Evaluation of Patiromer in Heart Failure Patients: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Evaluation of Patiromer in Heart Failure Patients (NCT00868439), a randomized phase 2 study. The primary outcome is Change From Baseline in Serum Potassium to the End of the 28-day Treatment Period..

2. Clinical Question

3. Trial Design

The registry describes a phase 2, randomized, parallel-group, placebo-controlled study. Its official title describes it as multicenter, double-blind, and multiple-dose; the registry’s masking field says quadruple. These are the record’s design descriptors and do not specify which roles were masked or how masking was maintained. The study status is completed.

The registry lists enrollment of 120 and identifies patiromer and placebo as the two interventions. For the primary outcome, the reported group counts are 55 for patiromer and 49 for placebo. Those counts are the denominators shown with the outcome results, not an explanation of participant flow. Reasons that the reported outcome counts differ from enrollment are not reported in the registry record.

The primary outcome timeframe is baseline and Day 28, described as the end of a 28-day treatment period. The primary analysis used last observation carried forward (LOCF). The registry lists the study start as 2009-04 and primary completion as 2009-11. Its brief summary says the study assessed serum potassium effects as well as safety and tolerability.

The registry record does not report the original sample-size justification, allocation sequence method, concealment procedures, or detailed treatment schedules. Although the study is described as randomized, those operational details should not be inferred from the design label alone.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: Eligible participants ≥ 18 y/o, had history of chronic HF, clinically initiated spironolactone therapy, serum K+ = 4.3 - 5.1 mEq/L at screening and baseline, and either had 1) CKD, w/ eGFR \< 60 mL/min and receiving HF therapies or 2) documented history of hyperkalemia led to discontinuation w/ aldosterone antagonist w/in 6 months prior to baseline.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Analysis was determined using Last Observation Carried Forward (LOCF).

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Population: One participant received one dose of patiromer and did not return to the clinic in a timely manner; the participant was terminated from the study for protocol noncompliance, and because the participant did not have post-treatment efficacy data, the participant was excluded from the Full Analysis Set but retained in the Safety Analysis Set.

6. Primary Endpoint

The registry lists the primary outcome as Change From Baseline in Serum Potassium to the End of the 28-day Treatment Period., measured over Baseline and Day 28.

The primary endpoint measures the change in serum potassium from baseline to the end of the 28-day treatment period, with results expressed in mEq/L. The posted arm estimates are least-squares means, and their accompanying dispersion is reported as a standard error. The analysis population was determined using last observation carried forward.

As a continuous outcome, this measure retains the magnitude and direction of observed change rather than classifying participants only as above or below a threshold. In general, comparing average change between randomized groups can summarize whether their measured trajectories differed over the stated period. It does not, by itself, show how individual participants changed or whether a difference is clinically important.

The registry identifies baseline and Day 28 as the timeframe, but does not report a clinically meaningful difference threshold. Nor does the primary endpoint directly measure longer-term outcomes. Its interpretation is therefore tied to the stated laboratory measure, the treatment period, and the analysis approach, rather than extending to outcomes not reported in the registry record.

7. Results Posted in the Registry

• Patiromer: mean -0.21 versus 0.23 on control; difference -0.44 (95% CI -0.63 to -0.25), standardized effect d = -0.89; z-test p = <0.001.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Proportion of Participants With a Serum Potassium Level During the 28-day Treatment Period That Was > 5.5 mEq/L.

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Patiromer: 7.3% (4/55) versus 24.5% (12/49) on control; risk difference -17.2 percentage points (95% CI -31.1 to -3.4), risk ratio 0.30 (0.10–0.86); unadjusted two-proportion z-test p = 0.015.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Patiromer than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Proportion of Participants Whose Spironolactone Dose Was Increased.

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Patiromer: 90.9% (50/55) versus 73.5% (36/49) on control; risk difference 17.4 percentage points (95% CI 2.9 to 31.9), risk ratio 1.24 (1.03–1.49); unadjusted two-proportion z-test p = 0.019.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Patiromer than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Proportion of Participants With an Increase in Serum Potassium Level From Baseline to the End of the 28-day Treatment Period That Was ≥ 0.5 mEq/L

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Patiromer: 12.7% (7/55) versus 24.5% (12/49) on control; risk difference -11.8 percentage points (95% CI -26.7 to 3.2), risk ratio 0.52 (0.22–1.21); unadjusted two-proportion z-test p = 0.121.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Patiromer and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes add context by looking at threshold events, medication adjustment, and other aspects of potassium change rather than the primary continuous change measure alone. Across the posted results, the directions are not uniform: some comparisons point toward fewer elevated potassium readings or increases, while the dose-adjustment outcome points in the other direction. The secondary pattern therefore complements rather than duplicates the primary result.

