020. The population was characterized during the year before the first AA-related hospitalization (index-date) and followed-up for all the available successive period. AA drug prescriptions and treatment discontinuation were analyzed during follow-up. Healthcare costs were also examined. Among 252 patients with AA (mean age 32.1 years, 40.9% males), the most common comorbidities were thyroid disease (22.2%) and hypertension (21.8%), consistent with literature; only 44.4% (112/252) received therapy for AA, more frequently with prednisone, triamcinolone and clobetasol. Treatment discontinuation (no prescriptions during the last trimester) was observed in 86% and 88% of patients, respectively at 12 and 24-month after therapy initiation. Overall healthcare costs were 1715€ per patient (rising to 2143€ in the presence of comorbidities), mostly driven by hospitalization and drugs expenses. This first real-world description of hospitalized AA patients in Italy confirmed the youth and female predominance of this population, in line with international data. The large use of corticosteroids over other systemic therapies followed the Italian guidelines, but the high discontinuation rates suggest an unmet need for further treatment options. Lastly, the analysis of healthcare expenses indicated that hospitalizations and drugs were the most impactive cost items.

Drug Recommendation System on Sentiment Analysis of Drug Reviews by Using Machine learning

Abstract: With the healthcare system facing increased challenges due to the COVID-19 pandemic, there's a big demand for new ideas to help doctors and nurses. This paper suggests a new way of using computers to help doctors decide which medicines to give to patients. By using smart computer programs, we can make it easier for healthcare workers to handle their workload and provide better care for patients. By analyzing patient reviews, we employ sentiment analysis using advanced vectorization methods like Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and Manual Feature Analysis. These techniques allow for the identification of subtle sentiment nuances crucial for tailoring personalized drug recommendations. We utilize various classification algorithms such as Naive Bayes, Support Vector Classifier (SVC), and Random Forest to predict sentiments. Among these, the LinearSVC classifier coupled with TF-IDF vectorization demonstrates superior performance in sentiment prediction. We tested our system using various measures like precision, recall, F1-score, and Area Under the Curve (AUC) to ensure its effectiveness. By incorporating advanced machine learning techniques, our system offers a strong foundation for enhancing the accuracy of drug prescriptions, which is a key issue in today's healthcare. Through better drug recommendations, our goal is to ultimately enhance patient health outcomes and play a part in advancing healthcare practices, especially in the face of complex and evolving healthcare challenges.

Polypharmacy in Older Patients: A Three-Year Longitudinal Analysis in Primary Care Settings of Aragón, Spain.

Challenges of polypharmacy and the impact of coronavirus disease 2019 (COVID-19) pandemic in older patients require further investigation. This retrospective study analyzed the progression of polypharmacy and anticholinergic burden in older patients in a primary care setting before, during, and after the COVID-19 pandemic. This 3-year cross-sectional study (2019, 2020, and 2021) comprised a dynamic cohort of individuals aged ≥75 years, who attended the Arrabal Primary Care Center in Zaragoza, Spain. Older patients with polypharmacy (≥5 medications) were identified according to their electronic health records. We collected demographic and clinical data, including medication prescriptions, diagnoses, and anticholinergic risks, and performed descriptive and statistical analyses. This study included a total of 1,928 patients with a mean age of 83.52±0.30 years. Over the 3-year study period, the mean number of medications prescribed increased, from 9.4 in 2019 to 10.4 in 2021. The prevalence of excessive polypharmacy (≥10 medications) increased from 39% in 2019 to 45% in 2021. The most commonly prescribed drugs were anilides, proton pump inhibitors, benzodiazepine derivatives, and platelet aggregation inhibitors. Women had a higher prevalence of illnesses and anticholinergic drug prescriptions than men. The results of this study highlighted an upward trend in polypharmacy and excessive polypharmacy among older patients in primary care settings. Future research should focus on optimizing medication management and deprescribing strategies and minimizing the adverse effects of polypharmacy in this population.

