Dosificación de ceftriaxona en el paciente crítico hipoproteinémico: un factor más a tener en cuenta / Dosage of ceftriaxone in the critical hypoproteinemic patient: another factor to take into account

Introducción: El objetivo principal del estudio fue evaluar la necesidad de ajuste posológico de ceftriaxona en pacientes críticos hipoproteinémicos. Pacientes y métodos: Estudio observacional y retrospectivo, llevado a cabo en la unidad de cuidados intensivos (UCI) del Hospital General Universitario de Ciudad Real (médico-quirúrgica de 21 camas), en el que se incluyeron pacientes tratados con ceftriaxona en la UCI desde enero de 2014 a diciembre de 2019 y se clasificaron en dos grupos al inicio del tratamiento: pacientes normoproteinémicos (proteínas totales >5,5g/dl) e hipoproteinémicos (proteínas totales ≤5,5g/dl).Variables principales: Edad, sexo, APACHE II, diagnóstico-localización del foco infeccioso, estancia en UCI, dosis de ceftriaxona, pauta posológica, tratamiento antibiótico concomitante, empírico o dirigido, necesidad de cambio de tratamiento, días de antibioterapia y mortalidad. Resultados: Se incluyeron 98 pacientes (44 normoproteinémicos y 54 hipoproteinémicos). No se obtuvieron diferencias estadísticamente significativas entre las características basales de ambos grupos, exceptuando la localización del foco, siendo respiratorio con mayor frecuencia en el grupo de pacientes normoproteinémicos (p=0,044). Se obtuvieron diferencias estadísticamente significativas a favor del grupo de pacientes normoproteinémicos para: estancia en UCI (p=0,001), necesidad de cambio de tratamiento antibiótico (p=0,004), días de antibioterapia (p=0,007) y mortalidad (p=0,046). Conclusión: Los resultados terapéuticos obtenidos en el grupo de pacientes críticos hipoproteinémicos tratados con ceftriaxona ponen en evidencia la necesidad de considerar la hipoproteinemia como un factor que podría condicionar dicho resultado si se emplean las pautas posológicas de tratamiento habituales. (AU)

Introduction: The main objective of the study was to evaluate the need for posologic adjustment of ceftriaxone in critical hypoproteinemic patients. Patients and methods: Observational and retrospective study, carried out in the intensive care unit (ICU) of the General University Hospital of Ciudad Real (21-bed medical-surgical), which included patients treated with ceftriaxone in the ICU from January 2014 to December 2019 and classified into two groups at the beginning of treatment: normoproteinemic (total proteins >5.5 g/dl) and hypoproteinemic (total proteins ≤5.5g/dl) patients.Main variables: Age, sex, APACHE II, diagnosis-location of the infectious site, ICU stay, ceftriaxone dose, dosage regimen, concomitant antibiotic treatment, empirical or targeted antibiotic treatment, need to change treatment, days of antibiotic therapy and mortality. Results: 98 patients were included (44 normoproteinemics and 54 hypoproteinemics).No statistically significant differences were obtained between the basal characteristics of both groups, except for the location of the infectious site, being respiratory more frequently in the group of normoproteinemic patients (p=0.044).Statistically significant differences were obtained in favour of the group of normoproteinemic patients for: stay in ICU (p=0.001), need for change of antibiotic treatment (p=0.004), days of antibiotherapy (p=0.007) and mortality (p=0.046). Conclusion: The therapeutic results obtained in the group of critical hypoproteinemic patients treated with ceftriaxone show the need to consider hypoproteinemia as a factor that could condition such result if the usual treatment dosage guidelines are used.

Modelo predictivo preliminar para la identificación de pacientes con oportunidades de mejora farmacoterapéutica / Preliminary prediction model for identifying patients with the possibility of pharmacotherapy improvement

