Exposición a fármacos citotóxicos en el personal sanitario / Exposure to cytotoxic drugs among health care professionals

Objetivo: Cuantificar los niveles de exposición del personal sanitario a fármacos citotóxicos con el fin de establecer el nivel umbral de exposición e implantar medidas para incrementar la protección y seguridad. Material y método La cuantificación de la contaminación de 5-fluorouracilo, gemcitabina y ciclofosfamida se llevó a cabo en las superficies de las siguientes áreas: cabina de seguridad biológica clase II tipo B3 (S1), mesa de preparación de tratamientos en antecámara (S2) y mesa de la sala de administración en hospital de día (S3). Se tomaron muestras de las superficies con un paño absorbente a tiempo t0, previo inicio de la sesión de trabajo, y t1, tras 3 h de trabajo mediante arrastre. En cada superficie se calculó el valor de la masa mediana respecto al valor basal y los percentiles 90, 75, 50 y 25 para cada citotóxico en μg/m2.Se comprobó la normalidad de la distribución con la prueba Shapiro-Wilk. El análisis estadístico incluyó las pruebas U de Mann-Whitney, Kruskal-Wallis y Wilcoxon. Se fijó el nivel de significación estadística para valores de p < 0,05.ResultadosSe recogieron un total de 90 muestras en total, 30 muestras por cada superficie de estudio. La masa media registrada de cualquier compuesto citotóxico fue superior en S1 y t1, con un valor de p = 0,017 y p = 0,004, respectivamente. Para cada fármaco citotóxico se fijó como valor objetivo el percentil 25 donde se obtuvieron valores de contaminación indetectables. Conclusiones La introducción de un programa de monitorización continua de superficies de diversos compuestos citotóxicos es esencial para fijar unos niveles aceptables de contaminación residual y reducir la exposición ocupacional (AU)

Objective: To quantify levels of exposure to cytotoxic drugs among health professionals in order to establish an exposure threshold and implement measures to increase protection and safety Material and method: Contamination with 5-fluorouracil, gemcitabine and cyclophosphamide was measured on work surfaces in the following areas: a class II type B3 biological safety cabinet(S1), a treatment prep table in an antechamber (S2) and a desk from the administrative room in the Outpatient Unit (S3). We took samples from the work surfaces by wiping them with an absorbent cloth at time t0, prior to the work session, and at t1 after three hours of work. For each surface, we calculated the median mass value with respect to the baseline value and the90th, 75th, 50th and 25th percentiles for each cytotoxin in g/m2.Distribution normality was assessed using the Shapiro-Wilk test. Statistical analysis included the Kruskal-Wallis and Mann-Whitney-Wilcoxon tests. Statistical significance was established for values of P<.05.Results: We gathered a total of 90 samples, 30 from each of the studied work surfaces. The mean recorded mass of any of the cytotoxic compounds was higher for S1 and t1, with values of P=.017 and P=.004 respectively. The target value for each cytotoxic drug was established at the 25th percentile, where undetectable contamination values were obtained. Conclusions: Introducing a continuous programme to monitor work surfaces for an array of cytotoxic compounds is fundamental in order to establish acceptable levels of residual contamination and reduce exposure in the workplace (AU)

[Exposure to cytotoxic drugs among health care professionals]. / Exposición a fármacos citotóxicos en el personal sanitario.

OBJECTIVE: To quantify levels of exposure to cytotoxic drugs among health professionals in order to establish an exposure threshold and implement measures to increase protection and safety. MATERIAL AND METHOD: Contamination with 5-fluorouracil, gemcitabine and cyclophosphamide was measured on work surfaces in the following areas: a class II type B3 biological safety cabinet (S(1)), a treatment prep table in an antechamber (S(2)) and a desk from the administrative room in the Outpatient Unit (S(3)). We took samples from the work surfaces by wiping them with an absorbent cloth at time t(0), prior to the work session, and at t(1) after three hours of work. For each surface, we calculated the median mass value with respect to the baseline value and the 90th, 75th, 50th and 25th percentiles for each cytotoxin in µg/m(2). Distribution normality was assessed using the Shapiro-Wilk test. Statistical analysis included the Kruskal-Wallis and Mann-Whitney-Wilcoxon tests. Statistical significance was established for values of P<.05. RESULTS: We gathered a total of 90 samples, 30 from each of the studied work surfaces. The mean recorded mass of any of the cytotoxic compounds was higher for S(1) and t(1), with values of P=.017 and P=.004 respectively. The target value for each cytotoxic drug was established at the 25th percentile, where undetectable contamination values were obtained. CONCLUSIONS: Introducing a continuous programme to monitor work surfaces for an array of cytotoxic compounds is fundamental in order to establish acceptable levels of residual contamination and reduce exposure in the workplace.

