Medicine-Induced Acute Kidney Injury Findings from Spontaneous Reporting Systems, Sequence Symmetry Analysis and a Case–Control Study with a Focus on Medicines Used in Primary Care

Spontaneous Report Studies

We searched the MEDLINE and EMBASE databases to identify all studies that had investigated all medicines associated with acute kidney injury from spontaneous adverse event reporting databases. Search terms included drug-related side effects or adverse reactions or drug-induced; acute kidney injury or acute kidney failure; adverse drug reaction reporting systems or pharmacovigilance or drug surveillance programme or spontaneous report. The full search is included in electronic supplementary Tables 1 and 2.

We included studies that had identified the range of possible medicines that were associated with acute kidney injury, but excluded studies that had examined a single medicine class, single medicine or medicine interaction.

For each result, we extracted the medicine name or class, disproportionality measure and 95% confidence interval (CI). The list of medicines associated with acute kidney injury was generated where at least one study had found a reporting odds ratio (ROR) above 2 and the CIs did not contain 1 [25].

Observational Studies

We used a self-controlled design, SSA, and a case–control design to confirm the associations identified from spontaneous reports.

Setting We used the Australian Government Department of Veterans’ Affairs administrative health claims database, which contains details of all prescription medicines, medical and allied health services, and hospitalisations provided to Department of Veterans’ Affairs clients for which the Department of Veterans’ Affairs pays a subsidy. The data cover a treatment population of approximately 250,000 clients.

The Study Period This study was conducted between 1 July 2005 and 30 June 2019.

The Primary Outcome Hospitalisations for acute kidney injury (primary diagnosis; International Classification of Diseases, Tenth Revision [ICD-10] code N17). We used the primary diagnosis code only, which indicates the cause of admission, and thus the primary outcome represents community-acquired acute kidney injury. Secondary diagnoses, which may include acute kidney injury that occurred during hospital stay, were not assessed because we did not have complete capture of medicines administered during hospital stay. The ICD-10 code N17 has been validated and shown to have high specificity but low-to-moderate sensitivity for acute kidney injury [26, 27].

The exposure Medicines identified as potentially nephrotoxic using data from the spontaneous reporting systems, i.e. medicines with an ROR above 2 and the CIs did not contain 1. Medicines used in the hospital setting only, such as parenteral antibiotics, were excluded from the analysis, as were medicines not available in Australia.

Study Design: Sequence Symmetry Analysis

The SSA is a self-controlled design that has been validated for adverse medicine event detection, showing moderate sensitivity, high specificity and robust performance [28, 29]. We examined the incident events of medicine exposure and hospitalisation for acute kidney injury, and included persons for whom both events occurred within a 12-month period.

SSA statistical methods For each medicine of interest, we calculated the sequence ratio [SR] by dividing the number of people who had acute kidney injury in the 12 months after initiation of the medicine by the number of people who had acute kidney injury in the 12 months before initiation of the medicine. To adjust for temporal changes in prescribing and hospitalisation trends over time, an adjusted SR [ASR] was calculated by dividing the crude SR by a null-effect SR [30]. The 95% CI was derived from bootstrapping with 10,000 samples of the ASR [31].

Study Design: Case–Control Study

We defined cases as persons who had their first hospitalisation for acute renal failure (ICD-10 code N17) in the study period, while controls were persons hospitalised for medical conditions other than acute renal failure in the same calendar year as the case. All cases and controls had at least one full year of claims history prior to the index admission.

Each case was matched to five controls by sex, age at date of admission (± 2 years) and year of admission (± 1 year). Cases could be controls prior to becoming a case, and controls were sampled with replacement. Medicine exposure was assessed in the year prior to the index date. We estimated medicine exposure based on the number of days supplied at the time of dispensings. The estimated days of supply was defined by the time period in which 75% of people returned for a repeat dispensing [19, 20]. For the majority of medicines in Australia, 1 month supply is provided under the national Pharmaceutical Benefits Scheme, and the time period within which 75% of people return is 35 days (the exposure period), allowing a 5-day grace period of non-adherence.

People were categorised into four groups: (1) new users were defined as people exposed at the time of admission (i.e. the supply period crossed the date of admission) and this was their first supply in that year; (2) current users were defined as persons who were on the medicine at the time of hospital admission but had previously had supplies during the year; (3) past users were defined as those who had had supplies previously in the year but did not have sufficient days supplied to cover the period, including the hospital admission; and (4) never users, i.e. persons with no dispensing of the medicine within the year prior to hospitalisation (see Fig. 1 in the electronic supplementary Appendix).

Case–Control Design Statistical Methods Descriptive statistics were used to report patient characteristics assessed at the time of hospital admission, including age, sex, residential status (living in the community or an aged care facility).

Conditional logistic regression was used to determine the association between exposure and acute kidney injury using non-exposure as the reference. The model was implemented once as a full model adjusting for other nephrotoxic medicines and number of dispensings of medicines in the year prior to the hospitalisation. ORs and 95% CIs were calculated for the medicines at a group and individual medicine level.

We used SAS version 9.4 (SAS Institute, Cary, NC, USA) to analyse the data.

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