Search

OBJECTIVES: Medication persistence, defined as the duration of time from its initiation to its discontinuation, is a surrogate for treatment effectiveness. The aim of the study was to evaluate persistence and causes of biological therapy (BT) suspension in patients with chronic inflammatory arthropathies: rheumatoid arthritis, ankylosing spondylitis and psoriatic arthritis. METHODS: Single institution, descriptive, retrospective cohort study. Adult patients with chronic inflammatory arthropathies on BT between January 2009 and December 2016 were included. Persistence to BT was compared considering the type of pathology and treatment. The Kaplan–Meier test was used to analyse medication persistanence and factors associated with it. An analysis of reasons for therapy discontinuation was performed. RESULTS: Three hundred and sixty-two patients were included in the study, which comprised 478 BT lines. For all patients, the 12-month persistence rate was 71.3% (341 out of 478). At the end of the study, 45.2% of the patients continued on their initial BT. Median treatment persistence was 1489 days (CI 95% 1195 to 1783). Longer BT persistence was associated with naïve BT patients: 1945 days (95% CI 1523 to 2367; P

Aims Adalimumab is a biological therapy used to treat different chronic inflammatory diseases. At present, there is an increasing number of adalimumab biosimilars. To assume the acceptability of interchangeability between reference adalimumab and biosimilars, there should be evidence about efficacy and safety of this switching. Regulation of this practice falls under the authority of individual European Union Member States. The aim of this study is to systematically review the evidence on the efficacy, safety and immunogenicity of switching between reference adalimumab and biosimilars in different chronic immune-mediated inflammatory diseases. Methods Studies presenting data about switching between reference adalimumab and biosimilars were identified by sensitive search strategies in Medline and EMBASE from 1 January 2004 to 30 June 2021. Results A total of 471 references were obtained and 21 finally included in the analysis (total number of patients switching: 2802). Eight different adalimumab biosimilars were tested after receiving reference adalimumab. Eight articles included rheumatoid arthritis (RA), one miscellaneous rheumatic disease, six psoriasis (PSO) and six inflammatory bowel disease (IBD) patients. Overall, the efficacy results in the switching groups were comparable to those obtained in the arms of continuous biosimilar and continuous reference adalimumab. There were no significant differences in treatment emergent adverse events, anti-drug or neutralising antibodies among the three groups. Conclusions Switching between reference adalimumab and biosimilars has no impact on efficacy, safety and immunogenicity in patients with RA, PSO and IBD. This finding was consistent for the different adalimumab biosimilars analysed. These conclusions could probably be extended to other rheumatic diseases such as psoriatic arthritis and ankylosing spondylitis.

WHAT IS KNOWN AND OBJECTIVE: Initial treatment recommendations of COVID-19 were based on the use of antimicrobial drugs and immunomodulators. Although information on drug interactions was available for other pathologies, there was little evidence in the treatment of COVID-19. The objective of this study was to analyse the potential drug-drug interactions (pDDIs) derived from the medication used in COVID-19 patients in the first pandemic wave and to evaluate the real consequences of such interactions in clinical practice. METHODS: Cohort, retrospective and single-centre study carried out in a third-level hospital. Adult patients, admitted with suspected COVID-19, that received at least one dose of hydroxychloroquine, lopinavir/ritonavir, interferon beta 1-b or tocilizumab and with any pDDIs according to "Liverpool Drug Interaction Group" between March and May 2020 were included. The possible consequences of pDDIs at the QTc interval level or any other adverse event according to the patient's medical record were analysed. A descriptive analysis was carried out to assess possible factors that may affect the QTc interval prolongation. RESULTS AND DISCUSSION: Two hundred and eighteen (62.3%) patients of a total of 350 patients admitted with COVID-19 had at least one pDDI. There were 598 pDDIs. Thirty-eight pDDIs (6.3%) were categorized as not recommended or contraindicated. The mean value difference between baseline and pDDI posterior ECG was 412.3 ms ± 25.8 ms vs. 426.3 ms ± 26.7 ms; p

The care transition is the time when more medication errors occur. The aim of this study is to analyze the usefulness of a pharmacotherapeutic report model at hospital discharge to prevent medication errors and to simplify pharmacotherapy during a patient's transition from the hospital to primary care.Prospective study including patients diagnosed with chronic obstructive pulmonary disease who were admitted to a short-stay unit or an emergency room. Relevant variables were extracted from the patients' clinical history and SPSS software was used to carry out the statistical analysis. Direct costs were also calculated.79.3% of patients were polymedicated, 15.5% of patients were identified as nonadherent to the treatment, 12.1% were users of alternative therapies, and 10.3% had been prescribed drugs that could be monitored. In 32.8% of the reports, reference was made to the primary care pharmacists with a view to resolve any pharmacotherapeutic discrepancies. A total of 132 discrepancies were identified, the majority being related to medicinal requirements (necessary/unnecessary medication). The major cause of drug-related problems (DRPs) were prescription errors. The drugs that were mainly involved in the onset of DRPs belonged to the R group, and the degree of simplification of the pharmacotherapy was 7.6%. The total cost avoided with the reconciliation was 200€/patient.A continuity program was implemented based on the drafting of a pharmacotherapeutic report, which allowed for detecting discrepancies and updating the patients' pharmacotherapeutic history, resulting in financial savings after its implementation.

