Evaluación de la adherencia al tratamiento con antipsicóticos inyectables de liberación prolongada dentro de un programa de seguimiento farmacoterapéutico / Assessment of adherence to treatment with injectable extended-release antipsychotics within a pharmacotherapeutic follow-up program

Objetivo: Describir un programa de seguimiento farmacoterapéutico de antipsicóticos inyectables de liberación prolongada y evaluar la adherencia de los pacientes incluidos. Método: Se describe un programa de prescripción electrónica, validación y dispensación de antipsicóticos a salud mental y centros de salud, coordinado entre farmacia de hospital y de atención primaria. La adherencia al tratamiento se evaluó mediante un estudio prospectivo, observacional y transversal de un mes realizado en un área sanitaria a más de 500.000 habitantes, en el que se incluyeron todos los pacientes en tratamiento con un antipsicótico inyectable de liberación prolongada. Las variables recogidas fueron: medicamento administrado, frecuencia de admi nis tra ción, centro de administración y si el paciente acudía o no a la administración, considerando que acudía si lo hacía en ± 7 días. Resultados: Se incluyeron un total de 919 pacientes y 1.073 consultas programadas. En la recogida de datos participaron 11 unidades de salud mental y 40 centros de salud. En un 95,7% (1.027) de los casos, los pacientes acudieron a la administración del antipsicótico inyectable de liberación prolongada. No se encontraron diferencias en la adherencia entre los medicamentos ni entre frecuencias de administración, pero sí con respecto al centro donde se administraba el medicamento (unidades de salud mental frente a centros de salud), presentando una ligera mayor adherencia los pacientes de las unidades de salud mental (97,6% frente al 91,1%; p < 0,001). Conclusiones: La elevada adherencia conseguida revela que el programa de seguimiento descrito es efectivo. En el futuro son necesarios estudios de mayor duración que confirmen esta tendencia

Objective: To describe an injectable extended-release antipsychotic pharmacotherapeutic follow-up program and to assess adherence among patients included in the program. Method: A coordinated program is described involving hospital and primary care pharmacy, which included electronic prescription, reviewing, and dispensing of injectable antipsychotic agents in mental health and primary health care centers. Adherence to treatment was assessed in a 1-month prospective observational cross-sectional study which included all patients under treatment with injectable extended-release antipsychotics in a health area of more than 500,000 inhabitants. The variables collected were: medication administered, frequency of administration, administration center, and whether or not the patient attended the center. Patients were considered to have adhered to treatment if they had attended their appointments within a margin of ± 7 days. Results: A total of 919 patients and 1,073 appointments were included. Eleven mental health units and 40 primary health care centers participated in data collection. In 95.7 % (1,027) of cases, the patients attended the appointment. No differences were found in adherence between drugs or administration frequency. However, differences were found between mental health units and primary health care centers. Patient adherence was slightly higher in mental health units (97.6% vs 91.1%; P < 0.001). Conclusions: The high adherence rate shows that the described followup program is effective. Further long-term studies are needed to confirm this trend

Assessment of adherence to treatment with injectable extended-release antipsychotics within a pharmacotherapeutic follow-up program.

OBJECTIVE: To describe an injectable extended-release antipsychotic pharmacotherapeutic follow-up program and to assess  adherence among patients included in the program. METHOD: A coordinated program is described involving hospital and  primary care pharmacy, which included electronic prescription,  reviewing, and dispensing of injectable antipsychotic agents in mental  health and primary health care centers. Adherence to treatment was  assessed in a 1-month prospective observational cross-sectional study  which included all patients under treatment with injectable extended- release antipsychotics in a health area of more than 500,000  inhabitants. The variables collected were: medication administered,  frequency of administration, administration center, and whether or not  the patient attended the center. Patients were considered to have  adhered to treatment if they had attended their appointments within a  margin of ± 7 days. Results: A total of 919 patients and 1,073 appointments were included. Eleven mental health units and 40 primary health care centers participated in data collection. In 95.7 % (1,027) of cases, the  patients attended the appointment. No differences were found in  adherence between drugs or administration frequency. However,  differences were found between mental health units and primary health  care centers. Patient adherence was slightly higher in mental health units (97.6% vs 91.1%; P < 0.001). CONCLUSIONS: The high adherence rate shows that the described  followup program is effective. Further long-term studies are needed to  confirm this trend.

