Dataset Information

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers.

ABSTRACT:

Background

Besides improving glucose control, sodium-glucose co-transporter 2 inhibition with dapagliflozin reduces blood pressure, body weight and urinary albumin:creatinine ratio (UACR) in patients with type 2 diabetes (T2DM). The parameter response efficacy (PRE) score was developed to predict how short-term drug effects on cardiovascular risk markers translate into long-term changes in clinical outcomes. We applied the PRE score to clinical trials of dapagliflozin to model the effect of the drug on kidney and heart failure (HF) outcomes in patients with T2DM and impaired kidney function.

Methods

The relationships between multiple risk markers and long-term outcome were determined in a background population of patients with T2DM with a multivariable Cox model. These relationships were then applied to short-term changes in risk markers observed in a pooled database of dapagliflozin trials (n?=?7) that recruited patients with albuminuria to predict the drug-induced changes to kidney and HF outcomes.

Results

A total of 132 and 350 patients had UACR?>200?mg/g and >30?mg/g at baseline, respectively, and were selected for analysis. The PRE score predicted a risk change for kidney events of -40.8% [95% confidence interval (CI) -51.7 to -29.4) and -40.4% (95% CI -48.4 to -31.1) with dapagliflozin 10?mg compared with placebo for the UACR?>200?mg/g and >30?mg/g subgroups. The predicted change in risk for HF events was -27.3% (95% CI -47.7 to -5.1) and -21.2% (95% CI -35.0 to -7.8), respectively. Simulation analyses showed that even with a smaller albuminuria-lowering effect of dapagliflozin (10% instead of the observed 35% in both groups), the estimated kidney risk reduction was still 26.5 and 26.8%, respectively.

Conclusions

The PRE score predicted clinically meaningful reductions in kidney and HF events associated with dapagliflozin therapy in patients with diabetic kidney disease. These results support a large long-term outcome trial in this population to confirm the benefits of the drug on these endpoints.

SUBMITTER: Idzerda NMA

PROVIDER: S-EPMC7473803 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers.

Idzerda Nienke M A NMA Stefansson Bergur V BV Pena Michelle J MJ Sjostrom David C DC Wheeler David C DC Heerspink Hiddo J L HJL

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 20200901 9

<h4>Background</h4>Besides improving glucose control, sodium-glucose co-transporter 2 inhibition with dapagliflozin reduces blood pressure, body weight and urinary albumin:creatinine ratio (UACR) in patients with type 2 diabetes (T2DM). The parameter response efficacy (PRE) score was developed to predict how short-term drug effects on cardiovascular risk markers translate into long-term changes in clinical outcomes. We applied the PRE score to clinical trials of dapagliflozin to model the effect ...[more]

PMID: 31005993

Similar Datasets

Project description:A recent phase II clinical trial (Reducing Residual Albuminuria in Subjects with Diabetes and Nephropathy with AtRasentan trial and an identical trial in Japan (RADAR/JAPAN)) showed that the endothelin A receptor antagonist atrasentan lowers albuminuria, blood pressure, cholesterol, hemoglobin, and increases body weight in patients with type 2 diabetes and nephropathy. We previously developed an algorithm, the Parameter Response Efficacy (PRE) score, which translates short-term drug effects into predictions of long-term effects on clinical outcomes.We used the PRE score on data from the RADAR/JAPAN study to predict the effect of atrasentan on renal and heart failure outcomes.We performed a post-hoc analysis of the RADAR/JAPAN randomized clinical trials in which 211 patients with type-2 diabetes and nephropathy were randomly assigned to atrasentan 0.75?mg/day, 1.25?mg/day, or placebo. A PRE score was developed in a background set of completed clinical trials using multivariate Cox models. The score was applied to baseline and week-12 risk marker levels of RADAR/JAPAN participants, to predict atrasentan effects on clinical outcomes. Outcomes were defined as doubling serum creatinine or end-stage renal disease and hospitalization for heart failure.The PRE score predicted renal risk changes of -23% and -30% for atrasentan 0.75 and 1.25?mg/day, respectively. PRE scores also predicted a small non-significant increase in heart failure risk for atrasentan 0.75 and 1.25?mg/day (+2% vs. +7%). Selecting patients with >30% albuminuria reduction from baseline (responders) improved renal outcome to almost 50% risk reduction, whereas non-responders showed no renal benefit.Based on the RADAR/JAPAN study, with short-term changes in risk markers, atrasentan is expected to decrease renal risk without increased risk of heart failure. Within this population albuminuria responders appear to contribute to the predicted improvements, whereas non-responders showed no benefit. The ongoing hard outcome trial (SONAR) in type 2 diabetic patients with >30% albuminuria reduction to atrasentan will allow us to assess the validity of these predictions.

