How do SGLT2 inhibitors protect the kidney? A mediation analysis of the EMPA-REG OUTCOME trial.

INTRODUCTION: Mechanisms underlying kidney benefits with sodium-glucose cotransporter-2 (SGLT2) inhibition in heart failure and/or type 2 diabetes (T2D) with established cardiovascular disease are currently unclear. METHODS: We evaluated post hoc the factors mediating the effect of empagliflozin on a composite kidney outcome (first sustained estimated glomerular filtration rate ≥40% reduction from baseline, initiation of renal replacement therapy or death due to kidney disease) in EMPA-REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients). Variables, calculated as change from baseline or updated mean, were evaluated as time-dependent covariates and using a landmark approach (at Week 12) in Cox regression analyses. In multivariable analyses, variables with the greatest mediating effect were added using a step-up procedure. RESULTS: In univariable time-dependent updated mean covariate analyses, the strongest mediator was hematocrit (99.5% mediation). Hemoglobin, uric acid and urine albumin-to-creatinine ratio mediated 79.4%, 33.2% and 31.0%, respectively. Multivariable analyses were not performed due to the very strong mediation effect of hematocrit. In univariable Week 12 landmark change from baseline analyses, the strongest mediators included hematocrit (40.7%), glycated hemoglobin (28.3%), systolic blood pressure (16.8%) and free fatty acids (16.5%), which yielded a combined mediation of 78.9% in multivariable analysis. CONCLUSIONS: Changes in hematocrit and hemoglobin were the strongest mediators of empagliflozin's kidney benefits in EMPA-REG OUTCOME participants with T2D and cardiovascular disease.

Mediators of the improvement in heart failure outcomes with empagliflozin in the EMPA-REG OUTCOME trial.

AIMS: In the EMPA-REG OUTCOME trial, empagliflozin reduced risk of death from heart failure (HF) or hospitalization for heart failure (HHF) versus placebo in patients with type 2 diabetes mellitus (T2DM) and established cardiovascular (CV) disease. We evaluated post hoc the degree to which covariates mediated the effects of empagliflozin on HHF or HF death. METHODS AND RESULTS: A mediator had to fulfil the following criteria: (i) affected by active treatment, (ii) associated with the outcome, and finally (iii) adjustment for it results in a reduced treatment effect compared with unadjusted analysis. Potential mediators were calculated as change from baseline or updated mean and evaluated in univariable analyses as time-dependent covariates in Cox regression of time to HHF or HF death; those with the largest mediating effects were then included in a multivariable analysis. Increases in heart rate, log urine albumin-to-creatinine ratio (UACR), waist circumference, and uric acid were associated with increased risk of HHF or HF death; increases in high-density lipoprotein cholesterol, estimated glomerular filtration rate, haematocrit, haemoglobin, and albumin were associated with reduced risk of HHF or HF death. In univariable analyses, change from baseline in haematocrit, haemoglobin, albumin, uric acid, and logUACR mediated 51%, 54%, 23%, 24%, and 27% of the risk reduction with empagliflozin versus placebo, respectively. Multivariable analysis including haemoglobin, logUACR, and uric acid mediated 85% of risk reduction with similar results when updated means were evaluated. CONCLUSIONS: Changes in haematocrit and haemoglobin were the most important mediators of the reduction in HHF and death from HF in patients with T2DM and established CV disease treated with empagliflozin. Albumin, uric acid, and logUACR had smaller mediating effects in this population.

Which patients to sample in clinical cohort studies when the number of events is high and measurement of additional markers is constrained by limited resources.

PURPOSE: We consider an existing clinical cohort with events but limited resources for the investigation of a further potentially expensive marker. Biological material of the patients is stored in a biobank, but only a limited number of samples can be analyzed with respect to the marker. The question arises as to which patients to sample, if the number of events preclude standard sampling designs. METHODS: Modifications of the nested case-control and the case-cohort design for the proportional hazards model are applied, that allow efficient sampling in situations where standard nested case-control and case-cohort are not feasible. These sampling designs are compared to simple random sampling and extreme group sampling, the latter including only patients with extreme outcomes, ie either with an event early in time or without an event until at least a point later in time. RESULTS: The modified nested case-control design and the modified case-cohort design provide powerful methods for sampling in a clinical cohort with many events. The simple random sampling usually is less efficient. If focus is on precise estimation of a potential effect in terms of a hazard ratio, extreme group sampling is not competitive. If focus is on screening for important biomarkers, extreme group sampling markedly outperforms the other sampling designs. CONCLUSIONS: When it is not feasible to sample all events, a modified nested case-control design or case-cohort design leads to efficient effect estimates in the proportional hazards model. If screening for important biomarkers is the primary objective, extreme group sampling is preferable.

Exposure density sampling: Dynamic matching with respect to a time-dependent exposure.