Objetivo: Describir un programa de seguimiento farmacoterapéutico de antipsicóticos inyectables de liberación prolongada y evaluar la  adherencia de los pacientes incluidos.Método: Se describe un programa de prescripción electrónica,  validación y dispensación de antipsicóticos a unidades de salud mental y  centros de salud, coordinado entre farmacia de hospital y de atención primaria. La adherencia al tratamiento se evaluó mediante un estudio  prospectivo, observacional y transversal de un mes realizado en un área  sanitaria a más de 500.000 habitantes, en el que se incluyeron  todos los pacientes en tratamiento con un antipsicótico inyectable de  liberación prolongada. Las variables recogidas fueron: medicamento  administrado, frecuencia de administración, centro de administración y  si el paciente acudía o no a la administración, considerando que acudía  si lo hacía en ± 7 días. Resultados: Se incluyeron un total de 919 pacientes y 1.073 consultas programadas. En la recogida de datos participaron 11  unidades de salud mental y 40 centros de salud. En un 95,7% (1.027)  de los casos, los pacientes acudieron a la administración del  antipsicótico inyectable de  liberación prolongada. No se encontraron  diferencias en la adherencia entre los medicamentos ni entre frecuencias  de administración, pero sí con respecto al centro donde se  administraba el medicamento (unidades de salud mental frente a  centros de salud), presentando una ligera mayor adherencia los  pacientes de las unidades de salud mental (97,6% frente al 91,1%; p <  0,001).Conclusiones: La elevada adherencia conseguida revela que el  programa de seguimiento descrito es efectivo. En el futuro son  necesarios estudios de mayor duración que confirmen esta tendencia.

Devising of a risk map on the management of high risk alert medication in a high level university hospital / Elaboracion de un mapa de riesgos sobre el manejo de medicamentos de alto riesgo en un hospital universitario de elevada complejidad

Objective: To classify hospital units into three risk levels in order to define and prioritise improvement and training measures in each of them. Method: 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: ∑ (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. Results: 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. Conclusions: 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: ∑ (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

Devising of a risk map on the management of high risk alert medication in a high level university hospital.

OBJECTIVE: To classify hospital units into three risk levels in order to define and prioritise  improvement and training measures in each of them. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

Liquid chromatography tandem mass spectrometry determination of selected synthetic cathinones and two piperazines in oral fluid. Cross reactivity study with an on-site immunoassay device.

Since the past few years, several synthetic cathinones and piperazines have been introduced into the drug market to substitute illegal stimulant drugs such as amphetamine and derivatives or cocaine due to their unregulated situation. These emerging drugs are not usually included in routine toxicological analysis. We developed and validated a LC-MS/MS method for the determination of methedrone, methylone, mephedrone, 3,4-methylenedioxypyrovalerone (MDPV), fluoromethcathinone, fluoromethamphetamine, 1-(3-chlorophenyl)piperazine (mCPP) and 3-trifluoromethylphenylpiperazine (TFMPP) in oral fluid. Sample extraction was performed using Strata X cartridges. Chromatographic separation was achieved in 10min using an Atlantis(®) T3 column (100mm×2.1mm, 3µm), and formic acid 0.1% and acetonitrile as mobile phase. The method was satisfactorily validated, including selectivity, linearity (0.2-0.5 to 200ng/mL), limits of detection (0.025-0.1ng/mL) and quantification (0.2-0.5ng/mL), imprecision and accuracy in neat oral fluid (%CV=0.0-12.7% and 84.8-103.6% of target concentration, respectively) and in oral fluid mixed with Quantisal™ buffer (%CV=7.2-10.3% and 80.2-106.5% of target concentration, respectively), matrix effect in neat oral fluid (-11.6 to 399.7%) and in oral fluid with Quantisal™ buffer (-69.9 to 131.2%), extraction recovery (87.9-134.3%) and recovery from the Quantisal™ (79.6-107.7%), dilution integrity (75-99% of target concentration) and stability at different conditions (-14.8 to 30.8% loss). In addition, cross reactivity produced by the studied synthetic cathinones in oral fluid using the Dräger DrugTest 5000 was assessed. All the analytes produced a methamphetamine positive result at high concentrations (100 or 10µg/mL), and fluoromethamphetamine also at low concentration (0.075µg/mL).

Assessment of different mouthwashes on cannabis oral fluid concentrations.

Since the implementation of mandatory drug testing in drivers' oral fluid, several solutions to avoid an onsite positive result can be found on drug users' forums, especially for marijuana, including the use of different mouthwashes. Recently, a product for personal hygiene, Kleaner, has been sold for this purpose. The aims of this study were to assess the effect of water, whole milk, and Kleaner mouthwashes on tetrahydrocannabinol (THC) oral fluid concentrations, and those observed in passive smokers subjected to extreme contamination conditions. The study was performed on four days. On day 0, study information was given to the participants. On days 1, 2, and 3, 11 chronic cannabis users smoked their usual daily dose, and oral fluid specimens were collected before smoking (t=-0.5h) and at t=0.25, 0.5, 1, 3, 6, 12, and 24 h post-smoking. On day 1, participants rinsed their mouth with water before each specimen collection. On day 2, 5 participants rinsed their mouth with Kleaner and 6 with whole milk. On day 3, a specimen was collected before and after rinsing the mouth with water. Statistically significant lower concentrations were observed comparing concentrations in oral fluid specimens collected before and after a water rinse. However, maximum THC concentrations at t=0.25 h were >3-fold higher than the cut-off employed by the Spanish police (25 ng/mL) regardless of the use of any mouthwash. THC was also detected in the oral fluid of passive smokers subjected to extreme contamination conditions; however, concentrations were <25 ng/mL in all cases.