Project description:Aims: The LEADER trial demonstrated that the glucagon-like peptide-1 receptor agonist (GLP1-RA) liraglutide reduces kidney and cardiovascular (CV) risk in patients with type 2 diabetes. We previously developed a Parameter Response Efficacy (PRE) score that translates multiple short-term risk marker changes, from baseline to first available follow-up measurement, into a predicted long-term drug effect on clinical outcomes. The objective of this study was to assess the accuracy of the PRE score in predicting the efficacy of liraglutide in reducing the risk of kidney and CV outcomes. Methods: Short-term changes in glycated hemoglobin (HbA1c), systolic blood pressure (BP), urinary-albumin-creatinine-ratio (UACR), hemoglobin, body weight, high-density-lipoprotein (HDL) cholesterol, low-density-lipoprotein (LDL) cholesterol, and potassium were monitored in the LEADER trial. Associations between risk markers and kidney or CV outcomes were established using a multivariable Cox proportional hazards model in a separate pooled database of 6,355 patients with type 2 diabetes. The regression coefficients were then applied to the short-term risk markers in the LEADER trial to predict the effects of liraglutide on kidney (defined as a composite of doubling of serum creatinine or end-stage kidney disease) and CV (defined as a composite of non-fatal myocardial infarction, non-fatal stroke, and CV death) outcomes. Results: Liraglutide compared to placebo reduced HbA1c (1.4%), systolic BP (3.0 mmHg), UACR (13.2%), body weight (2.3 kg), hemoglobin (2.6 g/L), and increased HDL-cholesterol (0.01 mmol/L) (all p-values <0.01). Integrating multiple risk marker changes in the PRE score resulted in a predicted relative risk reduction (RRR) of 16.2% (95% CI 13.7-18.6) on kidney outcomes which was close to the observed RRR of 15.5% (95% CI -9.0-34.6). For the CV outcome, the PRE score predicted a 7.6% (95% CI 6.8-8.3) RRR, which was less than the observed 13.2% (95% CI 3.2-22.2) RRR. Conclusion: Integrating multiple short-term risk markers using the PRE score adequately predicted the effect of liraglutide on the composite kidney outcome. However, the PRE score underestimated the effect of liraglutide for the composite CV outcome, suggesting that the risk markers included in the PRE score do not fully capture the CV benefit of liraglutide.

Project description:BackgroundAcute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.ResultsWhen trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.ConclusionsA model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient's risk profiles.

Project description:BACKGROUND AND OBJECTIVES:Electrocardiographic (ECG) markers of the temporal dispersion of the myocardial repolarization phase have been shown able to identify chronic heart failure (CHF) patients at high mortality risk. The present prospective single-center study sought to investigate in a well-characterized cohort of decompensated heart failure (HF) patients the ability of short-term myocardial temporal dispersion ECG variables in predicting the 30-day mortality, as well as their relationship with N-terminal Pro Brain Natriuretic Peptide (NT-proBNP) plasmatic values. METHOD:One hundred and thirteen subjects (male: 59, 67.8%) with decompensated CHF underwent 5 min of ECG recording, via a mobile phone. We obtained QT end (QTe), QT peak (QTp) and T peak to T end (Te) and calculated the mean, standard deviation (SD), and normalized index (VN). RESULTS:Death occurred for 27 subjects (24%) within 30 days after admission. Most of the repolarization indexes (QTe mean (p < 0.05), QTeSD (p < 0.01), QTpSD (p < 0.05), mean Te (p < 0.05), TeSD (p < 0.001) QTeVN (p < 0.05) and TeVN (p < 0.01)) were significantly higher in those CHF patients with the highest NT-proBNP (>75th percentile). In all the ECG data, only TeSD was significantly and positively related to the NT-proBNP levels (r: 0.471; p < 0.001). In the receiver operating characteristic (ROC) analysis, the highest accuracy for 30-day mortality was found for QTeSD (area under curve, AUC: 0.705, p < 0.01) and mean Te (AUC: 0.680, p < 0.01), whereas for the NT-proBNP values higher than the 75th percentile, the highest accuracy was found for TeSD (AUC: 0.736, p < 0.001) and QTeSD (AUC: 0.696, p < 0.01). CONCLUSION:Both mean Te and TeSD could be considered as reliable markers of worsening HF and of 30-day mortality. Although larger and possibly interventional studies are needed to confirm our preliminary finding, these non-invasive and transmissible ECG parameters could be helpful in the remote monitoring of advanced HF patients and, possibly, in their clinical management. (ClinicalTrials.gov number, NCT04127162).

Dataset Information

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers.

Background

Methods

Results

Conclusions

Publications

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets