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 ). All diagnoses coded in the DRG database for hospital stays that occurred within 3 months after OTT initiation were collected (potential ADR ). Filters (exclusion criteria) were automatically applied on potential ADR to exclude diagnoses that were not adverse drug reactions (false positives). A pharmacovigilance review was carried out to identify ADR in the medical records (reported ADR ). The sensitivity and specificity of the detection method were estimated for each filter combinations by comparison between potential and reported ADR . This study included 129 patients. The medical records review led to identify 19 ADR (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 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.

Overview of French databases available for studying anticancer drugs in real‐life setting

“…Structured data collected during MDT meetings such as demographic characteristics of patients, tumor characteristics including stage, histology, mutations, or details on the proposed treatment are thus maintained in the hospital information system. These data represent a valuable tool to exhaustively identify patients with a given cancer in a real‐life setting (including those without anticancer drug or surgery or best supportive care (BSC)), or to study treatment practices at a local or regional level [25–27].…”

MIM1 induces COLO829 melanoma cell death through mitochondrial membrane breakdown, GSH depletion, and DNA damage

“…For many years, the options for treatment of metastatic, advanced malignant melanoma were limited [2,3] and dacarbazine was the agent mainly used in systematic therapy. Currently, targeted and immune therapies are the most promising treatments [3][4][5]. However, the results of melanoma cure are still unsatisfactory and the resistance to novel therapeutic agents is mainly responsible for the poor prognosis [1,3,4].…”