Efficacy, Safety and Tolerability of Serelaxin When Added to Standard Therapy in AHF: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Efficacy, Safety and Tolerability of Serelaxin When Added to Standard Therapy in AHF (NCT01870778), a randomized phase 3 study. The primary outcome is Percentage of Participants With Confirmed Cardiovascular (CV) Death Through Day 180.

2. Clinical Question

3. Trial Design

This was a randomized, parallel-group, multicenter, double-blind, placebo-controlled phase III study of serelaxin added to standard therapy for acute heart failure. The registry lists quadruple masking. The study was sponsored by Novartis Pharmaceuticals, had planned enrollment of 6600, and is recorded as completed. The registry lists a study start of 2013-10-02 and primary completion of 2017-01-23.

Participants were assigned to continuous intravenous serelaxin at 30 ug/kg/day for 48 hours or matching placebo for 48 hours. The groups were analyzed separately for the posted outcomes. The primary analysis population was the Full Analysis Set, defined in the registry as all randomized participants who were not mis-randomized or excluded due to GCP reasons. The posted primary outcome includes 3274 participants assigned to serelaxin and 3271 assigned to placebo.

The primary outcome was the percentage of participants with confirmed cardiovascular death through day 180. The registry describes this outcome as adjudicated cardiovascular death through day 180. Secondary outcomes covered all-cause death, hospital stay, cardiovascular death or rehospitalization, intensive or coronary care stay, improvement in congestive signs and symptoms, and changes in biomarkers. The registry lists 10 outcome measures and 20 analyses.

The registry record does not report the trial's original sample-size justification, the assumptions used for its design, or details of interim analyses. It also does not report a full statistical analysis plan in the material available for these results. Those items cannot be inferred from the final enrollment or from the recalculated planning illustrations.

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: 6600 participants were randomized into the trial. Only 6545 participants were eligible for analysis. Therefore, the participants flow and baseline characteristics are based on 6545 participants.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The Full Analysis Set, which included all randomized participants who were not mis-randomized or excluded due to GCP reasons, was analyzed.

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.

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Participants With Confirmed Cardiovascular (CV) Death Through Day 180, measured over 180 days.

The primary endpoint measures the percentage of participants who experienced adjudicated cardiovascular death through day 180. It is a binary participant-level outcome: each participant is classified according to whether the specified event occurred during the stated follow-up period. The registry identifies the outcome as confirmed cardiovascular death and says that adjudication was used.

A percentage expresses how common the event was in each randomized group over the specified period. Comparing the groups addresses whether the observed event occurrence differed between participants assigned to serelaxin and participants assigned to placebo. A risk difference presents that comparison as the serelaxin-group event proportion minus the placebo-group event proportion. A negative value indicates a lower observed proportion in the serelaxin group; it does not by itself establish that the groups differ beyond chance variation.

Cardiovascular death is a clinically consequential outcome and is the primary endpoint named in the registry. The stated endpoint and follow-up period define what the result can speak to: this comparison concerns adjudicated cardiovascular death through day 180, not every possible outcome or later follow-up. The registry record does not report additional endpoint details beyond its description of adjudication and the Full Analysis Set used for analysis.

7. Results Posted in the Registry

• Serelaxin (RLX030): 8.7% (285/3274) versus 8.9% (291/3271) on control; risk difference -0.2 percentage points (95% CI -1.6 to 1.2), risk ratio 0.98 (0.84–1.14); unadjusted two-proportion z-test p = 0.785.

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.

Percentage of Participants With All-cause Death Through Day 180

• 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:
  • Serelaxin (RLX030): 11.2% (367/3274) versus 11.9% (389/3271) on control; risk difference -0.7 percentage points (95% CI -2.2 to 0.9), risk ratio 0.94 (0.82–1.08); unadjusted two-proportion z-test p = 0.387.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Serelaxin (RLX030) and the control. The interval gives the range of effects that remain compatible with the data.

Length of Total Hospital Stay (LOS) During the Index Acute Heart Failure (AHF) Hospitalization

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Serelaxin (RLX030): mean 9.36 versus 9.54 on control; difference -0.18 (95% CI -0.64 to 0.28), standardized effect d = -0.02; z-test p = 0.437.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Serelaxin (RLX030) and the control. The interval gives the range of effects that remain compatible with the data.

