A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database

Man Young Park, Dukyong Yoon, Kiyoung Lee, Seok Yun Kang, Inwhee Park, Suk Hyang Lee, Woojae Kim, Hye Jin Kam, Young Ho Lee, Ju Han Kim, Rae Woong Park

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Purpose: Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool. Methods: We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10years' EMR data from a tertiary teaching hospital, containing 32033710 prescriptions and 115241147 laboratory tests for 530829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated. Results: The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64-100%, 22-76%, 22-75%, and 54-100%, respectively. Conclusion: The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.

Original languageEnglish
Pages (from-to)598-607
Number of pages10
JournalPharmacoepidemiology and Drug Safety
Volume20
Issue number6
DOIs
StatePublished - 1 Jun 2011

Fingerprint

Electronic Health Records
Drug-Related Side Effects and Adverse Reactions
Databases
Sensitivity and Specificity
Tertiary Care Centers
Teaching Hospitals
Pharmaceutical Preparations
Prescriptions
Research Design

Keywords

  • Adverse drug event
  • Electronic medical record
  • Pharmacovigilance
  • Postmarketing drug surveillance

Cite this

Park, Man Young ; Yoon, Dukyong ; Lee, Kiyoung ; Kang, Seok Yun ; Park, Inwhee ; Lee, Suk Hyang ; Kim, Woojae ; Kam, Hye Jin ; Lee, Young Ho ; Kim, Ju Han ; Park, Rae Woong. / A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database. In: Pharmacoepidemiology and Drug Safety. 2011 ; Vol. 20, No. 6. pp. 598-607.
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abstract = "Purpose: Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool. Methods: We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10years' EMR data from a tertiary teaching hospital, containing 32033710 prescriptions and 115241147 laboratory tests for 530829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated. Results: The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64-100{\%}, 22-76{\%}, 22-75{\%}, and 54-100{\%}, respectively. Conclusion: The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.",
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Park, MY, Yoon, D, Lee, K, Kang, SY, Park, I, Lee, SH, Kim, W, Kam, HJ, Lee, YH, Kim, JH & Park, RW 2011, 'A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database', Pharmacoepidemiology and Drug Safety, vol. 20, no. 6, pp. 598-607. https://doi.org/10.1002/pds.2139

A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database. / Park, Man Young; Yoon, Dukyong; Lee, Kiyoung; Kang, Seok Yun; Park, Inwhee; Lee, Suk Hyang; Kim, Woojae; Kam, Hye Jin; Lee, Young Ho; Kim, Ju Han; Park, Rae Woong.

In: Pharmacoepidemiology and Drug Safety, Vol. 20, No. 6, 01.06.2011, p. 598-607.

Research output: Contribution to journalArticle

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AU - Park, Inwhee

AU - Lee, Suk Hyang

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AU - Lee, Young Ho

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