Study of physicians’ preferences for pharmacotherapy of arterial hypertension in pregnant women (the PHYGEST study)

Introduction: Elevated blood pressure in a pregnant woman is associated with serious risks for the mother and fetus. Therefore, the issues of rational antihypertensive pharmacotherapy are always extremely relevant in the scientific community. Purpose of the Study: To determine the preferences of physicians in the issues of pharmacotherapy for pregnant women with arterial hypertension and evaluate the influence of specialists’ length of employment. Materials and Methods: A multicenter study (a pharmacoepidemiologic knowledge assessment) was conducted in 2018–2021 using an anonymous questionnaire with open-ended questions. This paper discusses 5 questions of the questionnaire. Results and Discussion: The preferences of practicing specialists (411 physicians from 8 regions of Russia) for the prescription of basic and additional drugs for the treatment and prevention of hypertension, the impact of the physicians’ length of employment, and the degree of compliance with current clinical guidelines were determined. Conclusions: Certain specific and not fully rational specialists’ preferences were revealed, which confirms the importance of attention to this problem and requires the introduction of additional educational and supervisory efforts in this direction, as well as the continuation of similar studies in the future.

ИССЛЕДОВАНИЕ СТРУКТУРЫ ПОТРЕБЛЕНИЯ ЛЕКАРСТВЕННЫХ ПРЕПАРАТОВ У БОЛЬНЫХ БРОНХИАЛЬНОЙ АСТМОЙ В СТАЦИОНАРАХ КЫРГЫЗСКОЙ РЕСПУБЛИКИ

Bronchial asthma (BA) is one of the socially significant diseases, characterized by a high frequency of seeking medical help and mortality. The most important parameter in studies of drug consumption is the study of the structure of their use, which includes the volume of use and a number of qualitative characteristics of the use of drugs of the group being studied. Modern approaches to pharmacotherapy of asthma also include the use of drugs in combinations; such drug therapy makes it possible to control the course of asthma, which affects morbidity, mortality, quality of life and the quality of provision of pharmaceutical and medical care to the population. Aim. Study of the frequency and structure of prescription of drugs used in the pharmacotherapy of bronchial asthma. Matherials and Methods. During a retrospective pharmacoepidemiological study, 500 individual registration records of patients with bronchial asthma who were hospitalized for the period 2020 - 2023 were analyzed. in healthcare organizations of the Ministry of Health of the Kyrgyz Republic. Results. When analyzing the number of prescribed medications, it was found that in the pharmacotherapy of 500 patients with bronchial asthma in different combinations, 11 names of medications under international nonproprietary names and 26 medications under trade names were used, while the total number of prescribed medications per patient varied from 1 to 5 types of drugs in 62 different combinations. Of the combinations of salbutamol with drugs from other pharmacological groups, the leading ones were the simultaneous administration of 3 drugs: Salbutamol + Ambroxol + Theophylline; for beclomethasone, a combination of 5 drugs: Beclomethasone + Salbutamol + Ambroxol + Salbutamol + Theophylline; for salmeterol, a combination of 5 drugs: Salmeterol + Fluticacone, Salbutamol, Acetylcysteine, Salbutamol. Conclusions. A study of the structure of medications used in the hospital in the treatment of patients with bronchial asthma made it possible to establish that the largest share of drug prescriptions was for the treatment regimen Beclomethasone + Salbutamol + Ambroxol + Salbutamol + Theophylline, which was used in the treatment of 67 patients. The regimen Salbutamol + Ambroxol + Theophylline was used in the treatment of 62 patients and the prevailing treatment regimens were Salmeterol + Fluticacone, Salbutamol, Ambroxol, Theophylline and Salmeterol + Fluticacone, Salbutamol, Ambroxol, Salbutamol, Theophylline, each of which was used in the treatment of 28 patients.

Utilization of Ocrelizumab within Different Treatment Strategies for Multiple Sclerosis: A 5-Year Population-Based Study.

