A data-driven pipeline to extract potential adverse drug reactions through prescription, procedures and medical diagnoses analysis: application to a cohort study of 2,010 patients taking hydroxychloroquine with an 11-year follow-up

Abstract

Highlights • The challenge of drug-safety signal detection methods is to handle four types of difficulties: ○ The data source, the study of long-term adverse drug reactions or effects not suspected by healthcare professionals, requires the use of a real-life data source. ○ The consideration of a broad spectrum of potential adverse drug reactions (ADRs), and not only candidate ADRs. ○ The temporal impact (meaning that safety depends on the dose, date and duration of treatment). ○ The difference between true ADRs and disease natural course. • We aimed to create a data-driven pipeline strategy, without any assumption of any ADRs, which take into account the complex temporality of real-life data to provide the safety profile of a given drug. • Our pipeline used three sources of real-life data to establish a safety profile of a given drug: drug prescriptions, procedures and medical diagnoses. • We successfully applied our data-driven pipeline strategy to hydroxychloroquine (HCQ). Our pipeline enabled us to find diagnoses, drugs and interventions related to HCQ and which could reflect an ADR due to HCQ or the disease itself. • This data-driven pipeline strategy may be of interest to other experts involved in the pharmacovigilance discipline.

Keywords

• Hydroxychloroquine
• Adverse drug reactions
• Medico-administrative databases
• Massive data