Yu-Kyong Choi

Publications (3)7.53 Total impact

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  Available from: Nam-Kyong Choi

  Article: Comparison and validation of data-mining indices for signal detection: using the Korean national health insurance claims database.

  Nam-Kyong Choi, Yoosoo Chang, Ju-Young Kim, Yu-Kyong Choi, Byung-Joo Park
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  ABSTRACT: To detect the signals of celecoxib compared with other analgesics and anti-inflammatory drugs (AAIDs) by proportional claims ratio (PCR), claims odds ratio (COR), information component (IC), and relative risk (RR) using the Korean claims database. In addition, the concordance of the identified signals by the data-mining indices (DMIs) and the validity of the DMIs were evaluated. The Korean Health Insurance Review and Assessment Service claims database was used. The study population consisted of elderly ambulatory care patients with osteoarthritis who were prescribed AAIDs in Seoul from 1 January 2005 to 30 September 2005. A short-term serious adverse event (SAE) was defined as a hospital admission within 12 weeks from each AAID prescription. Among the screened SAEs, signals were identified by the DMIs. The sensitivity, specificity, and predictability were estimated with reference to known adverse events associated with celecoxib. A total of 135,232 elderly patients with osteoarthritis were prescribed AAIDs. There were 309,717 drug-SAE pairs and 481 different SAEs. The PCR, COR, IC, and RR detected were as follows: 56 (11.6%), 57 (11.9%), 129 (26.8%), and 123 (25.6%) signals for celecoxib, respectively. The RR detected signals had a relatively high sensitivity (23.4%) compared with the other indices (PCR 9.9%, COR 10.8%, and IC 18.9%). The specificity of RR (73.8%) was higher than that of IC (70.8%). The positive and negative predictive values of the RR were 21.1% and 76.3%, respectively. This study suggested that the RR was the most accurate of the DMIs for detecting signals in the claims database.
  Pharmacoepidemiology and Drug Safety 12/2011; 20(12):1278-86. · 2.53 Impact Factor
• Article: A population-based case-crossover study of polyethylene glycol use and acute renal failure risk in the elderly.

  Nam-Kyong Choi, Yoosoo Chang, Sun-Young Jung, Yu-Kyong Choi, Joongyub Lee, Jin-Ho Lee, Ju-Young Kim, Byung-Joo Park
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  ABSTRACT: To evaluate the possibility of an association between polyethylene glycol (PEG) and acute renal failure (ARF) in elderly patients using a health insurance claims database. We conducted a population-based case-crossover study using information obtained from Korean Health Insurance Review and Assessment Service (HIRA) claims from January 1, 2005 to December 31, 2005 (Seoul, Korea). The study population consisted of elderly patients who received PEG prior to experiencing their first ARF-related hospitalization from April 1, 2005 to December 31, 2005. For each patient, one case and two control periods were matched. PEG use in a 2- or 4-wk window period prior to hospitalization for ARF was compared with PEG use in two earlier 2- or 4-wk control window periods. Conditional logistic regression analysis was used to estimate odds ratios (ORs) and 95% CI, adjusting for concomitant uses of diuretics, angiotensin converting enzyme inhibitors, non-steroidal anti-inflammatory drugs, antibiotics, anti-cancer drugs, and contrast media. Within the HIRA database which contained 1,093,262 elderly patients, 1156 hospitalized ARF cases were identified. Among these cases, PEG was prescribed to 17 (1.5%) patients before hospitalization. The adjusted ORs when applying the 2- and 4-wk window periods were 0.4 (95% CI: 0.03-5.24) and 2.1 (95% CI: 0.16-27.78), respectively. No increased risk of ARF was found in elderly PEG users. However, based on the limited number of study subjects, further analysis should be performed to confirm these results.
  World Journal of Gastroenterology 02/2011; 17(5):651-6. · 2.47 Impact Factor
• Article: Signal detection of rosuvastatin compared to other statins: data-mining study using national health insurance claims database.

  Nam-Kyong Choi, Yoosoo Chang, Yu Kyong Choi, Seokyung Hahn, Byung-Joo Park
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  ABSTRACT: To detect adverse drug reaction (ADR) signals of rosuvastatin compared to other statins with a novel data-mining approach based on relative risk (RR) using the national health insurance claims database, and to evaluate the usefulness of this method as a tool for signal detection. We used the Health Insurance Review & Assessment Service (HIRA) claims database (Seoul, Korea). Serious adverse events (SAE) were defined as any diagnostic code at the time of hospitalization within 12 weeks from a statin prescription date, regardless of causality. Among statin users, RRs were calculated to compare the proportion of rosuvastatin-specific SAE pairs for rosuvastatin users with the corresponding proportion of drug-SAE pairs for users of other statins. Any SAE for which the lower limit of the RR's 95% confidence interval was greater than 1 was defined as a signal. All detected signals were reviewed to determine whether the signals corresponded with published adverse events (AEs) exclusive to rosuvastatin. Among 96 236 elderly outpatients who received rosuvastatin, or other statins, from January 2005 to September 2005, 40 304 drug-SAE pairs and 376 SAEs were observed. Twenty-five (6.6%) SAEs were detected as signals by the RR-based data-mining approach. Among the 13 references AEs published to be exclusive to rosuvastatin, 8 (61.5%) were found to correspond with the detected signals with a positive predictive value (PPV) of 32%. The RR-based data-mining approach successfully detected signals for rosuvastatin using a national health insurance claims database. This approach could be useful for safety surveillance of marketed products.
  Pharmacoepidemiology and Drug Safety 03/2010; 19(3):238-46. · 2.53 Impact Factor