These measures are related to the trial’s stated focus on serum potassium and spironolactone management, but they are not interchangeable with the primary endpoint. A threshold event discards information about values that do not cross the threshold, while a medication adjustment reflects a different outcome from the measured potassium change. Their relationship to the primary result is consequently supportive context, not an independent confirmation of the same question.

The registry names five secondary outcomes, but numerical results are posted for three. Numerical results for discontinuation due to elevated serum potassium and time to first elevated serum potassium are not reported in the registry record. This limits the completeness of the secondary-outcome picture.

Unadjusted secondary findings deserve less weight than a prespecified primary analysis because several outcomes create multiple opportunities to observe a small p-value by chance. The registry record does not report a multiplicity adjustment or a hierarchy for interpreting secondary outcomes. These results are best treated as additional evidence that may motivate further study, with attention to estimates, uncertainty, and the analysis methods, rather than as definitive standalone conclusions.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Proportion of Participants With a Serum Potassium Level During the 28-day Treatment Period That Was > 5.5 mEq/L.
• Proportion of Participants Discontinuing the Study Due to Serum Potassium Elevation (Serum K+ > 5.5 mEq/L).
• Proportion of Participants Whose Spironolactone Dose Was Increased.
• Proportion of Participants With an Increase in Serum Potassium Level From Baseline to the End of the 28-day Treatment Period That Was ≥ 0.5 mEq/L
• Time to First Elevated Serum K+ > 5.5 mEq/L.

9. Statistical Interpretation

10. Statistical Methods Explained

What does the primary comparison represent?

The primary outcome is continuous: it records change in serum potassium in mEq/L. The registry reports least-squares means for the groups and standard errors as their dispersion measure. A least-squares mean is an estimated group mean from a statistical model; in general, model-based means may account for terms included in that model. The registry names ANCOVA for the sponsor-reported primary test, but does not provide the model specification, covariates, or adjusted estimate. The posted means should therefore be read with the labels attached to them, not as a complete description of the fitted model.

The recalculated mean difference of -0.44 is an unadjusted contrast based on the posted summary statistics. It expresses the patiromer estimate relative to the placebo estimate, with a negative direction meaning the patiromer group’s reported change estimate is lower. This re-calculation is not a substitute for the sponsor’s ANCOVA. The registry reports p < 0.001 for that ANCOVA analysis, but its model details are not reported in the registry record.

Why report a standard error and a confidence interval?

In general, a standard error describes the sampling uncertainty of an estimated quantity, under the assumptions of the analysis. It is not the spread of individual participants’ measurements. A confidence interval gives a range of values compatible with the data and method at the stated confidence level. Narrower intervals indicate greater precision on that scale; wider intervals indicate more uncertainty. The primary recalculated interval lies on one side of zero, while the registry’s sponsor-reported ANCOVA result is expressed as a p-value without a posted estimate or interval.

The arm standard errors are 0.066 for patiromer and 0.072 for placebo. They should not be interpreted as individual-level variability. The registry does not report standard deviations for the primary outcome, and the pooled standard deviation in the recalculation is an approximation derived from the posted standard errors. In general, uncertainty estimates also depend on the model, missing-data handling, and analysis population, so they do not capture every source of uncertainty in a trial.

How should binary secondary comparisons be understood?

Several secondary measures are proportions. A risk difference contrasts the proportion in one group with the proportion in the other, expressed as an absolute difference. In general, this is distinct from a relative measure such as a risk ratio or odds ratio. The recalculated secondary comparisons use unadjusted summary data; their estimates and p-values should not be assumed to match the sponsor’s tests, which are reported as Fisher Exact tests for the posted comparisons.

A confidence interval for a risk difference that includes zero is compatible with no difference under the interval method; an interval that excludes zero is less compatible with that value. This does not by itself establish clinical importance. The registry’s posted secondary comparisons include intervals that exclude zero for some outcomes and an interval that includes zero for the measure of a large potassium increase. These are separate exploratory comparisons, and their interpretation is affected by the number of outcomes considered.