We aim to provide up-to-date real-world evidence on the persistence, adherence, healthcare resource utilization, and costs of multiple sclerosis (MS) by comparing ocrelizumab to other disease-modifying treatments (DMTs) and within different DMT sequences. We included 3371 people with MS who first received or switched DMT prescriptions from January 2018 to December 2022; they were identified through hospital discharge records, drug prescriptions, and exemption codes from the Campania Region (South Italy). We calculated persistence (time from the first prescription to discontinuation or switching to another DMT), adherence (proportion of days covered (PDC)), DMT costs, and MS hospital admissions and related costs. The most frequently prescribed DMT was dimethyl fumarate (n = 815; age 38.90 ± 11.91 years; 69.5% females), followed by ocrelizumab (n = 682; age 46.46 ± 11.29 years; 56.3%); 28.8% of the patients treated with ocrelizumab were naïve to DMTs. Using ocrelizumab as a statistical reference, the risk of discontinuation was higher for other highly active (HR = 6.32; 95%CI = 3.16, 12.63; p < 0.01) and low-/medium-efficacy DMTs (HR = 10.10; 95%CI = 5.10, 19.77; p < 0.01); adherence was lower for other highly active DMTs (Coeff = -0.07; 95%CI = -0.10, -0.04; p < 0.01) and low-/medium-efficacy DMTs (Coeff = -0.16; 95%CI = -0.19, -0.14; p < 0.01). monthly DMT costs were higher for other highly active DMTs (Coeff = 77.45; 95%CI = 29.36, 125.53; p < 0.01) but lower for low-/medium-efficacy DMTs (Coeff = -772.31; 95%CI = -816.95, -727.66; p < 0.01). The hospital admissions and related costs of MS were similar between ocrelizumab, other highly active DMTs, and other low-/medium-efficacy DMTs, and with ocrelizumab as the first-line DMT after other highly active DMTs and after low-/medium-efficacy DMTs, which was possibly due to the low number of observations. From 2018 to 2022, ocrelizumab was among the most frequently prescribed DMTs, with 28.8% prescriptions to incident MS patients, confirming its relevance in clinical practice. Ocrelizumab was associated with the highest persistence and adherence, pointing towards its favorable benefit-risk profile. The costs of ocrelizumab were lower than those of other highly active DMTs.

Failure to reduce benzodiazepine prescriptions through the implementation of a psychological intervention for insomnia in an Italian mental health service.

Despite the evidence of higher effectiveness of psychological interventions for insomnia compared to pharmacological ones, drug prescriptions for insomnia remain frequent. This study has assessed patterns of prescriptions of BZDs for insomnia before and after the delivery of a training in psychological interventions to professionals working in the services of a Department of Mental Health in northern Italy. The intervention consisted in two training sessions about psychological interventions for insomnia delivered to professionals of the participating services. The prevalence of users with a prescription of BZDs for insomnia in an index period after the delivery of the training was compared to the prevalence in an index period before the training. Among 727 people assessed for BZDs prescription at pre-intervention, 306 (42.1%, 95% CI 0.39-0.46) had a prescription, and 344 (49.2%, 95% CI 0.45-0.53) had a prescription among 699 people assessed at post-intervention, corresponding to a significant odds ratio of 1.33 to be prescribed with BZDs in the second index period compared to the first one. Psychological interventions were offered to a small group of patients. Prescribing attitudes of BZDs for insomnia were not modified after the training and delivery of a psychological intervention in a mental healthcare outpatient setting. Prescribing habits should be addressed more directly in training, and professionals should be more aware of risks of BZDs assumption. The failure in changing drug prescriptions in this study should prompt more real-world studies of the application of evidence-based strategies, particularly in outpatient mental health settings.

Does preterm birth increase the initiation of antidepressant use during the postpartum? A population-based investigation.

Preterm birth may affect maternal mental health. We explored the relationship between preterm birth and the risk of initiating antidepressant use during the year after birth. We conducted a population-based investigation using regional healthcare utilization databases. The exposure considered was preterm birth. The outcome was having at least one prescription for antidepressant medications during the year after birth. We used a log-binomial regression model including terms for maternal age at birth, nationality, educational level, parity, modality of conception, modality of delivery, use of other psychotropic drugs, and diabetes to estimate relative risk (RR) and 95% confidence intervals (CI) for the association between preterm birth and the initiation of antidepressant use. In addition, the absolute risk differences (ARD) were also computed according to the timing of birth. The cohort included 727,701 deliveries between 2010 and 2020 in Lombardy, Northern Italy. Out of these, 6,522 (0.9%) women had at least one prescription for antidepressant drugs during the year after birth. Preterm births were related to a 38% increased risk of initiation of antidepressant use during the year after birth (adjusted RR = 1.38; 95% CI: 1.25-1.52) for moderate to late preterm and to 83% (adjusted RR = 1.83; 95% CI: 1.46-2.28) for extremely and very preterm. Excluding women with only one antidepressant prescription, the association was consistent (adjusted RR = 1.41, 95%CI: 1.23-1.61 for moderate to late preterm and adjusted RR = 1.81, 95% CI: 1.31-2.49 for extremely and very preterm). Also, excluding women who used other psychotropics, the association remained consistent (adjusted RR = 1.39, 95%CI: 1.26-1.54 and adjusted RR = 1.91, 95% CI: 1.53-2.38, respectively for moderate to late and extremely and very preterm). Women who delivered preterm may have an excess risk of initiation of antidepressant consumption during the first year after birth.

