ArticleDrug safety2015
Detection of Drug-Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining.
Article in Drug safety, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Age-stratified analysis of adverse event signals for clarithromycin: a disproportionality analysis using the FDA Adverse Event Reporting System.Therapeutic advances in drug safety · 2025Article
- Navigating Drug-Drug Interactions in Multimorbid Patients: Utilizing Tools, Guidelines, and Clinical Implications.Current drug safety · 2025Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Drug-drug interaction and acute kidney injury development: A correlation-based network analysis.PloS one · 2023Article
- Identifying drug-related attributes to personalise antihypertensive agents: the outcome report of patients receiving metoprolol therapy.BMC medical informatics and decision making · 2021Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- Automatic health record review to help prioritize gravely ill Social Security disability applicants.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Identifying High-Risk Medications Associated with Acute Kidney Injury in Critically Ill Patients: A Pharmacoepidemiologic Evaluation.Paediatric drugs · 2017Article
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- The Virtuous Circles of Clinical Information Systems: a Modern Utopia.Yearbook of medical informatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveWhile 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
methodsData 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.
resultsSensitivity 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.
conclusionData mining techniques on CDW can foster the detection of adverse drug reactions when drugs are used alone or in combination.
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