[Cutaneous toxicity associated with cetuximab treatment in metastatic colorectal cancer]. / Toxicidad cutánea asociada a cetuximab en cáncer colorrectal metastásico.

OBJECTIVE: To evaluate the impact and type of side-effects in patients treated with cetuximab and provide a description of the general measures and treatment. METHODS: Retrospective safety study. We included all patients that received cetuximab from January to December 2009. All information was obtained from the Pharmacy and Oncology Department's Access databases and reviewed the patient's medical history. All data was registered in an Excel workbook. Skin toxicity was graded by the current National Cancer Institute-Common Toxicity Criteria (NCI-CTC). RESULTS: During the study period 43 patients received treatment with cetuximab. Acneiform eruption was present in 30 of the cases (69.8%): 14 patients with grade 1 (48.3%), 13 with grade 2 (44.8%) and 3 with grade 3 (10.3%). These adverse effects appeared in a median of seven (4-28) days. In a median of 40 (20-56) days, ten patients (23.3%) presented xerosis, and three (7%) suffered painful fissures in hands and feet after a median of 28 (21-35) days. Paronychia was present in two patients after a median of 42 (35-49) days. Finally, an alteration in hair growth was observed in two patients with overgrowth of facial hair and one patient with overgrowth of the eyelashes. Five patients presented important conjunctivitis. Three infusion reactions occurred. A grade-based treatment algorithm was used for all patients that presented cutaneous toxicity. CONCLUSIONS: A considerable number of patients treated with cetuximab develop dermatological side-effects which left untreated could represent a threat to the efficacy of the therapy. Therefore effective management is mandatory, patient education and immediate treatment based on a grade-based algorithm to alleviate symptoms is necessary, so that patient compliance is guaranteed.

Aceptación de recomendaciones en el paciente con oportunidades de mejora farmacoterapéutica / Doctors’ acceptance of recommendations for patients with the opportunity for pharmacotherapy improvement

Objetivo: Identificar y cuantificar la influencia de diferentes variables en la implantación de medidas de optimización farmacoterapéutica en pacientes ingresados. Método Estudio descriptivo, transversal. Período: 2000–2007. Ámbito: hospital general universitario público (25.000 pacientes ingresados/año).El Programa de Mejora de la Calidad de la Farmacoterapia y la Seguridad del Paciente implantado da cobertura al 30% de los pacientes. A partir de los registros del aplicativo Atefarm® Farmis, se analizaron las recomendaciones farmacoterapéuticas (RF) realizadas por los farmacéuticos al médico. Las variables seleccionadas fueron las siguientes: riesgo del medicamento (problema relacionado con el medicamento [PRM]) (0, bajo; 1, alto), categoría del PRM, (0, indicación; 1, efectividad; 2, seguridad), gravedad potencial (escala 1–5), impacto de la RF (0, efectividad; 1, seguridad; 2, eficiencia) e implantación de la RF (sí/no).Se calculó la frecuencia (%) y el intervalo de confianza del 95% (IC95%) de las variables categóricas y se realizó un análisis de regresión logística multivariante para identificar el grado de influencia de las variables en la implantación de las RF. Resultados Se identificaron 7.920 PRM en 4.680 pacientes. En el 85% (6.762) de los PRM se realizó una RF, que se implantó en el 83% (IC95%: 74,2–89,8). La gravedad potencial del PRM superior o igual a 2 (OR: 1,57; IC95%: 1,27–1,94) y la categoría del PRM de efectividad y seguridad (OR: 1,19; IC95%: 1,02–1,39) se manifestaron como determinantes de la implantación de la RF en el paciente. Conclusiones La probabilidad de implantación de RF en el paciente está relacionada con la gravedad potencial y la categoría del PRM identificado. Así, las recomendaciones orientadas a mejorar la efectividad de la farmacoterapia o la seguridad del paciente, y con consecuencias clínicas potenciales presentan mayor éxito en su aplicación al paciente(AU)

Objective: To identify and quantify the influence of different variables on the implementation of pharmacotherapy optimisation measures in hospitalised patients. Method: Descriptive transversal study. Period: 2000—2007. Environment: public university general hospital (25,000 patients admitted/year).The Programme implemented to improve pharmacotherapy quality and patient safety covers30% of all patients. Using records from the Atefarm®Farmis application, we analysed pharmacotherapy recommendations (PRs) made by pharmacists to doctors. The selected variables were the following: Risk of the medication for ADE (1-high, 0-low), ADE category, (0-indication, 1-effectiveness, 2-safety), potential severity (scale of 1 to 5), impact of the PR (0-effectiveness,1-safety, 2-efficiency) and implementation of the PR (yes/no).We calculated the frequency (%) and 95% CI for the categorical variables and performeda multivariate logistical regression analysis to identify the variables’ degree of influence onimplementing the PRs. Results: We identified 7920 ADEs in 4680 patients. A PR was issued in 85% of the cases (6762),and it was implemented in 83% (95% CI 74.2—89.8). Potential severity of the ADE ≥2 (OR 1.57;95% CI 1.27—1.94), and ADE category for effectiveness and safety (OR 1.19; 95% CI 1.02—1.39)were shown to be determining factors for implementing the PR for the patient. Conclusions: The probability that a PR will be implemented for a patient is related to the potential severity and the category of the identified ADE. Therefore, recommendations intended to improve effectiveness of pharmacotherapy or patient safety, and those with potential clinical consequences have a greater chance of being applied to a patient (AU)