To classify hospital units into three risk levels in order to define and prioritise improvement and training measures in each of them.The risk map was developed in two phases: First phase involved the setting up of a multidisciplinary team, a bibliographic search, the identification of medications and of the criteria to design the map: (1) Location: number of high-alert medications; (2) Staff turnover: the units were classified in low turnover units = 1, medium turnover units = 2 and high turnover units = 3 according to data provided by the human resource department; (3) Frequency: quotient between the number of high alert medicactions in each unit and the total of medications used, and (4) Severity: voluntary survey of professionals. An accumulated risk of severity of each unit was calculated: Σ (severity of the drug x number of its units). The Neperian logarithm was applied to this value to reduce the variability of the values. Thus a risk probability index was established = staff turnover x frecuency x Neperian logarithm of severity. In a second phase, the units were classified into three groups and the risk map of high-alert medication was elaborated in the hospital. In it, the units that had a risk probability index higher than 2.9 were classified as high risk units, those that had between 1-2.9 as intermediate risk units and those that had less than 1 as low risk units. According to the risk probability index, improvement measures were defined and priorities were set for each of them.A total 447 high-risk medications corresponding to 227 active ingredients were identified during the study period. The units showing a higher risk were: Intensive Care Medicine (10.51), Reanimation (4.01), and Palliative Care (3.90). Improvement actions (informative poster, visual identification, alerts, training and double checks) were defined and prioritised in accordance with the risk probability index.Knowing the degree of risk of hospitalization units in the management of high-alert medications allows for the implementation of improvement plans in relation to the degree of vulnerability detected.Objetivo: Estratificar las unidades del hospital en tres niveles y elaborar un mapa de riesgos para priorizar las mejoras y la formación sobre el manejo de medicamentos de alto riesgo. Método: La elaboración del mapa se realizó en dos fases: Primera fase, implicó la creación de un equipo multidisciplinar, búsqueda bibliográfica, identificación de medicamentos y de criterios para elaborar el mapa: (1) Localización: número de medicamentos de alto riego; (2) Rotación del personal: se clasificaron las unidades en rotación baja = 1, media = 2 y alta = 3, según datos de recursos humanos; (3) Frecuencia: cociente entre el número de medicamentos de alto riesgo en cada unidad y el total de medicamentos utilizados, y (4) Gravedad: encuesta voluntaria a profesionales. Se calculó un riesgo acumulado de gravedad de cada unidad: Σ (gravedad del medicamento x número de unidades del medicamento). Sobre este valor se aplicó el logaritmo neperiano para reducir la variabilidad de los valores. Con ello se estableció el índice de probabilidad de riesgo = rotación del personal x frecuencia x logaritmo neperiano del riesgo acumulado de gravedad. En una segunda fase, a partir de la ponderación de resultados, se clasificaron las unidades en tres grupos y se construyó el mapa de riesgo de medicamentos de alto riesgo en el hospital. En este se representaron las unidades que tuvieron un índice de probabilidad de riesgo mayor de 2,9 como unidades de alto riesgo, las que lo tuvieron entre 1-2,9 como unidades de riesgo intermedio y las que lo tuvieron menor a 1 como unidades de riesgo bajo. Y según el índice de probabilidad de riesgo en la unidad, se definieron y priorizaron las medidas de mejora para cada una de ellas.Resultados: Se identificaron 447 medicamentos de alto riesgo en el hospital, correspondientes a 227 principios activos. Las unidades de mayor riesgo fueron: Medicina Intensiva (10,51), Reanimación (4,01) y Paliativos (3,90). Se definieron las acciones de mejora por índice de probabilidad de riesgo: póster informativo, identificación visual, alertas, formación y doble chequeo.Conclusiones: Conocer el grado de riesgo de las unidades de hospitalización en el manejo de medicamentos de alto riesgo permite aplicar planes de mejora dirigidos en función de la mayor o menor vulnerabilidad detectada.