Comparable Psychotropic Prescription Rates After Hospital Discharge Between Patients with COVID-19 and Those With Non-COVID-19-Related Respiratory Infection.

Whether psychiatric symptoms after recovery from coronavirus disease 2019 (COVID-19) are specific to this illness remains unclear. In this retrospective study, the Diagnosis Procedure Combination data and outpatient clinic data were used for patients who received inpatient treatment in Saiseikai-affiliated hospitals for COVID-19 or other respiratory tract infections (non-COVID) from 2020 to 2022. The primary outcome was new prescriptions of psychotropic drugs after discharge (i. e., prescriptions of psychotropics to patients who had not received them before or during their hospitalization). Values of interest were compared between groups using the chi-square test or Fisher's exact test. A COX proportional-hazards model was used to examine factors associated with psychotropic prescriptions after discharge in age- and sex-matched COVID-19 and non-COVID patients. Of 31,993 chart records, 19,613 were excluded due to a positive history with psychiatric disorders (n=2,445), prescriptions of psychotropics (n=744), and no follow-ups (n=16,424). Thus, 3,648 COVID-19 and 8,732 non-COVID patients were included (mean [range] duration of follow-up, days: 146.9 [1-727] and 239.2 [1-729], respectively). Two hundred and four (5.6%) of the 3,648 patients with COVID-19 received psychotropic prescriptions after discharge. No statistically significant differences were observed in the prescription rates of any psychotropic category between the COVID-19 and non-COVID groups. An increase in severity during hospitalization was significantly associated with more frequent psychotropic prescriptions (hazard ratio 1.83, p<0.001). The development of psychiatric symptoms should be closely observed, especially in patients who experienced increased severity during hospitalization, regardless of whether they suffered from COVID-19.

Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis

Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In the process of concrete implementation, it is necessary to process natural language at multiple levels. Based on this, this paper combined deep learning and natural language processing technology to conduct sentiment analysis on patients' comments, so as to recommend drugs that are more suitable for patients, thus achieving accurate drug prescribing and personalized recommendation.

Effect of angiotensin-converting enzyme inhibitors versus that of angiotensin receptor blockers on survival in patients undergoing hemodialysis: a nationwide observational cohort study

Background This study aimed to evaluate the patient survival rates based on the use of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB) in a large cohort of patients undergoing maintenance hemodialysis (HD). Methods Data from a national HD quality assessment program were used in this retrospective study. The patients were classified into four groups based on the use of renin-angiotensin system blockers (RASBs) as follows: No group, patients without a prescription of any anti-hypertensive drugs including RASBs; Other group, patients with a prescription of anti-hypertensive drugs excluding RASBs; ACEI group, patients with a prescription of an ACEI; and ARB group, patients with a prescription of an ARB. Results The 5-year survival rates in the no, other, ACEI, and ARB groups were 68.6%, 67.8%, 70.6%, and 69.2%, respectively. The ACEI group had the best patient survival trend among the four groups. In multivariable Cox regression analyses, no differences were observed between the ACEI and ARB groups. Among young patients and patients without diabetes or heart disease, the ACEI group had the best patient survival among the four groups. However, among patients with DM or heart disease, the ARB group had the best patient survival. Conclusions Our study found that patients receiving ACEI and ARB had comparable survival. However, patients receiving ARB had better survival in the subgroups of patients with DM or heart disease, and patients receiving ACEI had better survival in the subgroup of young patients or patients without diabetes or heart disease.