Percentage of Participants With First Occurrence of Adjudicated CV Death or Adjudicated Re-hospitalization

• 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:
  • Serelaxin (RLX030): 24.3% (796/3274) versus 24.9% (814/3271) on control; risk difference -0.6 percentage points (95% CI -2.7 to 1.5), risk ratio 0.98 (0.90–1.06); unadjusted two-proportion z-test p = 0.591.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Serelaxin (RLX030) and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes broaden the view beyond adjudicated cardiovascular death. They include all-cause death, length of the index hospitalization, the combined outcome of cardiovascular death or rehospitalization, intensive or coronary care stay, symptom improvement, and biomarker changes. The posted secondary results include analyses of all-cause death, total hospital stay, and cardiovascular death or rehospitalization. Their overall pattern does not materially alter the primary interpretation: the posted comparisons do not establish a clear separation between serelaxin and placebo.

These outcomes are related to the clinical course of acute heart failure, but they measure different events and are not interchangeable with the primary endpoint. A composite outcome combines event types, while length of stay and biomarker changes capture other aspects of the study. A difference, or absence of a clear difference, in one outcome does not determine the result for another.

The registry lists 10 outcome measures and 20 analyses. Testing multiple outcomes increases the opportunity for chance findings when each result is assessed independently. The registry record does not report a multiplicity adjustment or a prespecified hierarchy for interpreting secondary outcomes. For that reason, unadjusted secondary results carry less confirmatory weight than a prespecified primary analysis. They can provide context and identify patterns for further study, but isolated secondary findings should be interpreted cautiously, particularly when several outcomes and analyses are considered together.

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

• Percentage of Participants With All-cause Death Through Day 180
• Length of Total Hospital Stay (LOS) During the Index Acute Heart Failure (AHF) Hospitalization
• Percentage of Participants With First Occurrence of Adjudicated CV Death or Adjudicated Re-hospitalization
• Length of Intensive Care Unit (ICU) and/or Coronary Care Unit (CCU) Stay for the Index AHF Hospitalization
• Percentage of Participants With First Improvement Since Baseline in Congestive Signs and Symptoms of Heart Failure
• Change From Baseline in hsTroponin T Biomarker
• Change From Baseline in NT-proBNP Biomarker
• Change From Baseline in Cystatin C Biomarker

9. Statistical Interpretation

10. Statistical Methods Explained

Binary outcome comparisons. The primary endpoint records whether each participant had adjudicated cardiovascular death through day 180. For a binary outcome, the observed proportion is the number of participants with the event divided by the number analyzed in that group. The posted group percentages are 8.7% for serelaxin and 8.9% for placebo. A risk difference subtracts the control-group proportion from the intervention-group proportion. The recalculated estimate is -0.002. A negative estimate indicates that the observed event proportion was lower in the serelaxin group in this unadjusted comparison.

A confidence interval for a risk difference expresses uncertainty about the underlying difference, subject to the calculation's assumptions. The 95% interval from -0.016 to 0.012 includes zero, the value corresponding to equal proportions. It also extends to either side of zero, so the data are compatible with differences in either direction. The interval is not a range containing 95% of individual outcomes, nor does it assign a 95% probability to a particular fixed effect after the data have been observed.

Hypothesis tests. A p-value describes how incompatible the observed data, or more extreme data, would be with a specified null model if that model and the test assumptions were correct. It is not the probability that the null hypothesis is true, and it does not quantify the size or clinical importance of a difference. The recalculated unadjusted p-value for the primary risk-difference comparison is 0.785. The registry-reported time-to-event analysis has p = 0.386. These values arise from different methods and should be read in the context of their respective effect estimates.

Time-to-event analysis. A log-rank test compares event-time patterns between groups while using information about when events occurred and accounting for follow-up time in the test. The registry reports a hazard ratio of 0.98, with a 95% confidence interval from 0.83 to 1.15. A hazard ratio compares the event rates over time as represented by the time-to-event model; a value of 1 corresponds to equal hazards. Its confidence interval includes 1. A hazard ratio is not a risk difference and is not simply the ratio of the two day-180 percentages.

