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KMID : 0603720110170010058
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Journal of Korean Society of Medical Informatics
2011 Volume.17 No. 1 p.58 ~ p.66
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Analysis of Relationship between Levofloxacin and Corrected QT Prolongation Using a Clinical Data Warehouse
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Park Man-Young
Kim Eun-Yeob
Lee Young-Ho
Kim Woo-Jae
Kim Ku-Sang
Sheen Seung-Soo
Lim Hong-Seok
Park Rae-Woong
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Abstract
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Objectives: The aim of this study was to examine whether or not levofloxacin has any relationship with QT pro-longation in a real clinical setting by analyzing a clinical data warehouse of data collected from different hospital informa-tion systems.
Methods: Electronic prescription data and medical charts from 3 different hospitals spanning the past 9 years were reviewed, and a clinical data warehouse was constructed. Patients who were both administrated levofloxacin and given electrocardiograms (ECG) were selected. The correlations between various patient characteristics, concomitant drugs, cor-rected QT (QTc) prolongation, and the interval difference in QTc before and after levofloxacin administration were analyzed.
Results: A total of 2,176 patients from 3 different hospitals were included in the study. QTc prolongation was found in 364 patients (16.7%). The study revealed that age (OR 1.026, p < 0.001), gender (OR 0.676, p = 0.007), body temperature (OR 1.267, p = 0.024), and cigarette smoking (OR 1.641, p = 0.022) were related with QTc prolongation. After adjusting for related factors, 12 drugs concomitant with levofloxacin were associated with QTc prolongation. For patients who took ECGs before and after administration of levofloxacin during their hospitalization (n = 112), there was no significant difference in QTc prolongation.
Conslusions: The age, gender, body temperature, cigarette smoking and various concomitant drugs might be related with QTc prolongation. However, there was no definite causal relationship or interaction between levofloxacin and QTc prolongation. Alternative surveillance methods utilizing the massive accumulation of electronic medical data seem to be essential to adverse drug reaction surveillance in future.
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KEYWORD
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Long QT Syndrome, Ofloxacin, Data Mining, Product Surveillance, Post-marketing, Hospital Information Systems
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