RESUMEN
BACKGROUND: Preventive strategies to reduce clinically significant medication errors (MEs), such as medication review, are often limited by human resources. Identifying high-risk patients to allow for appropriate resource allocation is of the utmost importance. To this end, we developed a predictive model to identify high-risk patients and assessed its impact on clinical decision-making. METHODS: From March 1st to April 31st 2014, we conducted a prospective cohort study on adult inpatients of a 1,644-bed University Hospital Centre. After a clinical evaluation of identified MEs, we fitted and internally validated a multivariate logistic model predicting their occurrence. Through 5,000 simulated randomized controlled trials, we compared two clinical decision pathways for intervention: one supported by our model and one based on the criterion of age. RESULTS: Among 1,408 patients, 365 (25.9%) experienced at least one clinically significant ME. Eleven variables were identified using multivariable logistic regression and used to build a predictive model which demonstrated fair performance (c-statistic: 0.72). Major predictors were age and number of prescribed drugs. When compared with a decision to treat based on the criterion of age, our model enhanced the interception of potential adverse drug events by 17.5%, with a number needed to treat of 6 patients. CONCLUSION: We developed and tested a model predicting the occurrence of clinically significant MEs. Preliminary results suggest that its implementation into clinical practice could be used to focus interventions on high-risk patients. This must be confirmed on an independent set of patients and evaluated through a real clinical impact study.
Asunto(s)
Toma de Decisiones Clínicas/métodos , Pacientes Internos/estadística & datos numéricos , Errores de Medicación/prevención & control , Administración de la Seguridad/métodos , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Modelos Logísticos , Masculino , Errores de Medicación/estadística & datos numéricos , Persona de Mediana Edad , Análisis Multivariante , Estudios Prospectivos , Reproducibilidad de los Resultados , Factores de RiesgoRESUMEN
Lactic acidosis is a marker of tissue hypoperfusion and impairs oxygen delivery. High lactate levels are associated with altered systemic hemodynamics, tissue hypoperfusion, and altered cellular metabolism. Increased lactate levels have also been reported as a complication of ß-adrenergic agents administered during asthma therapy. A 49-year-old woman with a prior diagnosis of asthma presented to the emergency department in respiratory distress. She immediately received, in 2 hours, 4 bronchodilator aerosols (ipratropium bromide 0.5 mg/2 mL and terbutaline 5 mg/2 mL) and methylprednisolone intravenous (120 mg). After these 4 aerosols, she was still dyspneic. First, arterial blood gases (pH 7.38; PCO2, 3.92 kPa; HCO3, 19.2 mmol/L) and arterial lactate (lactate, 7.96 mmol/L) were performed with a second series of 4 aerosols. Second, arterial blood gases (pH 7.29; PCO2, 4.01 kPa; HCO3, 15.4 mmol/L) and arterial lactate (lactate, 10.47 mmol/L) were performed at the end of the second series of aerosols. There was no hypoxemia, no inadequate cardiac output state, no anemia, no sepsis, and no use of biguanides. Previous studies have suggested that administration of ß agonists can lead to lactic acidemia in the absence of hypoxia or shock, but it is the highest level of lactate that we found in the literature. In sepsis and shock, lactic acidosis is used as a marker of disease severity. In this case, it is not necessarily the sign of an immediate gravity.