Time-to-event methods can use information about timing and censoring, whereas a comparison of event proportions focuses on whether an event was recorded by a specified endpoint. The registry record does not report the detailed censoring rules or model diagnostics for the primary analysis. The reported log-rank result should therefore be described as the sponsor's posted time-to-event analysis, while the risk-difference result is an unadjusted recalculation from the posted summary statistics.

Secondary-outcome methods. The secondary binary outcomes are also expressed as participant percentages, and their recalculated comparisons use risk differences. The length-of-stay outcome is continuous and is presented as a mean and standard deviation in each group; its recalculated mean difference is -0.183, with a 95% confidence interval from -0.644 to 0.278. The sponsor's posted test for that outcome is a Wilcoxon rank sum test, which compares the distributions using ranks rather than relying on a comparison of means. Its reported p-value is 0.220. The recalculated mean comparison and the sponsor's rank-based analysis answer related but not identical statistical questions.

Over-dispersion and variability. For continuous outcomes, the standard deviation describes variation among participant observations; it is not the uncertainty in the estimated group mean. General statistical consideration: when data vary more than a model assumes, standard errors and confidence intervals based on that model can be too narrow. The length-of-stay standard deviations are large relative to the reported means, indicating substantial variability in the posted observations. The registry identifies the reported dispersion as standard deviation; it does not report whether a particular over-dispersion adjustment was used for that outcome.

Analysis populations and multiplicity. The primary analysis population excludes randomized participants who were mis-randomized or excluded due to GCP reasons, according to the registry definition. The record lists 10 outcome measures and 20 analyses. When many outcomes or tests are considered, the chance of at least one apparently unusual result can rise if each is interpreted independently. Adjustment methods can address this issue, but the registry record does not report whether or how multiplicity was handled. Accordingly, unadjusted secondary p-values should not be treated as stand-alone confirmatory evidence.

11. Confidence Intervals and Precision

The primary risk-difference estimate is -0.002, with a 95% confidence interval from -0.016 to 0.012. The interval crosses zero and includes estimates favoring either group. This means the posted data do not pin down the direction of the underlying difference with high precision. The group percentages themselves are close, but the interval is more informative than the point estimate alone because it represents uncertainty around that estimate.

The sponsor-reported time-to-event estimate is a hazard ratio of 0.98, with a 95% confidence interval from 0.83 to 1.15. This interval includes 1, the no-difference reference for a hazard ratio, and allows for relative event rates on either side of that reference. Its interpretation concerns the hazard ratio, not the day-180 risk difference.

A confidence interval is not a guarantee that the true effect lies within its endpoints. Under the method's assumptions, the stated confidence level describes the long-run coverage of intervals constructed in the same way. Precision depends on the amount of information and the variability in the data, as well as the analysis method. The registry record does not provide all details needed to assess every modeling or follow-up assumption. The intervals support a cautious reading: the observed estimates are near their respective null values, but the plausible range is not limited to exactly no difference.

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 planning illustrations translate an assumed effect into an approximate sample requirement; they are not a report of the trial's original design calculation. For binary outcomes such as the primary endpoint, the entries are participants per arm. Time-to-event planning entries, when displayed, represent the total number of events, while other outcomes use participants per arm. The primary endpoint's planning figures are therefore interpreted as participants in each group, not as a combined total.

The assumptions listed for these illustrations are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. The registry record does not report the assumptions used for the original trial design, so these planning calculations should not be presented as that design rationale.

The 80% power column means the approximate number of participants per arm needed for an 80% chance of detecting the selected effect under the stated assumptions, if that effect is real and the model assumptions hold. The 90% power column uses a higher chance of detection and therefore requires a larger planned sample for the same assumed effect. Neither column is a statement about the probability that the observed result is true.