Doctors' acceptance of recommendations for patients with the opportunity for pharmacotherapy improvement.

OBJECTIVE: To identify and quantify the influence of different variables on the implementation of pharmacotherapy optimisation measures in hospitalised patients. METHOD: Descriptive transversal study. PERIOD: 2000-2007. ENVIRONMENT: public university general hospital (25,000 patients admitted/year). The Programme implemented to improve pharmacotherapy quality and patient safety covers 30% of all patients. Using records from the Atefarm(®) Farmis application, we analysed pharmacotherapy recommendations (PRs) made by pharmacists to doctors. The selected variables were the following: Risk of the medication for ADE (1-high, 0-low), ADE category, (0-indication, 1-effectiveness, 2-safety), potential severity (scale of 1 to 5), impact of the PR (0-effectiveness, 1-safety, 2-efficiency) and implementation of the PR (yes/no). We calculated the frequency (%) and 95% CI for the categorical variables and performed a multivariate logistical regression analysis to identify the variables' degree of influence on implementing the PRs. RESULTS: We identified 7,920 ADEs in 4,680 patients. A PR was issued in 85% of the cases (6,762), and it was implemented in 83% (95% CI 74.2-89.8). Potential severity of the ADE ≥2 (OR 1.57; 95% CI 1.27-1.94), and ADE category for effectiveness and safety (OR 1.19; 95% CI 1.02-1.39) were shown to be determining factors for implementing the PR for the patient. CONCLUSIONS: The probability that a PR will be implemented for a patient is related to the potential severity and the category of the identified ADE. Therefore, recommendations intended to improve effectiveness of pharmacotherapy or patient safety, and those with potential clinical consequences have a greater chance of being applied to a patient.

Genetic polymorphisms and individualized tacrolimus dosing.

BACKGROUND: Genetic polymorphisms of metabolism enzymes or intestinal drug transporters may affect pharmacokinetic responses to immunosuppressive drugs in renal transplant recipients. We sought to identify the frequency of genetic polymorphisms and their importance for individualization of tacrolimus doses. PATIENTS AND METHODS: We performed an observational study in 35 renal transplant recipients treated with tacrolimus, mycophenolate mofetil, and corticosteroids. Tacrolimus concentrations were determined by immunoanalysis (IMx method; Abbott Diagnostics, Abbott Park, Ill), on 11 blood samples per patient during the first 6 weeks after renal transplantation. For each patient, we calculated the mean value and its standard error (SEM) of the concentration/dose ratio (ng/mL/mg) of tacrolimus. The pharmacogenetic analysis included single nucleotide polymorphisms (SNPs) in the CYP3A5 (CYP3A5*3 (A6986G), CYP3A5*6 (G14690A), MDR1 (C3435T and G2677T/A) and PXR (C-25385T) genes. RESULTS: Of the patients, 62.8% (n=22) were men and the overall mean age was 55 years (95% confidence interval, 48.7-62.7). The SNP distribution was: CYP3A5*3: G/G=82.9%, A/G=17.1%; CYP3A5*6: G/G=88.6%, G/A=11.4%; MDR1 C3435T: C/C=25.7%, C/T=62.9%, T/T=11.4%; for MDR1 G2677T/A: G/G=22.9%, G/T=65.7%, T/T=11.4% and for PXR: C/T=85.7%, T/T=14.3%. Tacrolimus concentration/dose ratios in heterozygote patients for CYP3A5*3 genotypes was >120% lower than for the homozygote CYP3A5*3 genotype (0.65±0.04 vs 1.45±0.05; P<.0001). Wild-type MDR1 (3435 C/C) genotype patients showed up to 40% lower concentration/dose ratios compared with heterozygote and homozygote genotypes (C/C; 1±0.07 vs C/T; 1.4±0.06 vs T/T; 1.37±0.09; P<.0001). CONCLUSION: Intestinal absorption and metabolism of tacrolimus was significantly affected by the SNPs in the CYP3A5 and MDR1 genes, which may offer a useful tool to optimize tacrolimus dosing after renal transplantation.

