To update the classification system created by the Ruiz-Jarabo 2000 group to standardize detection, analysis, and recording of medication errors, with the aim of improving its capacity and functionality. The classification update was carried out by the Ruiz-Jarabo 2000 working group considering: a) other classifications used by incident reporting systems initiated after the original version had been created; b) suggestions offered by healthcare professionals with respect to the original version; and c) the experiences of the working group itself based on analyses of medication errors gathered in hospitals, and on analyses of reports notified to the ISMP-Spain medication error reporting and learning program. This article presents the updated version of the medication error classification system and describes the main changes made on to the different sections and categories. The new version may prove to be a useful tool for analyzing and reporting errors with regard to those detected within the framework of activities for improving safety in hospitals and primary care, as well as for those detected as a direct result of patient safety research. Thus, this document is expected to improve medication safety information management in such a way as to allow data to be used ever more efficiently for making medication use systems safer for patients.

Aims: Imatinib is considered the standard first‐line treatment in newly diagnosed patients with chronic‐phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model‐based predictions through Bayesian forecasting computed by each model at different treatment occasions.Methods: A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction‐ and simulated‐based diagnostics together with Bayesian forecasting analysis.Results: Fourteen imatinib popPK studies were included for model‐performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady‐state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction‐based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter‐occasion variability contributed to reducing bias and improving individual model‐based predictions.Conclusions: Imatinib popPK studies developed in Caucasian subjects including α1‐acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model‐based individual prediction performance trough Bayesian forecasting.

El riesgo de la asistencia sanitaria en general, especialmente el motivado por los errores de medicación, es un grave problema con gran repercusión humana, asistencial y económica, que está siendo abordado de forma prioritaria por las autoridades sanitarias de algunos países. Sin embargo, en España no se ha tomado conciencia de su trascendencia, aunque su magnitud en nuestro país sea similar a la de los países de su entorno. Las actividades de los gestores de riesgos en los centros sanitarios están evolucionando, adoptando una actitud más preventiva y anticipativa, que supone la colaboración con otros profesionales sanitarios en el desarrollo de programas de prevención de riesgos, entre ellos los de prevención de errores de medicación. En este artículo se describen los principales fundamentos de las estrategias de mejora de la seguridad y se expone el esquema de un programa de gestión de riesgos aplicado a la prevención de los errores de medicación a nivel hospitalario. En resumen, se postula que los gestores de riesgos formen parte de un equipo que debe analizar las causas de los incidentes que se producen, utilizar la información para implantar acciones de mejora, e introducir los principios de gestión de riesgos de una forma integral en las actividades y en la cultura de las organizaciones para, en definitiva, crear y mantener cambios significativos en los sistemas que mejoren la calidad de la prestación de servicios sanitarios y la seguridad de los pacientes.

(1) Background: The aim of this study was to explore the valproic acid (VPA) pharmacokinetic characteristics in a large population of pediatric and adult Caucasian patients and to establish a robust population pharmacokinetic (PopPK) model. (2) Methods: A total of 2527 serum VPA samples collected from 1204 patients included in a therapeutic drug monitoring program were retrospectively analyzed. Patients were randomly assigned to either a model development group or an external evaluation group. PopPK analysis was performed on 1751 samples from 776 patients with NONMEM using a nonlinear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment and comedication variables on the apparent clearance (CL/F) of VPA was studied. The bootstrap method was used to evaluate the final model internally. External evaluation was carried out using 776 VPA serum samples from 368 patients. (3) Results: A one-compartment model with first-order absorption and elimination successfully described the data. The final model included total body weight, age and comedication with phenytoin, phenobarbital and carbamazepine with a significant impact on VPA elimination. Internal and external evaluations demonstrated the good predictability of the model. (4) Conclusions: A PopPK model of VPA in Caucasian patients was successfully established, which will be helpful for model-informed precision dosing approaches in clinical patient care.

Objective To determine the utility of a tool (TRIGGER-CHRON) for identifying adverse drug events (ADEs) associated with the administration of high-alert medications in elderly patients with multimorbidity and to determine the medications most frequently implicated. Methods A retrospective observational study was conducted at 12 Spanish hospitals. A random sample of five medical records from each hospital was selected weekly for review over a 12-week period. We included patients aged 65 and over with multimorbidities, hospitalised for >48 hours. ADEs detected by the 32 TRIGGER-CHRON signals and caused by high-alert medications included on the Spanish HAMC list for chronic patients were selected for analysis. Triggers identified and ADEs detected were recorded. The severity and preventability of the ADEs were evaluated. The positive predictive value (PPV) of each trigger was calculated. Results On 720 charts reviewed, 908 positive triggers were identified that led to the detection of 158 ADEs caused by at least one high-alert medication on the HAMC list. These ADEs occurred in 139 patients (prevalence 19.3/100 admissions). The majority of ADEs were mild and 59.5% were deemed preventable. The drugs most frequently associated with ADEs were corticosteroids, loop diuretics, opioid analgesics and oral anticoagulants. Fifteen triggers had PPVs ≥20%. Six triggers (serum glucose >110 mg/dL, abrupt cessation of medication, oversedation/lethargy, hypotension, adverse reaction recorded and constipation) accounted for 69.8% of the ADEs identified. Conclusions Applying the TRIGGER-CHRON to hospitalised patients with multimorbidity in 12 Spanish centres allowed detection of one adverse event caused by a high-alert drug for every four patients, which were preventable in a large proportion of patients. This confirms the need to establish interventions that reduce harm with these medications. We believe that TRIGGER-CHRON can be a useful tool to measure this harm and to determine the effects of medication safety improvement programmes as they are implemented.