Kidney outcomes with finerenone: an analysis from the FIGARO-DKD study.

BACKGROUND: In FIGARO-DKD, finerenone reduced the risk of cardiovascular events in patients with type 2 diabetes (T2D) and stage 1-4 chronic kidney disease (CKD). In FIDELIO-DKD, finerenone improved kidney and cardiovascular outcomes in patients with advanced CKD. This analysis further explores kidney outcomes in FIGARO-DKD. METHODS: FIGARO-DKD (NCT02545049) included patients with urine albumin-to-creatinine ratio (UACR) 30-<300 mg/g and estimated glomerular filtration rate (eGFR) 25-90 mL/min/1.73 m2 or UACR 300-5000 mg/g and eGFR ≥60 mL/min/1.73 m2. Outcomes included two composite kidney endpoints, a composite of ≥40% decrease in eGFR from baseline sustained over ≥4 weeks, kidney failure or renal death, and a composite of ≥57% decrease in eGFR from baseline sustained over ≥4 weeks, kidney failure or renal death. Changes in albuminuria and eGFR slope were also analyzed. Kidney and CV outcomes were evaluated by baseline UACR. RESULTS: A lower incidence rate for the eGFR ≥40% kidney composite endpoint was observed with finerenone compared with placebo, but the between-group difference was not significant [hazard ratio (HR) = 0.87; 95% confidence interval (CI): 0.76-1.01; P = .069]. A greater treatment effect was observed on the eGFR ≥57% kidney composite endpoint (HR = 0.77; 95% CI: 0.60-0.99; P = 0.041) with a 36% relative risk reduction for end-stage kidney disease. A larger magnitude of effect on kidney outcomes was observed with finerenone versus placebo for patients with severely increased albuminuria than with moderately increased albuminuria. Improvements in UACR, eGFR slope and cardiovascular risk were evident in both subgroups with finerenone. CONCLUSIONS: The present analyses suggest that finerenone protects against kidney disease progression and cardiovascular events in patients with T2D and early- or late-stage CKD.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective. METHODS: The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.

AdEPro: Animation of Adverse Event Profiles-Presentation of an Easy-to-Use App for Visually Exploring Individual Study Data.

MOTIVATION: Reviewing the adverse event data collected in clinical trials is a lengthy and tedious process when these data are presented in the form of tables, data listings, and static graphs. Thus, to enable anyone interested in exploring adverse event data efficiently and relatively independently, we developed AdEPro, a compact, powerful, and easy-to-use interactive app. DESCRIPTION AND USE OF THE APP: AdEPro is an app for (audio-)visualizing adverse event data from clinical trials. The app dynamically displays the onset, severity, and development of selected adverse events on the individual subject level and on the treatment group level. This paper illustrates that there are numerous questions related to adverse events that can be approached by means of AdEPro, e.g., questions about temporal aspects of adverse events, associations between adverse events, and the influence of subject characteristics. AdEPro provides quick first answers to such questions; however, it does not provide statistical proof. Essentially, it acts as a versatile "hypothesis generator," helping the user to decide whether further analyses are indicated. No programming knowledge is required for exploring data by means of AdEPro. However, the user needs some basic knowledge of the software R and of extracting data from a clinical data base. The software code is open source, allowing modifications and expansions of the app, if desired. AVAILABILITY AND IMPLEMENTATION: AdEPro can be freely obtained from https://cran.r-project.org/package=adepro . It runs on any computer on which R is installed. Patient data are stored and processed locally.

Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial.

BACKGROUND: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. PATIENTS AND METHODS: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m2 and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. CONCLUSIONS: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. TRIAL REGISTRATION: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

BACKGROUND: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective. METHODS: The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness. RESULTS: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials. CONCLUSION: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.