Detection of adverse drug reactions: evaluation of an automatic data processing applied in oncology performed in the French Diagnosis Related Groups database

Abstract

The aim of this study was to assess an automated detection method of serious adverse reactions induced by oral targeted therapy (OTT) in patients with cancer, performed in the French Diagnosis Related Groups (DRG) database. Patients with cancer of the Poitiers hospital who started an OTT between 2014 and 2015 were included. This study focused on adverse drug reaction which required inpatient hospitalization (ADR_h ). All diagnoses coded in the DRG database for hospital stays that occurred within 3 months after OTT initiation were collected (potential ADR_h ). Filters (exclusion criteria) were automatically applied on potential ADR_h to exclude diagnoses that were not adverse drug reactions (false positives). A pharmacovigilance review was carried out to identify ADR_h in the medical records (reported ADR_h ). The sensitivity and specificity of the detection method were estimated for each filter combinations by comparison between potential and reported ADR_h . This study included 129 patients. The medical records review led to identify 19 ADR_h (all coded in the DRG database) in 14 patients. To maintain a 100% sensitivity of the method detection, the best specificity obtained was 58.3% (95% IC: [55.2-61.4]).The use of restrictive filters ('drug' in the diagnostic label, specific diagnosis code for adverse cancer drug reaction) resulted in a 97.8% specificity (95% IC: [96.6-98.5]) with a 38.2% sensitivity (95% IC: [23.9-55.0]). Our method has detected the third of ADR_h with an excellent specificity. Complementary experimentations in pharmacovigilance centers are necessary to evaluate the interest of this tool in routine in addition to spontaneous reporting.

Keywords: Diagnosis Related Groups-based information system; adverse drug reactions; automatic data processing; molecular targeted therapy; neoplasms; pharmacovigilance.