The rows for 100%, 75%, and 50% of the observed effect show how the assumed effect size changes the planning requirement. Smaller effects are harder to distinguish from random variation, so they require more participants. For the primary endpoint, planning for 100% of the observed effect gives 343,872 participants per arm for 80% power and 460,346 for 90% power; planning for 75% or 50% of the observed effect gives larger requirements. These are planning illustrations, not observed power. Observed power is not a useful replacement for interpreting the estimated effect and its confidence interval.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.089; treatment proportion p₁ = 0.087; average p̄ = 0.088.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.4007 = 0.7853.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.4007 = 0.3372.
4. n = (0.7853 + 0.3372)² / (-0.0019)² = 343,871.3, rounded up to 343,872 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
p0 <- 0.0889636196881688                          # control proportion
p1 <- 0.0870494807574832                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^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 (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(1870778)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 343,872 (80% power), 460,346 (90% power)
 75% of observed effect: n per arm = 612,829 (80% power), 820,404 (90% power)
 50% of observed effect: n per arm = 1,382,243 (80% power), 1,850,430 (90% power)
power.prop.test: 343871.3 
Simulated power at n = 343,872 per arm: 0.806 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-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

The registry reports the sponsor's primary analysis as a log-rank comparison with a hazard ratio of 0.98, a 95% confidence interval from 0.83 to 1.15, and p = 0.386. This is the posted time-to-event analysis and should be distinguished from the unadjusted recalculation based on the day-180 event percentages. That recalculation gives a risk difference of -0.002, a 95% confidence interval from -0.016 to 0.012, and p = 0.785.

The two analyses use different effect measures and methods: a hazard ratio from a time-to-event analysis versus a difference between event proportions. Their estimates and p-values need not match. Both confidence intervals include their respective null values, and neither result provides a clear statistical separation between the groups. The registry does not flag a discrepancy between the sponsor's analysis and the recalculation. The recalculation is a simple, unadjusted reading of the posted summaries; it does not replace the registry-reported analysis or incorporate information unavailable in those summaries.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists 10 outcome measures and 20 analyses. This creates a multiplicity issue: as the number of statistical comparisons grows, the chance of obtaining at least one small p-value by chance can increase when each test is considered separately. Statistical plans may address this through adjustment, prespecified testing sequences, or other approaches. Whether such a plan was used for this trial is not reported in the registry record. The posted secondary p-values therefore should not be interpreted as if they were necessarily protected against multiple testing.

The primary analysis population is described as the Full Analysis Set, including all randomized participants who were not mis-randomized or excluded due to GCP reasons. The registry does not report further details about how missing outcome data were handled, the amount of missingness, or whether sensitivity analyses evaluated its impact. These details matter because incomplete outcome ascertainment can affect estimates, especially if missingness differs between groups or relates to outcome status.

The record also does not report the original sample-size justification, interim analyses, or a complete analysis plan. The planning illustrations based on the observed effect are separate from the trial's original design assumptions. Without the missing details, it is not possible to determine from the registry record how the original design addressed multiplicity, missing data, or other analysis choices. These omissions limit the conclusions that can be drawn about the full prespecified statistical framework, but they do not change the basic description of the posted endpoint estimates and their uncertainty.

16. Safety Results

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

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0.5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: cardiac failure (2.1%), pneumonia (1.0%), acute kidney injury (0.8%), coronary artery disease (0.5%), ischaemic stroke (0.5%).

The registry's serious adverse event figures report 412 participants affected among 3257 at risk in the serelaxin group and 424 among 3248 at risk in the placebo group. The registry also lists other adverse events for 1336 participants among 3257 at risk with serelaxin and 1277 among 3248 at risk with placebo. These are descriptive group counts in the safety record, not the primary efficacy comparison.

The most frequently listed serious terms include cardiac failure at 2.1%, pneumonia at 1.0%, acute kidney injury at 0.8%, coronary artery disease at 0.5%, ischaemic stroke at 0.5%, and renal failure at 0.5%. The registry also reports deaths of 363 in the serelaxin group and 386 in the placebo group; the safety summary does not specify a time frame for these figures. They should not be conflated with the primary endpoint's adjudicated cardiovascular deaths through day 180.

Adverse-event counts describe events recorded in the study and do not by themselves establish causation or a difference in safety. The registry record does not provide additional detail about event severity definitions, exposure-adjusted rates, or the circumstances of individual events.

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

The primary result describes cardiovascular death through day 180 among the analyzed randomized participants. The observed percentages were 8.7% with serelaxin and 8.9% with placebo. The unadjusted risk-difference interval crosses zero, and the sponsor-reported hazard-ratio interval crosses 1. Taken together, the posted estimates do not establish a clear difference between the groups for this endpoint.

