Off-label prescription of drugs at hospital / Prescripción hospitalaria de medicamentos en condiciones fuera de ficha técnica

OBJECTIVES: To develop a procedure for management of off-label medications, and to analyze the treatments, indications, and hospital units which will request them more frequently, as well as which variables will have an impact on the authorization decision, and its economic impact. METHODS: A procedure was designed where clinicians would complete request forms and the Hospital Unit would prepare reports assessing their efficacy, safety, convenience, and cost. The request forms for the past five years were analyzed. RESULTS: A total of 834 applications were received, and 88.1% of these were accepted. The authorization rates were higher for Paediatric Units (95.7% vs. 86.6%; p < 0.05, Student's t test) when spending increased. CONCLUSIONS: The responsibility for assessing off-label prescriptions has fallen on the Pharmacy Unit. It has not been demonstrated that the quality of evidence represents a decisive variable for approval of treatment; on the other hand, age and cost have demonstrated a significant impact

OBJETIVOS: Desarrollar un proceso de gestión de medicamentos en condiciones fuera de ficha técnica y analizar los tratamientos, indicaciones y unidades clínicas que los solicitan, qué variables influyen en la decisión de autorización y su impacto económico. MÉTODOS: Se diseñó un procedimiento según el cual los clínicos cumplimentarían las solicitudes, el Servicio de Farmacia redactaría los informes valorando su eficacia, seguridad, conveniencia y coste, y la dirección médica tomaría la decisión de aceptar o no su uso. Se analizaron las solicitudes de los últimos cinco años. RESULTADOS: Se recibieron 834 solicitudes, autorizándose el 88,1%. Las tasas de autorización fueron mayores para los Servicios Pediátricos (95,7% frente a 86,6%; p < 0,05). Las razones por las que las prescripciones se consideraron fuera de ficha técnica fueron: diferente indicación (73,2%), combinación diferente (10,2%), línea diferente (8,6%) y edad diferente (8%). El 73,4% de las solicitudes fueron de antineoplásicos, siendo rituximab (120) y bevacizumab (103) los más prescritos. La calidad de la evidencia que avalaba las prescripciones fue moderada-baja, aunque sin demostrar relación directa con la probabilidad de aprobación (p = 0,413). El coste de los medicamentos aprobados fue de 8.567.537 € y el ahorro teórico de los denegados 2.268.642 €. El porcentaje de autorización disminuyó según aumentó el gasto de manera estadísticamente significativa (p < 0,05, test t de Student). CONCLUSIONES: La responsabilidad de evaluación de las prescripciones fuera de ficha técnica ha recaído en los Servicios de Farmacia. La calidad de la evidencia no ha demostrado ser una variable decisiva para la aprobación de los tratamientos. En cambio, la edad y el coste sí que han demostrado influir significativamente

Potential drug-drug interactions in oncological adult inpatients at a Spanish hospital: epidemiology and risk factors.

BACKGROUND: Oncological patients are at high risk for drug-drug interactions (DDIs), which may contribute to therapeutic failure or lead to serious adverse events. OBJECTIVE: To determine the prevalence of potential DDIs in medication lists, to describe the most frequent DDIs and to investigate the possible risk factors associated with them. A prospective cohort study was performed at the Oncology Department of a tertiary hospital over a 12-week period. Twice a week, every inpatient's treatment sheet was collected and screened through two databases: Micromedex™ and Drug Interaction Facts™. All identified potential DDIs with a moderate or higher severity rating were recorded. Multivariate analysis was used to identify risk factors associated with DDIs. RESULT: A total of 1956 DDIs were detected in 699 treatment sheets. The prevalence of treatment sheets with DDIs was 81.0 % and 32.6 % by Micromedex™ and Drug Interaction Facts™, respectively. Central nervous depressant agents and antiemetics were the most commonly involved groups in DDIs. A higher number of non-antineoplastic drugs was related with potential DDIs [adjusted-OR 1.398 and 1.613 by Micromedex™ and Drug Interaction Facts™, respectively]. CONCLUSION The prevalence of potential DDIs was widely variable among databases. The main risk factor associated with DDIs was a higher number of non-antineoplastic medicines.

Pharmacoepidemiological study of drug-drug interactions in onco-hematological pediatric patients.

BACKGROUND: Onco-hematological patients are particularly susceptible to drug-drug interactions (DDIs) because they often undergo multiple combined treatments. Some studies have analyzed the frequency of DDIs in adult patients with cancer; however, the prevalence of DDIs in children, and especially among pediatric cancer patients, remains unknown. OBJECTIVE: To determine the prevalence of DDIs in treatment sheets comparing two commonly used drug interaction databases, to describe the most common clinically relevant DDIs (CR-DDIs) and to investigate the risk factors associated with them. SETTING: An onco-hematological pediatric unit from a tertiary hospital in Spain. METHOD: A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient's treatment sheet was collected. Each medication list was screened through two databases: Thomson Micromedex™ and Drug Interaction Facts™. All identified DDIs were graded by their level of severity. Summary statistics were used to describe patient and disease characteristics, most often prescribed drugs, and frequency, types and classification of CR-DDIs. Multivariate analysis was used to identify risk factors associated with CRDDIs. MAIN OUTCOME MEASURE: Prevalence of CR-DDIs was measured as percentage. RESULTS: A total of 506 potential DDIs were detected in 150 treatment sheets. The prevalence of CR-DDIs by Micromedex database and Drug Interaction Facts database were 44.7 and 51.3% respectively. Amikacin, azole antifungals, antiemetics and cyclosporine were the most frequent drugs involved in CR-DDIs. In multivariate analysis, the main risk factor associated with increased odds for CR-DDIs was a higher number of drugs. CONCLUSION: The frequency of potential DDIs was related to a higher number of drugs, being immunosuppressant and azole antifungal agents the most commonly involved drugs. The lack of agreement between different databases enhances the complexity to detect drug interactions in clinical practice.