Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

Acute kidney injury from non-steroidal anti-inflammatory drugs: a novel pharmacovigilance strategy

A 2019/2020 Summer Studentship research project

Kidney injury from anti-inflammatory medicines is a common problem. This study uses large data sets to look for this side effect in a new way that is much more efficient than manual searches. Using this alongside existing methods of studying side effects, helps us to understand how often, when, and where kidney damage occurs. We can then target prevention strategies and reduce patient harm.

Student: Hayley Nehoff
Supervisors: Dr Richard McNeil, Dr Paul Chin (UOC), Dr Matt Doogue (CDHB)
Sponsor: Canterbury Medical Research Foundation

Introduction

Adverse drug effects (ADEs) remain a common and preventable cause of patient harm. Pharmacovigilance is critical to monitoring the safe “real world” use of medicines. Historically, pharmacovigilance has relied on spontaneous case reports and labour-intensive data collection. Electronic prescribing data, combined with various other outcome data, provide a new opportunity for rapid, large scale pharmacovigilance of specific ADEs.

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly associated with acute kidney injury (AKI). We have access to prescribing data and laboratory data in Christchurch Hospital, and propose using NSAID-induced AKI as a proof of concept for using big data to inform pharmacovigilance.

Aims

  1. To characterize the use of NSAIDs in Christchurch hospital
  2. To quantify the rate of AKI following NSAID administration
  3. To compare the rate of AKI following NSAIDs administration to a control population

Methods

NSAIDs will be defined using the NZF classification. Patients will be all inpatients in Christchurch Hospital in a six month period who have received a NSAID. The control group will be inpatients receiving codeine and/or tramadol and not NSAIDs, as this reflects a commonly used alternative not associated with AKI. The most recent creatinine and eGFR from the 6 week period prior to NSAID prescribing will be collected, and all creatinine and eGFR results for 7 days after administration of the first dose. AKI is defined as a 50% increase from baseline in any measured creatinine following administration. Data will be provided by CDHB Decision Support. Data analysis will be using Microsoft Excel and GraphPad Prism 8.

Student researcher’s component of the study

The data and ethics approval will be obtained in advance. The student will lead cleaning and analysing the data provided, and perform the statistical analysis. The student will also undertake a detailed review of a small selection of randomly selected cases to better describe the studied population. This will include considering causality of the AKI, susceptibility to AKI considering drug, disease and patient factors, and preventability. Supervisors will heavily support this interpretation. The student will be supported to publish the results.

Students will gain skills in big data analytics and clinical evaluation of ADRs, gain exposure to hospital pharmacovigilance programs, and have an opportunity to publish research.

Clinical significance

Kidney injury from anti-inflammatory medicines is a common problem. This study uses large data sets to look for this side effect in a new way that is much more efficient than manual searches. Using this alongside existing methods of studying side effects, helps us to understand how often, when, and where kidney damage occurs. We can then target prevention strategies and reduce patient harm.

This exciting new method can be applied to many other side effects from anti-inflammatories and other medicines, to improve interventions for other important causes of preventable harm to patients.

Student Prerequisites

Medical or pharmacy students with an interest in data analysis and medication safety.

How to apply

Email richard.mcneill@cdhb.health.nz