This does not mean that the two treatments are proven equivalent or that the underlying difference is exactly zero. A finding that does not establish a difference is not the same as a formal equivalence or non-inferiority conclusion, which would require a specified margin and an appropriate analysis. Such a margin is not reported in the registry record. The confidence intervals allow for uncertainty in both directions.

The result applies to the studied comparison: intravenous serelaxin added to standard therapy versus matching placebo added to standard therapy, in the enrolled acute heart failure population and for the stated endpoint and follow-up. It does not answer questions about other populations, other treatment regimens, other outcomes, or follow-up beyond the stated period. The registry record also does not provide enough detail to resolve all questions about missing data, multiplicity, or the original analysis plan.

These results are statistical summaries, not individual predictions or medical advice. They describe what was observed in the posted trial results and how uncertain the estimated group contrast is. They should be considered alongside the distinction between the registry's reported time-to-event analysis and the simple unadjusted recalculation, as well as the limits of registry-level reporting.

18. Why This Trial Matters Statistically

This trial illustrates why an endpoint percentage, an effect estimate, a confidence interval, and a p-value answer different questions. The group percentages describe observed event frequency. The risk difference quantifies the contrast on an absolute scale, while the hazard ratio summarizes a time-to-event comparison on a relative scale. The confidence intervals show the uncertainty around those estimates, and the p-values summarize compatibility with their respective null models.

The distinction matters because a small difference between percentages does not by itself show that the underlying groups differ, and a p-value alone does not express the range or practical scale of effects compatible with the data. In this study, both the recalculated risk-difference interval and the sponsor-reported hazard-ratio interval include their null values. Reading the estimates and intervals together gives a more complete account than focusing on either the point estimate or a test result alone.

The record also demonstrates why secondary findings and sample-size illustrations require context. Multiple outcomes and analyses can increase the likelihood of chance findings, while effect-based planning illustrations are not the same as the original trial design or observed power. Clear separation of sponsor-reported analyses, unadjusted recalculations, and planning assumptions helps prevent these different statistical quantities from being mistaken for one another.

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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The registry record does not report the original sample-size justification, detailed interim-analysis procedures, or the full statistical analysis plan. It also does not report whether multiplicity adjustments were used. These omissions make it difficult to evaluate the trial's complete prespecified approach to inference, particularly for secondary outcomes.

Details of missing outcome data, censoring rules for the primary analysis, and sensitivity analyses are not reported in the registry record. The primary population is defined, but that definition alone does not show how missingness or other exclusions affected the estimates. The posted summaries do not include subgroup results or enough participant-level information to assess variation in effects across different groups.

The recalculated binary comparisons use posted group summaries and are unadjusted; they do not reproduce the sponsor's time-to-event analysis. For the continuous length-of-stay outcome, the recalculated mean comparison differs from the sponsor's Wilcoxon rank sum test. Those methods answer related but distinct questions. The registry also reports the length-of-stay timeframe with participants still hospitalized at day 60 censored at day 60, which limits interpretation of that measure to its stated handling and period.

Finally, adverse-event summaries are descriptive and do not provide all details needed to determine causality, timing, or exposure-adjusted comparisons. The safety record's death counts are not explicitly linked to the primary endpoint's time frame. These registry-level limitations do not invalidate the posted results, but they restrict how specifically the results can be interpreted.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Efficacy, Safety and Tolerability of Serelaxin When Added to Standard Therapy in AHF — NCT01870778.
• Registry record last updated 2018-03-30; status last verified 2018-03.
• Results first posted 2018-03-30.
• 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

The registry describes a completed, randomized, parallel-group, quadruple-masked phase III study of intravenous serelaxin added to standard therapy versus matching placebo in acute heart failure. The primary endpoint was adjudicated cardiovascular death through day 180 in the Full Analysis Set.

The posted percentages were 8.7% with serelaxin and 8.9% with placebo. The unadjusted recalculated risk difference was -0.002, with a 95% confidence interval from -0.016 to 0.012. The registry-reported log-rank analysis gave a hazard ratio of 0.98, with a 95% confidence interval from 0.83 to 1.15. Both intervals include their respective null values, so the posted primary results do not establish a clear group difference.

Secondary outcomes offer related context, but multiplicity handling and several analysis details are not reported in the registry record. Planning figures based on observed effects are illustrations rather than the original design rationale or observed power.