ABSTRACT
BACKGROUND: Interest in finding efficient ways to remove penicillin allergy alerts has grown as a result of awareness of the considerable excess of false-negative diagnoses in patients with penicillin allergy labels (90%-95%), the poorer course with non-ß-lactam antibiotics, the increase in bacterial resistance, and the fact that these problems can affect up to 20% of the population in some countries. The strategies proposed have generated many publications in countries where the number of allergists to conduct such studies is low. In many cases where delabeling is performed, the risk of ß-lactam allergy is low, and a single penicillin challenge is sufficient to delabel the alert. However, other less "ultrarapid" strategies can be used to administer a ß-lactam during an admission for infection and thus postpone delabeling until traditional drug allergy consultations. However, the definitive withdrawal of ß-lactam alerts is threatened by nonremoval of alerts in electronic health records and by the reactivation or nonsynchronization of alerts between electronic systems at different levels of care. Allergy departments need to reflect on how to implement practices that enable rapid and efficient delabeling of drug allergy alerts, especially in patients with major comorbidities.
ABSTRACT
Background: At the beginning of the COVID-19 pandemic many drugs were used with an uncertain benefit/risk profile that needed to be evaluated. The goal of this study was to analyse the incidence of adverse drug reactions (ADRs) and describe the drugs used in COVID-19 hospitalised patients at the beginning of the COVID-19 pandemic through the minimum basic data set (MBDS). Methods: Retrospective observational study that included hospitalised patients with COVID-19 at our centre between March and May 2020 who had ADRs coded in discharge/death medical reports according to the International Classification of Diseases (ICD-10). Those patients with ADRs ascribed to COVID therapy were selected and the causal relationship was evaluated using the Naranjo algorithm. Descriptive statistical analysis was used. Results: We identified 141 ADRs in 110 cases of hospitalisation due to COVID-19 that entailed an incidence of 9.66% (141/1459), CI95% 8.25-11.29. From the ADRs analysed, 60.3% (85/141) were ascribed to COVID therapy. Lopinavir/ritonavir represented 38.8% (33/85) of ADRs, glucocorticoids 23.5% (20/85) and hydroxychloroquine 9.4% (8/85). Out of the ADRs, 31.8% (27/85) were gastrointestinal disorders (probable lopinavir/ritonavir), 27.0% (23/85) blood glucose disorders (probable glucocorticoid) and 17.6% (15/85) hypertransaminasaemia (probable azithromycin, possible lopinavir/ritonavir, possible hydroxychloroquine, possible interferon). Regarding intensity, 64.7% (55/85) were mild cases, 29.4% (25/85) moderate and 5.9% (5/85) severe. The percentage of ADRs that did not require intervention were 24.7% (21/85), 32.9% (28/85) required pharmacological treatment, 40.0% (34/85) suspension of the drug, 1.2% (1/85) close monitoring and 1.2% (1/85) dose reduction. Conclusions: The incidence of ADR in COVID population that required admission at the beginning of the pandemic seems to be higher than in the general population. The MBDS proves to be a useful tool to trace ADRs. (AU)
Introducción: La llegada de la pandemia de COVID-19 supuso la utilización de muchos fármacos con un perfil de riesgo/beneficio incierto que debe ser evaluado. El objetivo de este estudio fue analizar la incidencia de reacciones adversas a medicamentos (RAM) y describir los medicamentos utilizados en pacientes hospitalizados por COVID-19 al comienzo de la pandemia a través del conjunto mínimo básico de datos (CMBD). Materiales y métodos: Estudio observacional retrospectivo que incluyó pacientes hospitalizados por COVID-19 en nuestro centro entre marzo y mayo de 2020 que presentaban RAM codificadas en los informes médicos de alta/exitus según la Clasificación Internacional de Enfermedades (CIE-10). Se seleccionaron los pacientes con RAM atribuidas a la terapia COVID-19 y se evaluó la relación causal mediante el algoritmo de Naranjo. Se realizó un análisis estadístico descriptivo. Resultados: Identificamos 141 RAM en 110 casos de hospitalización por COVID-19 lo que supone una incidencia del 9,66% (141/1459), IC95% 8,25-11,29. De las RAM analizadas el 60,3% (85/141) se atribuyeron a la terapia COVID. Lopinavir/ritonavir representó el 38,8% (33/85) de las RAM, los glucocorticoides el 23,5% (20/85) y la hidroxicloroquina el 9,4% (8/85). De todas las RAM, el 31,8% (27/85) fueron trastornos gastrointestinales (probable lopinavir /ritonavir), el 27,0% (23/85) trastornos de la glucemia (probable glucocorticoide) y el 17,6% (15/85) hipertransaminasemia (probable azitromicina, posible lopinavir /ritonavir, posible hidroxicloroquina, posible interferón). En cuanto a la intensidad, el 64,7% (55/85) de las RAM fueron casos leves, el 29,4% (25/85) moderados y el 5,9% (5/85) graves. El porcentaje de RAM que no requirió intervención fue 24,7% (21/85), 32,9% (28/85) requirió tratamiento farmacológico, 40,0% (34/85) suspensión del fármaco, 1,2% (1/85) seguimiento estrecho y 1,2% (1/85) reducción de dosis... (AU)