For binary outcomes, event counts and denominators matter alongside percentages. The registry provides group percentages and outcome counts for some posted measures. In general, a percentage can look more decisive than the underlying count warrants, particularly when the number of events is limited. The available summary results do not provide enough information to reconstruct every analysis detail or to evaluate all possible sources of bias.

What does over-dispersion mean in general?

Over-dispersion occurs when observed variation is greater than a statistical model expects. In general, if a model assumes less variability than the data contain, standard errors may be too small and intervals too narrow. If the model accounts for over-dispersion, uncertainty estimates can better reflect the extra variation. The registry does not report an over-dispersion model for these outcomes, so it should not be assumed to have been used.

Time-to-event outcomes generally concern the time until an event and may account for participants who have not had the event by the end of observation. In general, such analyses require details about event definitions, follow-up, censoring, and the statistical method. Although time to first elevated serum potassium is named as a secondary outcome, its numerical results and analysis details are not reported in the registry record.

11. Confidence Intervals and Precision

Precision is about how tightly an estimate is determined, not whether the estimate is clinically important. The primary recalculated confidence interval lies entirely below zero, which indicates that its stated interval method does not include a zero mean difference. The interval is derived from posted summary statistics and is distinct from the sponsor-reported ANCOVA test. The registry does not give a confidence interval for the sponsor’s adjusted analysis.

The primary arm standard errors, 0.066 and 0.072, describe uncertainty around the reported group estimates under the relevant analysis. They are not ranges containing most individual participants. The registry does not provide the sponsor’s adjusted effect estimate or interval, nor enough model details to determine how adjustment affects the magnitude and precision of the contrast.

For the secondary outcomes, recalculated intervals vary: some exclude zero, while the interval for the increase in serum potassium of at least 0.5 mEq/L includes zero. An interval that includes zero leaves a wider range of possible differences compatible with the data; it is not proof that the groups are identical. Conversely, an interval excluding zero does not establish that the difference matters clinically. The registry does not state a clinically important threshold for these comparisons.

Precision is also limited by the reported group sizes and event counts, especially for binary outcomes. In general, fewer observed events tend to leave less information for estimating a proportion difference. The outcome counts available in the registry do not resolve uncertainty about missing observations, participant flow, or the analysis population for each endpoint. These issues are separate from the numerical width of a confidence interval and should inform interpretation of the results.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size table and curve are planning illustrations based on the observed effect, not a record of the trial’s original design calculation. For the continuous primary outcome and the posted binary comparisons, the figures are participants per arm. The stated assumptions are two-sided significance level 0.05, equal allocation between the two arms, and a normal approximation; the effect to detect is the stated fraction of the observed effect. The original trial’s own sample-size assumptions are not reported in the registry record.

The 80% power column gives the illustrative number of participants per arm needed for an 80% chance of detecting the specified effect under those planning assumptions, if that effect and the model assumptions were true. The 90% power column uses the higher target chance of detecting that effect and consequently gives a larger planning figure. These are prospective planning quantities, not a description of the probability that this completed study detected its effect. This is not observed power.

Rows that plan for 100% of the observed effect use the observed estimate as the target; rows for 75% or 50% use smaller target effects. Smaller effects are harder to distinguish from random variation, so they require more participants under the same assumptions. The figures vary by endpoint because continuous and binary outcomes have different data structures and observed effect estimates. The table does not show that those sample sizes were enrolled or that the trial was designed around them.

The registry’s enrollment is 120, while the primary outcome has reported group counts of 55 and 49. The planning illustrations should not be treated as a comparison between those figures or as evidence of the trial’s original statistical power. The registry record does not report the design-stage assumptions needed to assess whether the original target was achieved.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -0.440; pooled standard deviation σ = 0.496.
2. n = 2 × (1.96 + 0.842)² × 0.496² / -0.440² = 20.0, rounded up to 20 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- -0.44                      # observed difference in means
sigma <- 0.496360163353896                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(868439)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 20 (80% power), 27 (90% power)
 75% of observed effect: n per arm = 36 (80% power), 48 (90% power)
 50% of observed effect: n per arm = 80 (80% power), 107 (90% power)
power.t.test: 21 
Simulated power at n = 20 per arm: 0.772 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Analyses Reported by the Sponsor

For the primary outcome, the sponsor-reported analysis is ANCOVA and its p-value is < 0.001. The posted group estimates are least-squares means, but the registry does not report the ANCOVA effect estimate, confidence interval, or model specification. The recalculated mean difference is an unadjusted contrast from summary statistics. It is useful as a transparent descriptive comparison, but it is not a reconstruction of the sponsor’s adjusted model.