A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules

According to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation system for patients having both Type-2 Diabetes Mellitus (T2DM) and CKD. Although there are several computational solutions that use ontology-based systems for treatment plans for these type of diseases, none of them combine information analysis and treatment plans prediction for T2DM and CKD. The proposed method is novel: (1) We develop a new drug-drug interaction model and drug dose ontology called DIAKID (for drugs of T2DM and CKD). (2) Using comprehensive Semantic Web Rule Language (SWRL) rules, we automatically extract the correct drug dose, K-raising drugs, and drug-drug interaction warnings based on the Glomerular Filtration Rate (GFR) value of T2DM and CKD patients. The proposed work achieves very competitive results, and this is the first time such a study conducted on both diseases. The proposed system will guide clinicians in preparing prescriptions by giving necessary warnings about drug-drug interactions and doses.

Prescription Drug Dispensing and Patient Costs After Implementation of a No Behavioral Health Cost-Sharing Law

On January 1, 2022, New Mexico implemented a No Behavioral Cost-Sharing (NCS) law that eliminated cost-sharing for mental health and substance use disorder (MH/SUD) treatments in plans regulated by the state, potentially reducing a barrier to treatment for MH/SUDs among the commercially insured; however, the outcomes of the law are unknown. To assess the association of implementation of the NCS with out-of-pocket spending for prescription for drugs primarily used to treat MH/SUDs and monthly volume of dispensed drugs. This retrospective cohort study used a difference-in-differences research design to examine trends in outcomes for New Mexico state employees, a population affected by the NCS, compared with federal employees in New Mexico who were unaffected by NCS. Data were collected on prescription drugs for MH/SUDs dispensed per month between January 2021 and June 2022 for New Mexico patients with a New Mexico state employee health plan and New Mexico patients with a federal employee health plan. Data analysis occurred from December 2022 to January 2024. Enrollment in a state employee health plan or federal health plan. The primary outcomes were mean patient out-of-pocket spending per dispensed MH/SUD prescription and the monthly volume of dispensed MH/SUD prescriptions per 1000 employees. A difference-in-differences estimation approach was used. The implementation of the NCS law was associated with a mean (SE) $6.37 ($0.30) reduction (corresponding to an 85.6% decrease) in mean out-of-pocket spending per dispensed MH/SUD medication (95% CI, -$7.00 to -$5.75). The association of implementation of NCS with the volume of prescriptions dispensed was not statistically significant. These findings suggest that the implementation of the New Mexico NCS law was successful in lowering out-of-pocket spending on prescription medications for MH/SUDs, but that there was no association of NCS with the volume of medications dispensed in the first 6 months after implementation. A key challenge is to identify policies that protect from high out-of-pocket spending while also promoting access to needed care.

Prevalence of unqualified sources of antimalarial drug prescription for children under the age of five: A study in 19 low- and middle-income countries.

Antimalarial drug resistance poses a severe danger to global health. In Low- and Middle-Income Countries (LMICs), there is a lack of reliable information on antimalarial prescriptions for recent malarial fever in children under five. Our study aims to determine the prevalence of unqualified sources of antimalarial drug prescription for children under the age of five in 19 low- and middle-income countries. We performed a cross-sectional study of the Malaria Indicator Survey (MIS) datasets (n = 106265) across 19 LMICs. The recent MIS datasets were used, and the study only included children under five who had taken an antimalarial drug for a recent malarial fever. The outcome variable was classified into two distinct categories: those who had taken antimalarial drugs for malarial fever from qualified sources and those who did not. Among LMICs, we found that 87.1% of children under five received an antimalarial prescription from unqualified sources who had recently experienced malarial fever. In several LMICs (Tanzania, Nigeria, and Ghana), a substantial portion of recent antimalarial prescriptions for malaria was taken from unqualified sources (about 60%). Some LMICs (Guinea (31.8%), Mali (31.3%), Nigeria (20.4%), Kenya (2.6%), and Senegal (2.7%)) had low rates of antimalarial drug consumption even though children under five received a high percentage of antimalarial prescriptions from qualified sources for a recent malarial fever. Living in rural areas, having mothers with higher education, and having parents with more wealth were frequently taken antimalarial from qualified sources for recent malarial fever in children under five across the LMICs. The study draws attention to the importance of national and local level preventative strategies across the LMICs to restrict antimalarial drug consumption. This is because antimalarial prescriptions from unqualified sources for recent malarial fever in children under five were shockingly high in most LMICs and had high rates of unqualified prescriptions in certain other LMICs.