Background The introduction of biological therapies (BT) for the treatment of chronic inflammatory arthropathies (CIA) has improved the prognosis of these diseases and the quality of life of patients. However, the safety of these drugs in the long term continues to be an aspect of interest. Objectives This study aims to review the safety of BT in patients with CIA and the consequences of these adverse effects for patients and the health system under daily clinical practice conditions. Methods A descriptive, observational and retrospective study was performed. All patients with CIA: rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA) who started BT (abatacept, adalimumab, certolizumab, etanercept, golimumab, infliximab, tocilizumab and ustekinumab) from January 2009 to January 2017 in the Rheumatology Department of the Sanitary Area of Vigo (Spain) were included. Demographic variables (age, sex), variables related to CIA and their comorbidities: clinical and analytical data (CRP, ESR, haemoglobin, DAS and BASDAI) and years of disease evolution and variables about BT: concomitant treatment (glucocorticoids, methotrexate and leflunomide), persistence, adherence and number of lines were collected. Variables about the adverse effects (AEs) of BT: type and clinical consequence, requirement for additional visits to the Rheumatology clinic, visits to the Emergency Department or need for hospital admission was also collected. Results Three hundred sixty-two patients and 478 BT lines (250 lines in patients with RA, 119 with AS and 109 with PsA) were analysed (mean ±SD of BT lines: 1.7±1.1 per patient). In 301 (63.0%) BT lines, some AEs occurred. In total, 683 AEs were detected (0.57 AEs per patient and year of treatment). The most frequent types of AEs were: bacterial infection (0.39 events per year of BT), viral infections (0.04 events per year of BT), fungal infections (0.04 events per year of BT), reactions related to the administration of BT (0.03 events per year of BT) and cardiovascular alterations (0.02 events per year of BT). Certolizumab and abatacept were the drugs that were more associated with bacterial infections: 0.82 and 0.69 per year of BT, respectively. Infliximab was the BT that was less associated with bacterial infections: 0.24 per year of BT. There were 7 deaths in probable relation with the AEs (4 bacterial infections, 1 cardiovascular event and 2 solid tumours). The AEs caused 71 hospital admissions (668 days of hospital stay), 113 hospital emergency room visits and 42 urgent visits to the Rheumatology Department. Sixty percent of the AE were managed from Primary Care without having to go to the hospital. The AEs caused a definitive interruption of BT in 51 episodes and a switch to a different BT in 52 episodes. Conclusions In our setting, patients with CIA on BT have an incidence of 0.57 AE per patient and year of BT. Infection (particularly bacterial) is the most frequent AE. Although more than 50% of these AEs are managed from Primary Care, they imply a high consumption of hospital resources. Disclosure of Interest None declared

Objective To analyse the volume and content of tweets in relation to biological treatments for chronic inflammatory arthropathies. Methods A Twitter analysis was carried out during one month using the following keywords: ’rheumatoid arthritis9, ’ankylosing spondylitis9, ’psoriatic arthritis9 and their biological therapies: ’abatacept9, ’adalimumab9, ’certolizumab9, ’etanercept9, ’golimumab9, ’infliximab9 and ’tocilizumab9. Tweets were hand-coded and filtered for content. Results 25 441 tweets contained at least one of the keywords. After filtering, 2480 tweets were included in the analysis. Regarding the 983 tweets about therapies, the most frequently mentioned biologics were ’adalimumab9 (n=359), ’infliximab9 (n= 278) and ’etanercept9 (n= 205). In the 1497 tweets about diseases, the term ’rheumatoid arthritis9 (n= 1109) was used more frequently than ’psoriatic arthritis9 (n= 233) and ’ankylosing spondylitis9 (n= 155). The most commonly addressed subjects in the tweets in relation to biological therapies were related to safety/adverse events (136 of 983 (13.8%)) and to administration, particularly drug infusion (60 of 983 (6.1%)) and self-administration (57 of 983 (5.8%)). Regarding diseases, the most commonly addressed subjects were non-pharmacological recommendations such as alternative therapies (145 of 1497 (9.7%)), nutrition (128 of 1497 (8.5%)) and exercise (91 of 1497 (6.1%)). Conclusions Twitter is widely used to search for information about biological treatments for chronic athropathies. Learning more about the subjects dealt with in the tweets will enable us to improve our understanding of the areas of greater interest and concern among patients. This could help hospital pharmacists establish patient-focused strategies addressing the needs of the patients.

Objective To analyse the factors leading to greater satisfaction among patients attending the outpatient hospital pharmacy (OPh). Methods A cross-sectional study was conducted of patients attending the OPh of a 1250-bed university hospital. A self-administered questionnaire for measuring outpatients9 satisfaction was developed. Global satisfaction was measured on a scale of 1 to 10. Indices of perceived quality for accessibility, interpersonal professional–patient relationship and the convenience of the process were modelled through a principal component analysis using varimax rotation. The relationship between the principal components and overall satisfaction was evaluated using regression analysis. Results A questionnaire-based survey was conducted between May and June 2015. A total of 509 valid responses (86.9% response rate) were collected from the OPh. The overall satisfaction score was 7.81 (95% CI 7.59 to 8.04). The principal component analysis produced two components that explained 62.1% of the variance. The first component (CP1) contained questions related to the adequacy of the resources and services. The second component (CP2) contained questions about interpersonal professional–patient relationship. An additional unit in the CP2 was associated with a 3.23 increased risk of having higher satisfaction scores, while an increase of an additional unit in CP1 was associated with a 1.93 increased risk of having higher satisfaction scores. Conclusions Our study shows that the factor which predicts the satisfaction of patients who come to the OPh is the quality of care provided by pharmacists—in particular, information provided, resolution of doubts, personal attention and time devoted to the patient.