Post-Authorization Safety Study of Hospitalization for Acute Kidney Injury in Patients with Type 2 Diabetes Exposed to Dapagliflozin in a Real-World Setting

Study Design and Setting

This population-based, noninterventional, retrospective cohort study was performed by using an active-comparator, new-user design [20] with data from three real-world, longitudinal databases—one in the UK (Clinical Practice Research Datalink [CPRD]) and two in the US (the HealthCore Integrated Research Database [HIRD] and the Medicare database). CPRD is an electronic primary healthcare medical records database, with linkage to hospital data through the Hospital Episode Statistics database; the HIRD is an administrative claims database including commercially insured individuals; and the Medicare research database includes information on federally funded insurance claims. For this study, Medicare enrollees with fee-for-service insurance were used. The study period varied across the data sources; the start of the study period was defined as the date that dapagliflozin became available in each country after regulatory approval, and the end of the study period was defined as the timing of the most recent data available at the time of data extraction (CPRD: November 2012 through December 2018; the HIRD: January 2014 through February 2019; Medicare: January 2014 through December 2017).

The study protocol was reviewed and approved by the RTI International Institutional Review Board. For the CPRD and Medicare aspects of the study, the UK Medicines Healthcare products Regulatory Agency’s Independent Scientific Advisory Committee as well as the Centers for Medicare and Medicaid Services’ Privacy Board reviewed the study protocol and approved the use of the respective data for this study. HealthCore-specific components were reviewed and approved by the New England Institutional Review Board. A waiver of informed consent was obtained, as data used in these studies were obtained from databases of anonymized medical, claims, and pharmacy records and not directly from human subjects.

Study Population

The population for this study comprised adult patients (CPRD: 18 years or older; the HIRD: aged 18–64 years; Medicare: aged 65 years or older) initiating dapagliflozin or an eligible comparator GLD (Table 1), with or without concomitant use of insulin or any other GLD. As dapagliflozin was recommended as a second-line therapy for T2DM at the time of the study [21, 22], monotherapy with metformin or a sulfonylurea (first-line therapy) were not considered eligible comparators. In addition, monotherapy with insulin was not an eligible comparator. ‘New use’ was defined as the first recorded prescription/dispensing for an eligible study drug during the study period, without any prior recorded prescription/dispensing for that medication using all available lookback data before the first prescription/dispensing (a minimum lookback period of 180 days before the first prescription/dispensing was required for study eligibility). The date of new use of a study drug was labeled as the index date, and the period of time a patient remained continuous on that treatment defined a treatment episode. To obtain a sufficient sample size and retain statistical power, a given patient could potentially contribute more than one nonoverlapping treatment episode within the study period for different eligible medications.

Table 1 Glucose-lowering drugs eligible for inclusion in the comparator GLD group

Patients were excluded if on or before the index date of the eligible treatment episode they had a recorded use of a non-dapagliflozin SGLT2 inhibitor, were diagnosed with type 1 diabetes mellitus, or were diagnosed with chronic kidney disease (Fig. S1 in Online Resource 1 illustrates the study design and cohort eligibility, see electronic supplementary material [ESM]). Patients were also excluded if they had a recorded use of dapagliflozin before the start of the study period or had an acute kidney injury diagnosis during the period of 180 days before (and including) the index date.

Eligible comparator GLD treatment episodes were randomly matched to dapagliflozin treatment episodes at a ratio of 6:1 in CPRD and 15:1 in the HIRD and Medicare on each of the following variables: calendar year of the index date, age, sex, and geographic region.

VariablesExposure

The primary exposure of interest for this study was initiation of dapagliflozin or an eligible comparator GLD. Medication use was identified in written prescription records in CPRD GOLD (General Practitioner Online Database) using Gemscript codes or in pharmacy dispensing records in the US claims data using National Drug Codes (NDCs) or Generic Product Identifier (GPI).

Exposure time at risk was defined for each treatment episode based on the assumption that any potential risk of hAKI would increase shortly after therapy initiation, remain increased during treatment, and then decrease gradually after treatment discontinuation. Therefore, for each treatment episode, patients were considered ‘at risk’ starting the day after the index date until 30 days after the end of the days’ supply of the last consecutive prescription or dispensing in the treatment episode. When there was more than one consecutive prescription/dispensing for the index medication with gaps of 30 days or fewer separating the prescriptions/dispensings, the prescriptions/dispensings were concatenated into one treatment episode; the duration of the treatment episode included the gaps between the prescriptions/dispensings and ended 30 days after the end of the days’ supply of the last prescription/dispensing.

Outcome

The primary outcome was hAKI. Outcomes were evaluated during the exposure time at risk with electronic algorithms tailored to each data source (Table 2). A sample of up to 125 algorithm-identified cases of hAKI in each data source were reviewed for validation. In the CPRD electronic medical records database, validation was performed by clinician review of chronological patient profiles and by completed questionnaires from general practitioners. In the HIRD and Medicare claims databases, validation was performed by clinician review of medical records. The hAKI cases included in the validation sample were confirmed as cases or noncases according to a clinical case definition based on a subset of the RIFLE (Risk of renal dysfunction, Injury to the kidney, Failure of kidney function, Loss of kidney function, and End-stage kidney disease) criteria proposed by the Acute Dialysis Quality Initiative [23].