Physiological and psychological determinants of long-term diet-induced type 2 diabetes (T2DM) remission: A narrative review.

Type 2 diabetes mellitus (T2DM) is a highly prevalent metabolic disease, causing a heavy burden on healthcare systems worldwide, with related complications and anti-diabetes drug prescriptions. Recently, it was demonstrated that T2DM can be put into remission via significant weight loss using low-carbohydrate diets (LCDs) and very low-energy diets (VLEDs) in individuals with overweight and obesity. Clinical trials demonstrated remission rates of 25-77%, and metabolic improvements such as improved blood lipid profile and blood pressure were observed. In contrast, clinical trials showed that remission rate declines with time, concurrent with weight gain, or diminished weight loss. This review aims to discuss existing literature regarding underlying determinants of long-term remission of T2DM including metabolic adaptations to weight loss (e.g., role of gastrointestinal hormones), type of dietary intervention (i.e., LCDs or VLEDs), maintaining beta (β)-cell function, early glycemic control, and psychosocial factors. This narrative review is significant because determining the factors that are associated with challenges in maintaining long-term remission may help in designing sustainable interventions for type 2 diabetes remission.

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.

One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo. We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients' probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC). We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10. Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.

Diversity of oncopharmacogenetic profile within Spanish population.

Consensus guidelines for genotype-guided fluoropyrimidine dosing based on variation in the dihydropyrimidine dehydrogenase (DPYD) gene before treatment have been firmly established. The prior pharmacogenetic report avoids the serious toxicity that inevitably occurred in a non-negligible percentage of the treated patients. The precise description of the allelic distribution of the variants of interest in our reference populations is information of great interest for the management of the prescription of these antineoplastic drugs. We characterized the allelic distribution of the UGT1A1*28 variant (rs3064744), as well as the DPYD*2A (rs3918290) variant, c.1679T>G (rs55886062), c.2846A>T (rs67376798) and c.1129-5923C>G (rs75017182; HapB3) in series of 5251 patients who are going to receive treatment with irinotecan and fluoropyrimidines, representative of Valencian, Aragonese and Western Andalusian populations.

O68: Long term stroke prevention drug therapy following Carotid endarterectomy (CEA) is good, but could be better

Abstract Introduction CEA reduces the risk of future stroke. The benefit is maximised with live-long drug therapy to reduce major adverse cardiovascular events (MACE), including stroke. Studies suggest around half discontinue these drugs within 12 months. We compared the drugs prescribed at CEA with those prescribed several years later. Methods Of 347 patients who underwent CEA between 2010-2019, at late follow-up (9+/-3.1y), 187 (54%) patients were alive. The prescription of generic MACE-reducing drugs (anti-thromboembolic, lipid-lowering, b-blockers and ACE-Inhibitors) and incidence of cardiovascular risk factors (hypertension, diabetes, ischaemic cardiac disease, stroke) at CEA were compared with that at late follow-up. Results At late follow-up, fewer were taking antiplatelet drugs (170v143 p&amp;lt;0.01), but more were formally anticoagulated (4v37 p&amp;lt;0.01). Overall there was no change in anti-thromboembolic prescription rates. Lipid-regulating drugs were equally highly prescribed at CEA and follow-up (169v173), Both B-blocker and ACE-Inhibitor prescriptions rates were low, but had increased by follow-up (46v69 p&amp;lt;0.01; 38v88 p&amp;lt;0.01). More patients were treated for hypertension at follow-up (67v166 p&amp;lt;0.01), but numbers with managed diabetes had not changed at late follow-up. 47 (25%) of patients had a history of ischaemic heart disease at CEA and 24 (14%) had developed new symptoms. There were 22 (12%) new strokes at late follow-up. Discussion Despite a higher than expected prescription rate of MACE-reducing drugs years after CEA, which is tailored to the patient’s changing needs, continued improvement is needed to optimise cardiovascular protection fully.