Estimating the potential risk associated with an exposure occurring over time requires complex statistical techniques, since ignoring the time from study entry until the exposure leads to potentially seriously biased effect estimates. A prominent example is estimating the effect of hospital-acquired infections on adverse outcomes in patients admitted to the intensive care unit. Exposure density sampling has been proposed as an approach to dynamic matching with respect to a time-dependent exposure. Firstly, exposure density sampling can be useful to reduce the workload of study follow up, as it includes all exposed but only a subset of the not yet exposed individuals. Secondly, it can help to obtain a comparable control group by including propensity score matching. In the present article, we provide the theoretical justification that data obtained by exposure density sampling can be analyzed as a left-truncated cohort. It is shown that exposure density sampling allows estimation of the effect of a time-dependent exposure as well as further baseline covariates on a subsequent event, with only minor loss in precision as compared with a full cohort analysis. The sampling is applied to a real data example (hospital-acquired infections in intensive care units) and in a simulation study. We also provide an estimate of the loss in precision in terms of an increased standard error in the reduced data set after exposure density sampling as compared with the full cohort.

Risk prediction for Staphylococcus aureus surgical site infection following cardiothoracic surgery; A secondary analysis of the V710-P003 trial.

BACKGROUND: Identifying patients undergoing cardiothoracic surgery at high risk of Staphylococcus aureus surgical site infection (SSI) is a prerequisite for implementing effective preventive interventions. The objective of this study was to develop a risk prediction model for S. aureus SSI or bacteremia after cardiothoracic surgery based on pre-operative variables. MATERIALS/METHODS: Data from the Merck Phase IIb/III S. aureus vaccine (V710-P003) clinical trial were analyzed. In this randomized placebo-controlled trial, the effect of preoperative vaccination against S. aureus was investigated in patients undergoing cardiothoracic surgery. The primary outcome was deep/superficial S. aureus SSI or S. aureus bacteremia through day 90 after surgery. Performance, calibration, and discrimination of the final model were assessed. RESULTS: Overall 164 out of 7,647 included patients (2.1%) developed S. aureus infection (149 SSI, 15 bacteremia, 28 both). Independent risk factors for developing the primary outcome were pre-operative colonization with S. aureus (OR 3.08, 95% confidence interval [CI] 2.23-4.22), diabetes mellitus (OR 1.87, 95% CI 1.34-2.60), BMI (OR 1.02 per kg/m2, 95% CI 0.99-1.05), and CABG (OR 2.67, 95% CI 1.91-3.78). Although vaccination had a significant (albeit modest) protective effect, it was omitted from the model because its addition did not significantly change the coefficients of the final model and V710-vaccine development has been discontinued due to insufficient efficacy. The final prediction model had moderate discriminative accuracy (AUC-value, 0.72). CONCLUSION: Pre-operative S. aureus colonization status, diabetes mellitus, BMI, and type of surgical procedure moderately predicted the risk of S. aureus SSI and/or bacteremia among patients undergoing cardiothoracic surgery.

How Does Empagliflozin Reduce Cardiovascular Mortality? Insights From a Mediation Analysis of the EMPA-REG OUTCOME Trial.

OBJECTIVE: In the BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) trial involving 7,020 patients with type 2 diabetes and established cardiovascular (CV) disease, empagliflozin given in addition to standard of care reduced the risk of CV death by 38% versus placebo (hazard ratio [HR] 0.62 [95% CI 0.49, 0.77]). This exploratory mediation analysis assesses the extent to which treatment group differences in covariates during the trial contributed to CV death risk reduction with empagliflozin. RESEARCH DESIGN AND METHODS: Effects of potential mediators, identified post hoc, on the HR for CV death with empagliflozin versus placebo were analyzed by Cox regression models, with treatment group adjusted for the baseline value of the variable and its change from baseline or updated mean (i.e., considering all prior values), each as a time-dependent covariate. HRs were compared with a model without adjustment for covariates. Multivariable analyses also were performed. RESULTS: Changes in hematocrit and hemoglobin mediated 51.8% and 48.9%, respectively, of the effect of empagliflozin versus placebo on the risk of CV death on the basis of changes from baseline, with similar results in analyses on the basis of updated means. Smaller mediation effects (maximum 29.3%) were observed for uric acid, fasting plasma glucose, and HbA1c. In multivariable models, which incorporated effects of empagliflozin on hematocrit, fasting glucose, uric acid, and urine albumin:creatinine ratio, the combined changes from baseline provided 85.2% mediation, whereas updated mean analyses provided 94.6% mediation of the effect of empagliflozin on CV death. CONCLUSIONS: In this exploratory analysis from the EMPA-REG OUTCOME trial, changes in markers of plasma volume were the most important mediators of the reduction in risk of CV death with empagliflozin versus placebo.

Modelling two cause-specific hazards of competing risks in one cumulative proportional odds model?

Competing risks extend standard survival analysis to considering time-to-first-event and type-of-first-event, where the event types are called competing risks. The competing risks process is completely described by all cause-specific hazards, ie, the hazard marked by the event type. Separate Cox models for each cause-specific hazard are the standard approach to regression modelling, but they come with the interpretational challenge that there are as many regression coefficients as there are competing risks. An alternative approach is to directly model the cumulative event probabilities, but again, there will be as many models as there are competing risks. The aim of this paper is to investigate the usefulness of a third alternative. Proportional odds modelling of all cause-specific hazards summarizes the effect of one covariate on "opposing" competing outcomes in one regression coefficient. For instance, if the competing outcomes are hospital death and alive discharge from hospital, the modelling assumption is that a covariate affects both outcomes in opposing directions, but the effect size is of the same absolute magnitude. We will investigate the interpretational aspects of the approach analysing a data set on intensive care unit patients using parametric methods.