The registry also reports Fisher Exact tests for the posted binary secondary comparisons. Recalculated unadjusted p-values differ from the sponsor-reported values for those comparisons. For the proportion with a potassium level above 5.5 mEq/L, the recalculated p-value is 0.015 and the sponsor-reported value is 0.027. For spironolactone dose increase, the corresponding values are 0.019 and 0.022. For an increase in serum potassium of at least 0.5 mEq/L, they are 0.121 and 0.136.

These differences can arise because the calculations use different methods or test details; the registry record does not give enough information to identify the precise reason. The recalculated values should be understood as simple unadjusted analyses, not replacements for the sponsor’s reported methods. The registry does not flag a discrepancy for the primary comparison.

15. Multiplicity, Missing Data and Analysis Populations

The registry identifies one primary outcome and five secondary outcomes. Multiple outcome comparisons raise a multiplicity issue: in general, the more hypotheses tested, the greater the chance that at least one result appears statistically notable by chance alone. A prespecified strategy can address this through adjusted thresholds, a testing hierarchy, or another stated plan. Such a strategy is not reported in the registry record, so the secondary p-values should be read as unadjusted evidence rather than as results known to control the overall false-positive rate.

The primary analysis population was determined using last observation carried forward. In general, LOCF carries a participant’s last observed value forward to a later assessment when the later value is missing. This requires assumptions about what the unobserved measurement would have been. If the outcome would have changed after the last observation, carrying the earlier value forward can misrepresent that trajectory. The registry does not report how many observations were carried forward, why measurements were missing, or sensitivity analyses using alternative missing-data methods.

The record does not specify the analysis population for each secondary outcome, or whether the same missing-data approach was applied to them. Participant flow and reasons for differences between enrollment and endpoint denominators are not reported in the registry record. Without these details, it is difficult to assess how missing outcomes or exclusions may have affected particular comparisons.

Other elements of the statistical plan are also not reported in the registry record, including interim analyses, covariate selection for ANCOVA, and a detailed prespecification of analyses. Their absence from the record does not show that they were or were not undertaken. It means that interpretation must remain limited to the methods and results the record actually reports.

16. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: coronary artery disease (3.6%), sudden cardiac death (2.0%), gout (2.0%), acute myocardial infarction (1.8%), atrial fibrillation (1.8%).

The registry reports serious adverse events for a risk set of 56 in the patiromer group and 49 in the placebo group. Serious adverse events affected 2 participants in each group. It separately reports other adverse events affecting 11 in the patiromer group and 1 in the placebo group. These are counts as reported; the registry summary does not provide a detailed event-by-event account or a basis for causal attribution.

The listed serious-event terms include coronary artery disease at 3.6%, sudden cardiac death at 2.0%, gout at 2.0%, acute myocardial infarction at 1.8%, and atrial fibrillation at 1.8%. The registry record does not specify the denominator or treatment-group breakdown for these term-level percentages, so they should not be assigned to a group or added together.

Deaths are not reported in the registry record. Safety counts describe events observed and recorded in this study; they do not by themselves establish whether an intervention caused an event or characterize risks beyond the study period. The record’s stated treatment period was 28 days, and the safety summary does not report longer-term follow-up.

17. What the Result Does, and Does Not, Mean

The posted data describe a randomized comparison of patiromer and placebo in participants with heart failure, focused on serum potassium over a 28-day treatment period. The primary group estimates differ in direction, and the recalculated unadjusted contrast is -0.44 mEq/L. The sponsor-reported ANCOVA test has p < 0.001. Together, these results indicate statistical evidence of a difference in the primary measure under the reported analyses.

That statistical result does not, on its own, say whether the difference is clinically important. The registry does not specify a threshold for a meaningful change in serum potassium, and the primary endpoint is a laboratory measure over a short period. The record also does not provide the adjusted ANCOVA estimate or its confidence interval, limiting assessment of the sponsor model’s effect magnitude and precision.

