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A prediction model‐based algorithm for computer‐assisted database screening of adverse drug reactions in the Netherlands
Author(s) -
Scholl Joep H.G.,
Hunsel Florence P.A.M.,
Hak Eelko,
Puijenbroek Eugène P.
Publication year - 2018
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4364
Subject(s) - pharmacovigilance , medicine , bootstrapping (finance) , database , receiver operating characteristic , pharmacoepidemiology , logistic regression , drug reaction , area under the curve , data mining , machine learning , statistics , artificial intelligence , algorithm , drug , adverse effect , computer science , pharmacology , econometrics , mathematics , medical prescription , economics
Purpose The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model‐based approach. Methods A logistic regression‐based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug‐ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. Results A total of 25 026 unique drug‐ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734–0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). Conclusions A prediction model‐based approach can be a useful tool to create priority‐based listings for signal detection in databases consisting of spontaneous ADRs.