Loop Diuretics and Weight Change in Heart Failure A Meta-Analysis

Lead Investigator: Ly-Mee Yu, University of Oxford
Title of Proposal Research: Loop Diuretics and Weight Change in Heart Failure A Meta-Analysis
Vivli Data Request: 9137
Funding Source: This is a part of staff’s professional development scheme from the employer to pursue MSc degree
Potential Conflicts of Interest: Dr. Phan reports: The data will be used for dissertation at University of Oxford, funded by the Yen’s employer CodLad as a part of staff’s professional development scheme. Yen Phan is an external employee of Boehringer Ingelheim.
All publications, presentations, and public communications related to this dissertation will include a full disclosure of my employment status with CodLad and my external affiliation with Boehringer Ingelheim. This disclosure will clearly state the nature of my relationships with these entities and how they may influence the research. My academic advisors at the University of Oxford will provide oversight and guidance throughout the research process to help identify and mitigate any biases or undue influences resulting from my affiliations. Regular meetings will be held to review the research progress and discuss any potential COI issues.

Summary of the Proposed Research:

a. Project Background:
Heart failure, often simply referred to as “heart disease,” is a condition where the heart muscle weakens and can’t efficiently pump blood to meet the body’s needs. It is a growing concern, impacting millions globally. This circulatory insufficiency can lead to symptoms like fatigue, breathlessness, and fluid retention.

b. Loop Diuretics Explained:
Loop diuretics are a class of medications commonly prescribed to heart failure patients. Their primary function is to promote diuresis, the increased production of urine. They achieve this by acting on the nephrons (which are the functional unit of the kidney, the structure that actually produces urine in the process of removing waste and excess substances from the blood), facilitating the removal of excess salt and water from the body. This is crucial in heart failure where fluid can accumulate in the body, leading to conditions like edema which is the medical term for swelling caused by fluid trapped in your body’s tissues. Edemas usually occur when small blood vessels leak fluid into the surrounding tissues, causing them to become swollen, and mostly are seen in the feet, ankles and legs.

c. The Necessity of this Research:
Although loop diuretics’ primary effects are well-documented, there’s limited comprehensive data on their specific impact on heart failure patients, especially concerning weight fluctuations during treatment. Delving deeper into this aspect will bridge the existing knowledge gap, enhancing clinical practices and optimizing patient outcomes.

d. Our Research Approach:
This project will analyze the use of loop diuretics in the treatment of heart failure and how they correlate to weight change during the treatment period in subgroup populations. To thoroughly investigate this topic, we’re undertaking a meta-analysis. This entails aggregating and systematically reviewing data from multiple studies to yield a consolidated outcome. Meta-analyses are favored for their ability to provide a robust and comprehensive understanding, drawing from vast datasets. For our project, this approach ensures our conclusions have both depth and breadth, offering valuable insights for future clinical applications.

Statistical Analysis Plan:

1. Selection Criteria for Studies:
We will employ a comprehensive search strategy, focusing on studies that:

– Investigate the impact of loop diuretics on heart failure outcomes.
– Have a sample size >100 participants.
– Are randomized controlled trials (RCTs) or observational studies with robust design.
– Publish results in the last 10 years in English-language journals.

2. Incorporation of External Studies:
In addition to the data requested, we do not plan to involve studies from other sources.

3. Analytical Strategy:

– Descriptive Analysis: Frequency, mean, median, standard deviation, and inter-quartile range for relevant variables.
– Bivariate Analysis: T-tests for continuous variables and chi-squared tests for categorical variables.
– Multivariable Analysis: Multiple logistic regression for binary outcomes and multiple linear regression for continuous outcomes.

4. Advanced Analyses:

– Propensity score matching to control for confounding.
– Kaplan-Meier survival curves for time-to-event data.
– Cox proportional hazards regression for time-to-event analysis.

5. Effect Measure:

– Risk ratios and odds ratios for inferential studies.
– Incidence rates with 95% confidence intervals for descriptive analyses.

6. Controlling Bias:

– Restriction: Including patients who met specific criteria.
– Matching: Based on age, sex, and baseline severity.
– Stratification: Age groups, severity levels.
– Covariate Adjustment: Adjusting for potential confounders in regression models.