The secondary outcomes provide related but distinct information. Their directions and uncertainty vary, and the registry does not report numerical results for every named secondary outcome. The record also does not report a multiplicity strategy. Accordingly, secondary p-values should not be treated as if they were all confirmatory tests of a single prespecified claim.

The study’s results do not establish effects on outcomes that were not reported, nor do they describe long-term effects. The registry’s safety counts are descriptive and do not establish causation. The findings should be understood within the study’s population, measured outcomes, treatment period, analysis methods, and reporting detail. They are not a substitute for clinical judgment or individual medical advice.

18. Why This Trial Matters Statistically

This trial illustrates why statistical interpretation should begin with the outcome definition. Its primary measure is continuous change in serum potassium, while several secondary measures classify participants by threshold events or record medication adjustment. Those outcomes answer related but different questions. A mean change can capture the overall direction and magnitude of a measure, whereas a threshold proportion focuses on whether a boundary was crossed.

It also shows the importance of separating a sponsor-reported model from a transparent summary-statistic recalculation. The ANCOVA p-value is reported, but the adjusted estimate and model details are not. The unadjusted contrast offers a readable comparison from the posted means, yet does not replace the sponsor’s analysis. Clear labels prevent an unadjusted result from being mistaken for an adjusted one.

Precision and completeness matter alongside statistical significance. The primary recalculated interval lies below zero, but the registry does not state a clinically meaningful threshold. Secondary comparisons have varying uncertainty, and two named outcomes lack numerical results. The record also does not describe a multiplicity approach or fully characterize missing data. These gaps limit how much can be inferred from a set of p-values.

Finally, the planning illustrations demonstrate how sample-size requirements depend on the target effect and assumptions. They are not a report of the trial’s original design rationale or its observed power. Together, these features make the study useful for learning how endpoint type, analysis method, missing-data handling, and reporting completeness shape interpretation.

19. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The registry record leaves several trial-specific questions unresolved. The arm descriptions do not give dosing details, and the record does not describe treatment administration beyond the study title’s multiple-dose wording. It also does not report a clinically meaningful threshold for the primary serum-potassium change, making it difficult to connect the statistical estimate to an explicit clinical benchmark.

Although enrollment is listed as 120, the primary outcome group counts are 55 and 49. The record does not provide participant flow or explain the difference between enrollment and endpoint counts. The number of missing measurements, reasons for missingness, and use of LOCF in individual cases are not reported. The effect of alternative missing-data assumptions therefore cannot be assessed from the registry summary.

The sponsor reports an ANCOVA test for the primary outcome but not its adjusted estimate, confidence interval, covariates, or full model specification. The recalculated estimate is unadjusted and should not be read as the same model result. For the binary secondary outcomes, recalculated tests and sponsor-reported Fisher Exact tests have different p-values; the record does not provide enough detail to resolve the methodological differences.

The registry names five secondary outcomes but reports numerical results for three, and it does not report a multiplicity strategy. Details about interim analyses, original sample-size justification, analysis populations for secondary endpoints, and longer-term follow-up are not reported in the registry record. These limitations constrain interpretation of both the completeness and robustness of the posted findings.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Evaluation of Patiromer in Heart Failure Patients — NCT00868439.
• Registry record last updated 2021-06-02; status last verified 2021-05.
• Results first posted 2015-12-15.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

NCT00868439 is a completed phase 2 randomized, parallel-group, placebo-controlled study of patiromer in heart failure patients. The registry describes the study as multicenter, double-blind, and multiple-dose, and lists quadruple masking. Enrollment is 120.

The primary outcome was change from baseline in serum potassium at the end of the 28-day treatment period. Posted estimates were -0.21 mEq/L for patiromer and 0.23 mEq/L for placebo; the recalculated unadjusted mean difference was -0.44 mEq/L. The sponsor-reported ANCOVA p-value was < 0.001. The primary analysis used last observation carried forward.

Three secondary outcomes have numerical results in the registry record, while two named secondary outcomes do not. Serious adverse events affected 2 participants in each group; deaths are not reported in the registry record. Details needed to assess missing data, multiplicity, and the original sample-size rationale are not reported.