Predicting risk of acute rejection in patients with kidney transplants.

OBJECTIVE: Create a model to predict the risk of acute rejection of kidney transplant considering variables related to the immunosuppressant agent used, the receiver, the donor and the transplanted organ. METHODS: Cohort study in a population of 68 patients with kidney transplants being treated with tacrolimus triple therapy. Predicting the risk of acute rejection was carried out with a logistic regression analysis using age, sex, re-transplant status, number of HLA incompatibilities, cold ischaemia time, acute tubular necrosis, induction with basiliximab or thymoglobulin and treatment type as explanatory variables. The contribution of variables associated with determining the blood concentration of tacrolimus was also evaluated; these variables include the average blood concentration, the number of values below and included in the pre-defined therapeutic interval, and the time during which those values remained within that interval. RESULTS: The logistic regression analysis indicates that the risk of acute rejection depends on the acute tubular necrosis (OR: 3; CI 95%, 0.7 to 13.2) and on the time that the blood concentrations of tacrolimus remains within the therapeutic interval (OR: 0.8; CI 95%, 0.7 to 0.9). The final model presents an optimal discrimination power (AUCROC: 77%; CI 95%, 62% to 92%). For the selected cut-off point (probability greater than or equal to 0.24) the model shows a sensitivity of 83% (CI 95%, 74 to 90%) and a specificity of 71% (CI 95%, 61 to 80%). CONCLUSIONS: In patients with kidney transplants, the presence of acute tubular necrosis, together with the time the blood concentration of tacrolimus remained within the predetermined therapeutic interval, permitted the identification of patients with a higher probability of having an acute rejection episode during the first two weeks following the transplant.

Predicción de riesgo de rechazo agudo en pacientes con trasplante renal / Predicting risk of acute rejection in patients with kidney transplants

Objetivo: Construir un modelo para predecir el riesgo de rechazo agudo al trasplante renal considerando variables relacionadas con el tratamiento inmunosupresor instaurado, el receptor, el donante y el órgano trasplantado. Método: Estudio de cohortes en una población de 68 pacientes con trasplante renal en tratamiento con tacrolimus en triple terapia. La predicción del riesgo de rechazo agudo se realizó mediante un análisis de regresión logística utilizando como variables explicativas la edad, sexo, presencia de retrasplante, número de incompatibilidades HLA, tiempo de isquemia fría, necrosis tubular aguda, inducción con basiliximab o timoglobulina y tipo de tratamiento. También se evaluó la contribución de variables asociadas a la determinación de la concentración sanguínea de tacrolimus, entre ellas la media de la concentración sanguínea, el número de valores por debajo e incluidos en el intervalo terapéutico predefinido, y el tiempo que dichos valores permanecían en las condiciones referidas. Resultados: El análisis de regresión logística indica que el riesgo de rechazo agudo depende de la necrosis tubular aguda (odds ratio [OR] = 3; intervalo de confianza [IC] del 95 %, 0,7 a 13,2) y del tiempo que las concentraciones sanguíneas de tacrolimus permanecen dentro del intervalo terapéutico (OR = (..) (AU)

Objective: Create a model to predict the risk of acute rejection of kidney transplant considering variables related to the immunosuppressant agent used, the receiver, the donor and the transplanted organ. Methods: Cohort study in a population of 68 patients with kidney transplants being treated with tacrolimus triple therapy. Predicting the risk of acute rejection was carried out with a logistic regression analysis using age, sex, re-transplant status, number of HLA incompatibilities, cold ischaemia time, acute tubular necrosis, induction with basiliximab or thymoglobulin and treatment type as explanatory variables. The contribution of variables associated with determining the blood concentration of tacrolimus was also evaluated; these variables include the average blood concentration, the number of values below and included in the pre-defined therapeutic interval, and the time during which those values remained within that interval. Results: The logistic regression analysis indicates that the risk of acute rejection depends on the acute tubular necrosis (OR: 3; CI 95 %, 0.7 to 13.2) and on the time that the blood concentrations of tacrolimus remains within the therapeutic interval (OR: 0.8; CI 95 %, 0.7 to 0.9).The final model presents an optimal discrimination power (AUCROC:77 %; CI 95 %, 62 % to 92 %). For the selected cut-off point (probability greater than or equal to 0.24) the model shows a sensitivity of 83 % (CI 95 %, 74 to 90 %) and a specificity of 71 % (CI 95 %, 61 to 80 %).Conclusions: In patients with kidney transplants, the presence of acute tubular necrosis, together with the time the blood concentration of tacrolimus remained within the predetermined therapeutic interval, permitted the identification of patients with a higher probability of having an acute rejection episode during the first two weeks following the transplant (AU)