Using Electronic Health Care Records for Drug Safety Signal Detection A Comparative Evaluation of Statistical Methods

作者：Schuemie Martijn J^*; Coloma Preciosa M; Straatman Huub; Herings Ron M C; Trifiro Gianluca; Matthews Justin Neil; Prieto Merino David; Molokhia Mariam; Pedersen Lars; Gini Rosa; Innocenti Francesco; Mazzaglia Giampiero; Picelli Gino; Scotti Lorenza; van der Lei Johan; Sturkenboom Miriam C J M

来源：Medical Care, 2012, 50(10): 890-897.

DOI：10.1097/MLR.0b013e31825f63bf

• drug safety
• active surveillance
• electronic health record
• method evaluation

Background: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). %26lt;br%26gt;Objectives: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. %26lt;br%26gt;Research Design: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method %26quot;longitudinal evaluation of observational profiles of adverse events related to drugs%26quot; (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method. %26lt;br%26gt;Measures: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. %26lt;br%26gt;Results: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. %26lt;br%26gt;Conclusions: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.

• 出版日期2012-10