Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records

Liu, Mei; Hinz, Eugenia R. McPeek; Matheny, Michael E.; Denny, Joshua C.; Schildcrout, Jonathan S.; Miller, Randolph A.; Xu, Hua

doi:10.1136/amiajnl-2012-001119

Cited by 69 publications

(50 citation statements)

References 43 publications

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“…15 The method of creation and validation of AHRQ-CCS was previously published. [19][20][21][22][23] AHRQ-CCS disease categories have been widely used in numerous publications to identify comorbidities and outcomes, [24][25][26][27][28][29] predict mortality, 30,31 and estimate utilization and costs. [32][33][34][35] AHRQ-CCS disease categories are recognized by the Department of Defense Military Health System and are incorporated into Tricare data as an industry standard.…”

Section: Discussionmentioning

confidence: 99%

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

et al. 2015

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BACKGROUND: Statin use is associated with increased incidence of diabetes and possibly with increased body weight and reduced exercise capacity. Data on the longterm effects of these associations in healthy adults, however, are very limited. In addition, the relationship between these effects and diabetic complications has not been adequately studied. OBJECTIVE: To examine the association between statin use and new-onset diabetes, diabetic complications, and overweight/obesity in a cohort of healthy adults. RESEARCH DESIGN: This was a retrospective cohort study. PARTICIPANTS: Subjects were Tricare beneficiaries who were evaluated between October 1, 2003 and March 1, 2012. Patients were divided into statin users and nonusers. INTERVENTION: We excluded patients who, at baseline, had a preexisting disease indicative of cardiovascular diseases, any positive element of the Charlson comorbidity index (including diabetes mellitus), or life-limiting chronic diseases. Using 42 baseline characteristics, we generated a propensity score to match statin users and nonusers. MAIN MEASURES: Outcomes assessed included newonset diabetes, diabetic complications, and overweight/ obesity. KEY RESULTS: A total of 25,970 patients (3982 statin users and 21,988 nonusers) were identified as healthy adults at baseline. Of these, 3351 statins users and 3351 nonusers were propensity score-matched. Statin users had higher odds of new-onset diabetes (odds ratio [OR] 1.87; 95 % confidence interval [95 % CI] 1.67-2.01), diabetes with complications (OR 2.50; 95 % CI 1.88-3.32), and overweight/obesity (OR 1.14; 95 % CI 1.04-1.25). Secondary and sensitivity analyses demonstrated similar findings. CONCLUSIONS: Diabetes, diabetic complications, and overweight/obesity were more commonly diagnosed among statin-users than similar nonusers in a healthy cohort of adults. This study demonstrates that short-term clinical trials might not fully describe the risk/benefit of long-term statin use for primary prevention.

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Section: Discussionmentioning

confidence: 99%

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

et al. 2015

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“…Safety signals are considered significant when the values of RORs and the lower limits of the corresponding 95 % CIs are ≥1 [14,23,24]. The detection of possible DDIs is based on the concept that a suspected ADR is more often reported on the combination of two drugs compared with the situation where either of the two drugs has been used in absence of the other [17].…”

Section: Discussionmentioning

confidence: 99%

Detecting drug–herbal interaction using a spontaneous reporting system database: an example with benzylpenicillin and qingkailing injection

Deng

Yue

et al. 2015

Eur J Clin Pharmacol

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Section: Introductionmentioning

confidence: 99%

“…Traditionally, spontaneous reporting systems (SRSs) have been the predominant data source for the detection of signals of adverse reactions . This system, usually maintained by a government agency, receives suspected adverse drug reaction (ADR) reports submitted by health care professionals, pharmaceutical companies, and consumers . With the expanding use of electronic medical records (EMRs) in recent years, the pharmacovigilance community has another potentially rich source of information for drug safety surveillance .…”

Section: Introductionmentioning

confidence: 99%

“…This system, usually maintained by a government agency, receives suspected adverse drug reaction (ADR) reports submitted by health care professionals, pharmaceutical companies, and consumers . With the expanding use of electronic medical records (EMRs) in recent years, the pharmacovigilance community has another potentially rich source of information for drug safety surveillance . The prospect of scanning EMRs is attractive, as it overcomes some of the limitations inherent in the SRS: (1) reliance mainly on voluntary reporting from its contributors, and susceptibility to under‐reporting as well as over‐reporting (eg, due to media interest), (2) incomplete or missing data, hindering causality assessment, and (3) difficulty in detecting duplicate reports …”

Section: Introductionmentioning

confidence: 99%

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Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries

Tham

Ang

et al. 2017

Pharmacoepidemiology and Drug

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Purpose: The Singapore regulatory agency for health products (Health Sciences Authority), in performing active surveillance of medicines and their potential harms, is open to new methods to achieve this goal. Laboratory tests are a potential source of data for this purpose. We have examined the performance of the Comparison on Extreme Laboratory Tests (CERT) algorithm, developed by Ajou University, Korea, as a potential tool for adverse drug reaction detection based on the electronic medical records of the Singapore health care system. Methods:We implemented the original CERT algorithm, comparing extreme laboratory results pre-and post-drug exposure, and 5 variations thereof using 4.5 years of National University Hospital (NUH) electronic medical record data (31 869 588 laboratory tests, 6 699 591 drug dispensings from 272 328 hospitalizations). We investigated 6 drugs from the original CERT paper and an additional 47 drugs. We benchmarked results against a reference standard that we created from UpToDate 2015.Results: The original CERT algorithm applied to all 53 drugs and 44 laboratory abnormalities yielded a positive predictive value (PPV) and sensitivity of 50.3% and 54.1%, respectively. By raising the minimum number of cases for each drug-laboratory abnormality pair from 2 to 400, the PPV and sensitivity increased to 53.9% and 67.2%, respectively. This post hoc variation, named CERT400, performed particularly well for drug-induced hepatic and renal toxicities. Discussion:We have demonstrated that the CERT algorithm can be applied across national boundaries. One modification (CERT400) was able to identify adverse drug reaction signals from laboratory data with reasonable PPV and sensitivity, which indicates potential utility as a supple- reporting from its contributors, and susceptibility to under-reporting as well as over-reporting (eg, due to media interest), (2) incomplete or missing data, hindering causality assessment, and (3) difficulty in detecting duplicate reports. | Selection of drugs for evaluationAmong the 10 drugs analysed in the original CERT paper, one drug (ketorolac) was not used at NUH, while 3 oncologic drugs (etoposide, fluorouracil, and methotrexate) were incompletely captured because oncologic drugs are mainly ordered and recorded in another database.To have a direct head-to-head comparison of algorithm performance from the EMRs of two different health care institutions, we first analysed only 6 drugs described in the original CERT publication (round 1, Table 1).In round 2, we investigated an additional 47 drugs (Table 2).Factors considered in drug selection were drug usage volume and the likelihood of the drug being started during hospitalisation. Drugs with high usage were prioritised to provide sufficient number of cases for analysis. We also included negative controls (chlorpheniramine, metronidazole, and risedronic acid) with no ADRs detectable by abnormal laboratory test results in the reference standard. KEY POINTS• The Comparison on Extreme Laboratory Tests (CERT) algor...

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Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records

Abstract: Results suggest that EMR-derived laboratory measurements and medication orders can help to validate previously reported ADRs, and detect new ADRs.

Cited by 69 publications

References 43 publications

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

Detecting drug–herbal interaction using a spontaneous reporting system database: an example with benzylpenicillin and qingkailing injection

Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries

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