Objetivo Desarrollar un modelo predictivo para la identificación de pacientes con oportunidades de mejora en la farmacoterapia durante el proceso de validación farmacéutica de la prescripción. Método Estudio transversal de dos meses de duración realizado en los servicios de medicina interna y enfermedades infecciosas. La detección de oportunidades de mejora en la calidad de la farmacoterapia se efectuó mediante validación farmacéutica de la prescripción. A partir de la información obtenida en este proceso se realizó un análisis mediante regresión logística multivariante utilizando como factores pronóstico variables demográficas, farmacoterapéuticas y clínicas relacionadas con la identificación en el paciente de problemas relacionados con la medicación. La validez predictiva del modelo se evaluó mediante la curva de rendimiento diagnóstico y el cálculo de su área. Resultados El modelo predictivo final incluyó las variables edad, fármacos cardiovasculares (digoxina) y fármacos en los que se recomienda el ajuste posológico por insuficiencias orgánicas. El análisis de la curva ROC mostró un área bajo la curva estimada del 84,0% (IC95%: 80,5–87,1), un valor de sensibilidad del 28% (IC95%: 24,07–32,19), un valor de especificidad del 99,10% (IC95%: 97,80–99,73), un valor predictivo para positivos del 77,78% y un valor predictivo para negativos del 92,41%.ConclusiónEl modelo predictivo obtenido permite la detección poblacional del riesgo de seguridad farmacoterapéutica en los pacientes adultos ingresados en los servicios hospitalarios seleccionados. Las variables predictoras manejadas por el modelo son habitualmente utilizadas en la práctica asistencial diaria (AU)

Objective To develop a prediction model for identifying patients with the possibility of improving pharmacotherapy during the process of pharmaceutical validation of the prescription. Method Cross-sectional study over two months, performed in the Internal Medicine and Infectious Disease divisions. Detecting opportunities for improving quality of pharmacotherapy is done by means of a pharmacist's validation of the prescription. Based on the information we obtained through this process, we performed a multivariate logistic regression analysis using as prognostic factors the demographic, pharmacotherapy and clinical variables related to identifying any drug-related problems (DRPs) in the patient. The model's prediction validity was assessed using the diagnostic performance curve and calculating the area under it. Results The final prediction model included the variables age, cardiovascular drugs (digoxin) and drugs for which a dosage adjustment is recommended in the case of organ failures. Analysis of the ROC curve showed an estimated area under the curve AUCROC) of 84.0% (95% CI: 80.5–87.1), a sensitivity value of 28% (95% CI: 24.07–32.19), a specificity value of 99.10% (95% CI: 97.80–99.73), a positive predictive value of 77.78% and a negative predictive value of 92.41%.ConclusionThe resulting prediction model enables population-based detection of pharmacotherapy safety risks in adult patients admitted to the selected hospital units. The predictive variables used by the model are commonly used in daily practice (AU)

[Preliminary prediction model for identifying patients with the possibility of pharmacotherapy improvement]. / Modelo predictivo preliminar para la identificación de pacientes con oportunidades de mejora farmacoterapéutica.

OBJECTIVE: To develop a prediction model for identifying patients with the possibility of improving pharmacotherapy during the process of pharmaceutical validation of the prescription. METHOD: Cross-sectional study over two months, performed in the Internal Medicine and Infectious Disease divisions. Detecting opportunities for improving quality of pharmacotherapy is done by means of a pharmacist's validation of the prescription. Based on the information we obtained through this process, we performed a multivariate logistic regression analysis using as prognostic factors the demographic, pharmacotherapy and clinical variables related to identifying any drug-related problems (DRPs) in the patient. The model's prediction validity was assessed using the diagnostic performance curve and calculating the area under it. RESULTS: The final prediction model included the variables age, cardiovascular drugs (digoxin) and drugs for which a dosage adjustment is recommended in the case of organ failures. Analysis of the ROC curve showed an estimated area under the curve AUCROC) of 84.0% (95% CI: 80.5-87.1), a sensitivity value of 28% (95% CI: 24.07-32.19), a specificity value of 99.10% (95% CI: 97.80-99.73), a positive predictive value of 77.78% and a negative predictive value of 92.41%. CONCLUSION: The resulting prediction model enables population-based detection of pharmacotherapy safety risks in adult patients admitted to the selected hospital units. The predictive variables used by the model are commonly used in daily practice.

Cisplatin preparation error; patient management and morbidity.

INTRODUCTION: Antineoplastic drug therapy errors represent a high iatrogenic potential due to antineoplastic drugs narrow therapeutic ranges and the complexity of chemotherapy regimens that may increase the risk of morbidity and mortality for oncology patients. SETTING: We report a 57-year-old man with head and neck cancer who mistakenly received 180 mg/ m(2) of cisplatin overdose despite the safety measures and validations carried out during preparation. The patient developed moderate nausea and vomiting, acute renal failure, hearing difficulty (tinnitus), and severe myelodepression. PATIENT MANAGEMENT: Prophylactic and symptomatic treatments were applied in order to prevent and correct toxicity during the 9 days stay at hospital. RESULT: He recovered with mild tinnitus and mild renal impairment as the only sequelae. This case presents a hospital stay and treatment quite different to others used to reverse all cisplatin overdose toxicity and it shows the benefits of prompt management.