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Pharmacoepidemiol Drug Saf. 2020 Mar 4. doi: 10.1002/pds.4983. [Epub ahead of print]

Empirical assessment of case-based methods for drug safety alert identification in the French National Healthcare System database (SNDS): Methodology of the ALCAPONE project.

Author information

1
Bordeaux PharmacoEpi, INSERM CIC1401, Université de Bordeaux, Bordeaux, France.
2
INSERM U1219, Université de Bordeaux, Bordeaux, France.
3
Epidemiology Analytics, Janssen Research and Development, Titusville, New Jersey, USA.
4
Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA.
5
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
6
Aetion, Inc., New York, New York, USA.
7
Department of Biostatistics and Clinical Research, Rouen University Hospital, Rouen, France.
8
INSERM U1181, Paris, France.
9
Caisse Nationale de l'Assurance Maladie, Paris, France.
10
CHU de Bordeaux, Bordeaux, France.

Abstract

OBJECTIVES:

To introduce the methodology of the ALCAPONE project.

BACKGROUND:

The French National Healthcare System Database (SNDS), covering 99% of the French population, provides a potentially valuable opportunity for drug safety alert generation. ALCAPONE aimed to assess empirically in the SNDS case-based designs for alert generation related to four health outcomes of interest.

METHODS:

ALCAPONE used a reference set adapted from observational medical outcomes partnership (OMOP) and Exploring and Understanding Adverse Drug Reactions (EU-ADR) project, with four outcomes-acute liver injury (ALI), myocardial infarction (MI), acute kidney injury (AKI), and upper gastrointestinal bleeding (UGIB)-and positive and negative drug controls. ALCAPONE consisted of four main phases: (1) data preparation to fit the OMOP Common Data Model and select the drug controls; (2) detection of the selected controls via three case-based designs: case-population, case-control, and self-controlled case series, including design variants (varying risk window, adjustment strategy, etc.); (3) comparison of design variant performance (area under the ROC curve, mean square error, etc.); and (4) selection of the optimal design variants and their calibration for each outcome.

RESULTS:

Over 2009-2014, 5225 cases of ALI, 354 109 MI, 12 633 AKI, and 156 057 UGIB were identified using specific definitions. The number of detectable drugs ranged from 61 for MI to 25 for ALI. Design variants generated more than 50 000 points estimates. Results by outcome will be published in forthcoming papers.

CONCLUSIONS:

ALCAPONE has shown the interest of the empirical assessment of pharmacoepidemiological approaches for drug safety alert generation and may encourage other researchers to do the same in other databases.

KEYWORDS:

SNDS; calibration; case-population; claims database; method assessment; pharmacoepidemiology; pharmacovigilance

PMID:
32133717
DOI:
10.1002/pds.4983

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