BMC Med Inform Decis Mak - Adverse drug events with hyperkalaemia during inpatient stays: evaluation of an automated method for retrospective detection in hospital databases.

Tópicos

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Resumo

CKGROUND: Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays.METHODS: We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs.RESULTS: In terms of recall, 89.5% of ADEs with hyperkalaemia "with or without an abnormal symptom" were automatically identified (including all three serious ADEs). In terms of precision, 63.7% of the automatically identified ADEs with hyperkalaemia were true ADEs.CONCLUSIONS: The use of context-sensitive rules appears to improve the automated detection of ADEs with hyperkalaemia. This type of tool may have an important role in pharmacoepidemiology via the routine analysis of large inter-hospital databases.

Resumo Limpo

ckground advers drug reaction advers drug event ade major public health issu mani differ prospect tool autom detect ade hospit databas develop evalu object present studi evalu autom method retrospect detect ade hyperkalaemia inpati staysmethod use set complex detect rule take account patient clinic biolog context chronolog relationship caus expect outcom dataset consist inpati stay french general hospit autom review perform data result compar expert chart review complex detect rule analyt qualiti evalu adesresult term recal ade hyperkalaemia without abnorm symptom automat identifi includ three serious ade term precis automat identifi ade hyperkalaemia true adesconclus use contextsensit rule appear improv autom detect ade hyperkalaemia type tool may import role pharmacoepidemiolog via routin analysi larg interhospit databas

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