Table 2 Electronic algorithms for identifying cases of hAKICovariates

Baseline characteristics included demographic and lifestyle characteristics, comorbidities, comedications, and healthcare resource utilization and were assessed on or before the index date for each treatment episode (Tables S1, S2, and S3 in Online Resource 1 display all measured baseline covariates, see ESM). Baseline covariates were assessed with the use of all available lookback data unless otherwise specified; the mean duration of available lookback time was about 12 years in CPRD, about 3 years in the HIRD, and about 4 years in Medicare. Healthcare resource utilization variables included as covariates were the number of outpatient encounters with a general practice or outpatient clinic, number of hospitalizations, number of emergency department visits, and number of specialty care visits in the 180 days before the index date. Comedications were assessed in the 180 days before and including the index date.

Statistical Analysis

Each data source was analyzed separately. Data from CPRD and Medicare were analyzed by RTI Health Solutions, and data from the HIRD were analyzed by HealthCore, Inc. Descriptive analyses were conducted to describe the study sample. For categorical variables, frequencies and percentages were calculated; for continuous variables, means and standard deviations or medians, interquartile ranges, or minimum and maximum values were calculated. The balance of baseline characteristics was compared between dapagliflozin and comparator GLD treatment episodes by calculating the absolute standardized difference between the two exposure groups [24].

Potential confounding was addressed with the use of propensity score (PS) methods, including trimming and stratification, to identify dapagliflozin and comparator GLD groups with balanced characteristics [25,26,27,28]. Propensity scores, which quantify the predicted probability of initiating treatment with dapagliflozin or a comparator GLD given the observed patient characteristics at the time of treatment initiation, were estimated for each treatment episode by fitting a multivariable logistic regression model, which included dapagliflozin or comparator GLD initiation as the dependent variable and baseline covariates as independent variables (detailed information on PS modeling is provided in Online Resource 1). All baseline variables (see Section 2.3.3) were considered for inclusion in the PS models, including demographic and lifestyle characteristics, number of years since the initial T2DM diagnosis, diabetes severity indicators, comedications, comorbidities, and healthcare utilization variables, as well as calendar year of the index date and the type of index medication. The PS distribution was plotted by treatment group, and treatment episodes with extreme PS values (i.e., below the 2.5th percentile value of the dapagliflozin-exposed PS distribution and above the 97.5th percentile of the comparator GLD PS distribution) were trimmed (i.e., excluded from the analytic sample). Baseline characteristics in the dapagliflozin and comparator GLD groups were compared, both before and after PS trimming. Finally, the remaining treatment episodes were ranked by PS value and divided into equally sized strata; confounding control was assessed by using the absolute standardized difference values to evaluate the balance of key covariates between treatment groups within each PS stratum.

Incidence rates (IRs) and IR ratios (IRRs) were calculated by using algorithm-identified hAKI events. Propensity score-adjusted IRs were estimated by standardizing crude IRs across the PS strata within each exposure group. First, the IR for each exposure group was estimated separately for each PS stratum. Then, for each exposure group, the stratum-specific IR was standardized by using the person-years in the dapagliflozin cohort to estimate the standardized IR and variance, with the 95% confidence intervals (CIs) estimated using the exact limits method [29].

Incidence rate ratios were calculated by dividing the IR in the dapagliflozin group by the IR of the comparator GLD group, and the estimation of 95% CIs were based on a Poisson distribution. Adjusted IRRs were estimated by calculating the IRR within each PS stratum, and the Mantel-Haenszel method was used to combine the stratum-specific IRRs [30]. The Mantel-Haenszel method was also used to pool database-specific adjusted IRRs and generate an overall adjusted IRR estimate and 95% CI [30] (information on the pooling method is provided in Online Resource 1, see ESM).

Subgroup analyses estimated IRs and IRRs stratified by insulin use at the index date where feasible due to sample size. Several sensitivity analyses were performed and comprised (1) an extension of the risk window from 30 days to 90 days after the end of the medication’s days’ supply, (2) the inclusion of only dipeptidyl peptidase-4 (DPP-4) inhibitors in the comparator GLD group, (3) the inclusion of only glucagon-like peptide-1 (GLP-1) receptor agonists in the comparator GLD group, (4) the inclusion of only patients new to the comparator GLD class (i.e., without previous use of any drug within the GLD class), and (5) the inclusion of only the first treatment episode for an individual patient. Quantitative bias analysis was performed to assess the possible effect of potential unmeasured confounding variables of various strengths and prevalences on the effect estimate (information on the quantitative bias analysis methods is provided in Online Resource 1, see ESM) [31].

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