Analysis of Relationship between Levofloxacin and Corrected QT Prolongation Using a Clinical Data Warehouse / 대한의료정보학회지

OBJECTIVE: The aim of this study was to examine whether or not levofloxacin has any relationship with QT prolongation in a real clinical setting by analyzing a clinical data warehouse of data collected from different hospital information 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, corrected 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. CONCLUSIONS: 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.

A Data Warehouse Based Retrospective Post-marketing Surveillance Method: A Feasibility Test with Fluoxetine / 대한의료정보학회지

OBJECTIVE: Post-marketing surveillance (PMS) is an adverse events monitoring practice of pharmaceutical drugs on the market. Traditional PMS methods are labor intensive and expensive to perform, because they are largely based on manual work including phone-calling, mailing, or direct visits to relevant subjects. The objective of this study was to develop and validate a PMS methodology based on the clinical data warehouse (CDW). METHODS: We constructed a archival DB using a hospital information system and a refined CDW from three different hospitals. Fluoxetine hydrochloride, an antidepressant, was selected as the target monitoring drug. Corrected QT prolongation on ECG was selected as the target adverse outcome. The Wilcoxon signed rank test was performed to analyze the difference in the corrected QT interval before and after the target drug administration. RESULTS: A refined CDW was successfully constructed from three different hospitals. Table specifications and an entity-relation diagram were developed and are presented. A total of 13 subjects were selected for monitoring. There was no statistically significant difference in the QT interval before and after target drug administration (p=0.727). CONCLUSION: The PMS method based on CDW was successfully performed on the target drug. This IT-based alternative surveillance method might be beneficial in the PMS environment of the future.

What kind of medical equations and decision trees do physicians want in their daily activities? : Analysis of one-year MedCalc 3000(R) log data / 대한의료정보학회지

OBJECTIVE: Medical personnel require many evidence-based medical equations and decision trees for their daily medical practice, medical education and research. Among the hundreds of medical equations, the essential or frequently used equations are not revealed yet. We tried to reveal the most frequently used medical equations and decision trees. METHODS: A Korean version of medical equation tool was implemented on the Intranet, which provides 288 medical equations and decision trees. One year after implementation of the system, the log file was analyzed for the use status. RESULTS: Of the 288 equations and decision trees, 170 items were visited more than once. The creatinine estimation equation was most frequently used (545 times, 18.7%). Body mass index, Apache II score, diabetes screening decision tree, and unit conversion were followed. CONCLUSIONS: We found the frequently used medical equations and decision trees in practice by analyzing the server log file. The list would be a reference for incorporation of selected equations into a computerized prescriber-order entry system.