7. Assumptions and Adjustments:

– Linearity for continuous variables in regression models.
– Meta-regression to explore potential sources of heterogeneity.

8. Statistical Approach:

– Frequentist (classical) approach.
– Random effects for meta-analysis to account for between-study variability.

9. Statistical Tests:

– Fisher’s exact test for small sample sizes.
– Log-rank test for comparing survival curves.
– Bonferroni correction for multiplicity adjustments.

10. Power and Significance:

– 80% power to detect a minimum clinically relevant difference.
– Alpha level set at 0.05 for statistical significance.

11. Model Fit and Sensitivity:

– Chi-squared goodness-of-fit test.
– I^2 statistic to assess heterogeneity in meta-analysis.

12. Subgroup Analyses:
By:

– Age groups (e.g., elderly patients, middle-aged adults)
– Sex or gender (e.g., male vs. female)
– Comorbidities (e.g., hypertension, diabetes)
– Co-medications (e.g., concomitant use of other cardiovascular medications)
– Duration of heart failure diagnosis
– Severity of heart failure

13. Intervention Types:
Different doses of loop diuretics will be compared.

14. Missing Data Handling:
Multiple imputation techniques, using the Markov Chain Monte Carlo method, for significant variables. Random missing data will be excluded.

15. Tools and Scripts:
R, SAS and Stada for data processing and statistical analysis. Custom scripts for specialized analyses.

Special Note on Outcome Assessments:

– Missing data will be imputed using multiple imputation techniques.
– Handling of different outcome measures across studies: Standardization to a common scale or transformation.
– Each study will be analyzed separately to maintain independence.
– Different study designs will be incorporated using a random-effects meta-analysis.
– Enrollment criteria differences will be addressed using propensity score matching.
– The analysis will be conducted primarily in Ireland and UK, but with global implications.

Relation to Hypothesis:
The data requested will provide insights into the effectiveness and safety of loop diuretics in heart failure patients. With the detailed plan mentioned above, we aim to comprehensively answer the research question, ensuring that the results are robust, reliable, and contribute meaningfully to the current literature.

Requested Studies:

A Phase III Randomised, Double-blind Trial to Evaluate Efficacy and Safety of Once Daily Empagliflozin 10 mg Compared to Placebo, in Patients With Chronic Heart Failure With Reduced Ejection Fraction (HFrEF)
Data Contributor: Boehringer Ingelheim
Study ID: NCT03057977
Sponsor ID: 1245.121

A Phase III Randomised, Double-blind Trial to Evaluate Efficacy and Safety of Once Daily Empagliflozin 10 mg Compared to Placebo, in Patients With Chronic Heart Failure With Preserved Ejection Fraction (HFpEF)
Data Contributor: Boehringer Ingelheim
Study ID: NCT03057951
Sponsor ID: 1245.110

A Randomized Parallel-group, Placebo-controlled, Double-blind, Multi-center Dose Finding Phase II Trial Exploring the Pharmacodynamic Effects, Safety and Tolerability, and Pharmacokinetics of Four Dose Regimens of the Oral sGC Stimulator BAY1021189 Over 12 Weeks in Patients With Worsening Heart Failure With Reduced Ejection Fraction (HFrEF)
Data Contributor: Bayer
Study ID: NCT01951625
Sponsor ID: 15371

A 52-week double-blind study of the effect of rosiglitazone on cardiovascular structure and function in subjects with type 2 diabetes mellitus and congestive heart failure (NYHA class I/II)
Data Contributor: GlaxoSmithKline
Study ID: 49653/211
Sponsor ID: 49653/211

A Randomized Parallel-group, Placebo-controlled, Double-blind, Multi-center Trial to Evaluate the Efficacy and Safety of the Oral sGC stImulator Vericiguat to Improve Physical Functioning in Activities of Daily Living in Patients With Heart Failure and Preserved Ejection Fraction (VITALITY-HFpEF)
Data Contributor: Bayer
Study ID: NCT03547583
Sponsor ID: 19334

Public Disclosure:

Phan, Y., 2024. Meta-Analysis on Aggregate Data and Individual Patient Data Using R. Phuse EU Connect. Abstract PP06.