Detection of Drug-Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining.

BACKGROUND AND OBJECTIVE: While risk of acute kidney injury (AKI) is a well documented adverse effect of some drugs, few studies have assessed the relationship between drug-drug interactions (DDIs) and AKI. Our objective was to develop an algorithm capable of detecting potential signals on this relationship by retrospectively mining data from electronic health records. MATERIAL AND METHODS: Data were extracted from the clinical data warehouse (CDW) of the Hôpital Européen Georges Pompidou (HEGP). AKI was defined as the first level of the RIFLE criteria, that is, an increase ≥50 % of creatinine basis. Algorithm accuracy was tested on 20 single drugs, 10 nephrotoxic and 10 non-nephrotoxic. We then tested 45 pairs of non-nephrotoxic drugs, among the most prescribed at our hospital and representing distinct pharmacological classes for DDIs. RESULTS: Sensitivity and specificity were 50 % [95 % confidence interval (CI) 23.66-76.34] and 90 % (95 % CI 59.58-98.21), respectively, for single drugs. Our algorithm confirmed a previously identified signal concerning clarithromycin and calcium-channel blockers (unadjusted odds ratio (ORu) 2.92; 95 % CI 1.11-7.69, p = 0.04). Among the 45 drug pairs investigated, we identified a signal concerning 55 patients in association with bromazepam and hydroxyzine (ORu 1.66; 95 % CI 1.23-2.23). This signal was not confirmed after a chart review. Even so, AKI and co-prescription were confirmed for 96 % (95 % CI 88-99) and 88 % (95 % CI 76-94) of these patients, respectively. CONCLUSION: Data mining techniques on CDW can foster the detection of adverse drug reactions when drugs are used alone or in combination.

Pharmacological considerations for azole antifungal drug management in cystic fibrosis lung transplant patients.

This paper aims to present our experience in the pharmacological approach of the management of azole antifungal drugs in cystic fibrosis lung transplant patients. Cystic fibrosis (CF) lung transplantation is associated with multi-factorial care management, because of immunosuppressive requirements, risk of infections, frequency of gastro-oesophageal reflux disease, hepatic alterations and CF pharmacokinetics (PK) specificities that result in important PK variability. CF is associated with frequent colonization of the airways by filamentous fungi, especially by Aspergillus species. Today the antifungal therapeutic arsenal offers several possibilities for long-term oral therapy including azole drugs (itraconazole, voriconazole and posaconazole). Therefore, nephrotoxic amphotericin B should be avoided. The liver is important in the pharmacological profile of azole drugs, due to metabolic elimination, hepatotoxicity and PK drug-drug interaction (DDI) involving CYP3A4 metabolic inhibition. Targets for such DDI are numerous, but immunosuppressive drugs are of major concern, justifying combined therapeutic drug monitoring (TDM) of both azoles (inhibitors) and immunosuppressants (targets) on an individualized patient basis to adjust the coprescription quantitatively. The risk of long under-dosed periods, frequently addressed in this population, could justify, on a PK basis, the need for combination with an exclusive parenteral antifungal while waiting for azole relevant drug level. High PK variability, the risk of low exposure, therapeutic issues and DDI management in this complex underlying disease justify close monitoring with systematic combined TDM of azole and immunosuppressants, in case of coprescription.

Implementation of automated signal generation in pharmacovigilance using a knowledge-based approach.

Automated signal generation is a growing field in pharmacovigilance that relies on data mining of huge spontaneous reporting systems for detecting unknown adverse drug reactions (ADR). Previous implementations of quantitative techniques did not take into account issues related to the medical dictionary for regulatory activities (MedDRA) terminology used for coding ADRs. MedDRA is a first generation terminology lacking formal definitions; grouping of similar medical conditions is not accurate due to taxonomic limitations. Our objective was to build a data-mining tool that improves signal detection algorithms by performing terminological reasoning on MedDRA codes described with the DAML+OIL description logic. We propose the PharmaMiner tool that implements quantitative techniques based on underlying statistical and bayesian models. It is a JAVA application displaying results in tabular format and performing terminological reasoning with the Racer inference engine. The mean frequency of drug-adverse effect associations in the French database was 2.66. Subsumption reasoning based on MedDRA taxonomical hierarchy produced a mean number of occurrence of 2.92 versus 3.63 (p < 0.001) obtained with a combined technique using subsumption and approximate matching reasoning based on the ontological structure. Semantic integration of terminological systems with data mining methods is a promising technique for improving machine learning in medical databases.