Adverse Drug Reactions and Prescription Patterns of Antiretroviral Drugs: A Longitudinal Observational Study From a Tertiary Care Hospital in Western India.

Background In 2018, the World Health Organisation (WHO) released interim guidelines, advising a change of regimens to dolutegravir-based first- and second-line antiretroviral therapy (ART), based on which, in 2021, the National Aids Control Organisation (NACO) updated its guidelines to include the tenofovir+ lamivudine + dolutegravir (TLD) regimen as a first line of therapy for all people living with HIV (PLHIV) and second- and third-line regimens to dolutegravir-based regimens. Considering this change of regimen, the adverse drug reaction (ADR) profiling and longitudinal prescription pattern of antiretroviral and concomitant medications in adult patients at the ART centre of a tertiary care hospital were assessed in this study. Methods Ninety-seven PLHIV out of all the patients who attended the ART centre from September 2021 to July 2022 were enrolled and followed up for six months. The ADRs that occurred during this period were collected along with details of prescription patterns and analyzed by descriptive statistics. Causality assessment for ADR was done using the World Health Organisation-Uppsala Monitoring Centre (WHO-UMC) scale. Results Seventy-eight percent (n=76 out of 97) of patients experienced at least one ADR, and 128 ADRs were seen in 97 patients. The most common ADRs were increased alkaline phosphatase (39.0%, n=128), dyslipidaemia (12.5%, n=128), and nephrotoxicity (10.1%, n=128). The drug most suspected of causing ADRs was dolutegravir (27.5%, n=342). The most common therapeutic regimen was TLD (71.2%, n=97). The most prescribed drug was lamivudine (30.6%, n=1183). The most prescribed concomitant medication was cotrimoxazole (15%, n=312). Conclusions Dolutegravir-based regimens have been implemented for PLHIV in a phased-out manner from previous non-dolutegravir-based ART regimens, which is in line with the recent NACO guidelines. However, it has also led to an increase in dolutegravir-associated ADRs like increased alkaline phosphatase, dyslipidaemia, and nephrotoxicity. Continuous monitoring of prescriptions and ADRs can add to our knowledge regarding their use and ADRs.

Proportion of Antipsychotics with CYP2D6 Pharmacogenetic (PGx) Associations Prescribed in an Early Intervention in Psychosis (EIP) Cohort: A Cross-Sectional Study.

Prescribing drugs for psychosis (antipsychotics) is challenging due to high rates of poor treatment outcomes, which are in part explained by an individual's genetics. Pharmacogenomic (PGx) testing can help clinicians tailor the choice or dose of psychosis drugs to an individual's genetics, particularly psychosis drugs with known variable response due to CYP2D6 gene variants ('CYP2D6-PGx antipsychotics'). This study aims to investigate differences between demographic groups prescribed 'CYP2D6-PGx antipsychotics' and estimate the proportion of patients eligible for PGx testing based on current pharmacogenomics guidance. A cross-sectional study took place extracting data from 243 patients' medical records to explore psychosis drug prescribing, including drug transitions. Demographic data such as age, sex, ethnicity, and clinical sub-team were collected and summarised. Descriptive statistics explored the proportion of 'CYP2D6-PGx antipsychotic' prescribing and the nature of transitions. We used logistic regression analysis to investigate associations between demographic variables and prescription of 'CYP2D6-PGx antipsychotic' versus 'non-CYP2D6-PGx antipsychotic'. Two-thirds (164) of patients had been prescribed a 'CYP2D6-PGx antipsychotic' (aripiprazole, risperidone, haloperidol or zuclopenthixol). Over a fifth (23%) of patients would have met the suggested criteria for PGx testing, following two psychosis drug trials. There were no statistically significant differences between age, sex, or ethnicity in the likelihood of being prescribed a 'CYP2D6-PGx antipsychotic'. This study demonstrated high rates of prescribing 'CYP2D6-PGx-antipsychotics' in an EIP cohort, providing a rationale for further exploration of how PGx testing can be implemented in EIP services to personalise the prescribing of drugs for psychosis.