Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System

Suchard, Marc A.; Zorych, Ivan; Simpson, Shawn E.; Schuemie, Martijn J.; Ryan, Patrick B.; Madigan, David

doi:10.1007/s40264-013-0100-4

Cited by 32 publications

(31 citation statements)

References 24 publications

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“…The Sentinel group successfully identified intussusception after rotavirus vaccination in infants and risk of coeliac disease in patients on long‐term therapy with olmesartan from algorithms applied to their extensive databases. The Observational Medical Outcomes Partnership investigated methods that relied primarily on diagnosis codes in administrative databases . Our dataset had limited structured diagnostic coding, which made it challenging for us to explore algorithms that rely on codes such as ICD‐9.…”

Section: Discussionmentioning

confidence: 99%

“…Other groups have been investigating a variety of data mining methodologies to query health records for identification of ADRs based primarily on clinical features. [11][12][13][14][15][16] One notable effort is the Sen- 11,20,21 Our dataset had limited structured diagnostic coding, which made it challenging for us to explore algorithms that rely on codes such as ICD-9.…”

Section: Negative Controlsmentioning

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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Section: Discussionmentioning

confidence: 99%

Section: Negative Controlsmentioning

confidence: 99%

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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“…A SCCS analysis was performed for four different ADEs in the CCAE administrative claims database. A detailed description of the analysis has been published in the study by Ryan et al 20 , 37 , 38 . Two sets of drug candidates for each ADE, using as thresholds P < 0.05 and P < 0.0005, were evaluated using similarity‐based modeling as described below.…”

Section: Methodsmentioning

confidence: 99%

Similarity‐Based Modeling Applied to Signal Detection in Pharmacovigilance

Vilar

Ryan

Madigan

et al. 2014

CPT Pharmacom & Syst Pharma

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One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

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“…Cases may be identified in the main database, especially if these cases are rare, or from exhaustive field data (liver transplantations), and controls taken from the more manageable 1/97th sample, EGB, for case‐control studies or for case‐population approaches . Ongoing studies use the OMOP datasets to validate these approaches …”

Section: Some Examples Of Studiesmentioning

confidence: 99%

“…27,28 Ongoing studies use the OMOP datasets to validate these approaches. [29][30][31][32][33] These models could be used for automated signal detection in predefined "critical events. "…”

Section: Drug-related Risk Identification and Alert Generationmentioning

confidence: 99%

The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology

Bezin

Duong

Lassalle

et al. 2017

Pharmacoepidemiology and Drug

426

369

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The French health care system is based on universal coverage by one of several health care insurance plans. The SNIIRAM database merges anonymous information of reimbursed claims from all these plans, linked to the national hospital-discharge summaries database system (PMSI) and the national death registry. It now covers 98.8% of the French population, over 66 million persons, from birth (or immigration) to death (or emigration), making it possibly the world's largest continuous homogeneous claims database. The database includes demographic data; health care encounters such as physician or paramedical visits, medicines, medical devices, and lab tests (without results); chronic medical conditions (ICD10 codes); hospitalisations with ICD10 codes for primary, linked and associated diagnoses, date and duration, procedures, diagnostic-related groups, and cost coding; date but currently not cause of death. The power of the database is correlatively great, and its representativeness is near perfect, since it essentially includes the whole country's population. The main difficulty in using the database, beyond its sheer size and complexity, is the administrative process necessary to access it. Recent legislative advances are making this easier. EGB (Echantillon Généraliste de Bénéficiaires) is the 1/97th random permanent representative sample of SNIIRAM, with planned 20-year longitudinal data (10 years at this time). Access time is 1 to 3 months, but its power is less (780 000 subjects). This is enough to study common issues with older drugs but may be limited for new products or rare events.

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Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System

Cited by 32 publications

References 24 publications

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

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

Similarity‐Based Modeling Applied to Signal Detection in Pharmacovigilance

The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology

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