This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
ACURASYS was a phase 4, randomized, parallel-group, quadruple-masked trial in patients with ARDS. The study enrolled 340 participants and evaluated systematic early use of neuromuscular blocking agents, with placebo listed among the study interventions. The registered primary endpoint was reduction of the mortality rate of ARDS patients at d90.
| Feature | ACURASYS |
|---|---|
| Official title | Systematic Early Use of Neuromuscular Blocking Agents in ARDS Patients |
| Acronym | ACURASYS |
| ClinicalTrials.gov identifier | NCT00299650 |
| Phase | Phase 4 |
| Status | Completed |
| Condition | ARDS |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Quadruple |
| Primary purpose | Treatment |
| Enrollment | 340 |
| Number of arms | 2 |
| Lead sponsor | Assistance Publique Hopitaux De Marseille |
| Sponsor type | Other |
2. Clinical Question
The central clinical question was whether systematic early use of a neuromuscular blocking agent could reduce mortality among patients with ARDS compared with a placebo-controlled approach.
Population
Patients with ARDS.
Intervention
Systematic early use of neuromuscular blocking agents, with cisatracurium and cisatracurium besilate listed among the interventions.
Comparator
Placebo was listed as a study intervention.
Primary question
Does systematic early use of neuromuscular blocking agents reduce the mortality rate of ARDS patients at d90?
3. Trial Design
Neuromuscular blocking agent
- Cisatracurium was listed as a drug intervention.
- Cisatracurium besilate was also listed as a drug intervention.
- The study title describes systematic early use of neuromuscular blocking agents.
Placebo
- Placebo was listed as a drug intervention.
- The registry describes the overall design as randomized, parallel, and quadruple-masked.
4. Trial Timeline
Study start
The registry lists the study start date as 2006-03.
Primary completion
The registry lists the primary completion date as 2008-10.
Current registry status
The study status is listed as completed.
5. Endpoints
| Endpoint | Registry definition | Time frame |
|---|---|---|
| Primary endpoint | Reduction of the mortality rate of ARDS patients at d90 | 36 months |
The registry record summarized here contains one registered primary endpoint. Its wording specifies mortality at d90, while the registry time frame is listed as 36 months. Those are different concepts: d90 identifies the clinical time point in the endpoint description, whereas 36 months is the registry's stated endpoint time frame.
6. Statistical Methodology
The ClinicalTrials.gov record summarized here does not post a formal statistical analysis for the primary endpoint. The appropriate statistical framework can nevertheless be described from the structure of the endpoint.
Fixed-time mortality comparison
A mortality endpoint defined at d90 is naturally expressed as a binary outcome: alive at the specified assessment point or dead by that point. A primary comparison would typically estimate the mortality proportion in each randomized arm and quantify the between-arm difference with an appropriate confidence interval.
For a mortality endpoint, both absolute and relative measures can be informative. The risk difference describes the absolute separation in mortality proportions, while the risk ratio describes the mortality proportion in one group relative to the other.
Randomization as the basis for comparison
Because the trial is randomized, the primary treatment comparison should be anchored to randomized assignment rather than selectively comparing participants according to treatment actually received. Randomization is the design feature that supports a causal comparison between the assigned treatment strategies, subject to the usual assumptions and limitations of the trial.
Confidence intervals
A confidence interval around a mortality difference or ratio quantifies statistical uncertainty in the estimated treatment contrast. It is especially useful for showing the range of effect sizes compatible with the observed data and the specified statistical model.
Hypothesis testing
A conventional two-group mortality analysis could test a null hypothesis of no difference in d90 mortality. The corresponding p-value would address compatibility with that null hypothesis; it would not itself describe the magnitude or clinical importance of the treatment effect.
Time-to-event analysis as a distinct possibility
Because death occurs over time, investigators could also analyze time to death using survival methods such as Kaplan-Meier estimation and a Cox proportional-hazards model. That approach answers a somewhat different question from a fixed d90 mortality comparison: it uses the timing of deaths and censoring information rather than reducing the outcome to mortality status at one specified day.
7. Statistical Methods Explained
What does “mortality at d90” mean statistically?
It defines a specific assessment point. For each randomized participant, the primary outcome can be represented as whether death occurred by day 90. The resulting outcome is binary, allowing mortality proportions to be compared between the randomized groups.
Why report an absolute risk difference?
An absolute risk difference directly describes the separation between two mortality proportions. For example, a difference of 0.10 would represent a 10-percentage-point difference. This measure is often easier to translate into the absolute frequency of events than a relative measure alone.
What does a risk ratio tell us?
A risk ratio compares the mortality proportion in one group with the mortality proportion in the other. A value below 1 indicates a lower observed mortality proportion in the numerator group, whereas a value above 1 indicates a higher observed mortality proportion.
Why might Kaplan-Meier analysis also be useful?
A fixed d90 analysis treats the endpoint as mortality status at a particular time. Kaplan-Meier analysis instead retains information about when deaths occur and can accommodate right-censoring. It therefore addresses time to death rather than only the status at day 90.
Why does the p-value not measure treatment effect size?
A p-value measures the strength of evidence against a specified null hypothesis under the statistical model. It depends on both the estimated effect and the amount of information available. Two studies can therefore have similar effect estimates but different p-values, or different effect estimates with similar p-values.
Why does quadruple masking matter statistically?
Masking can reduce opportunities for knowledge of treatment assignment to influence treatment administration, outcome assessment, or other trial procedures. The registry identifies the study as quadruple-masked, but the record summarized here does not specify which four groups were masked.
8. Planned Analysis
The registered primary endpoint is reduction of the mortality rate of ARDS patients at d90, with a registry time frame of 36 months. For an endpoint of this type, a conventional analysis would compare d90 mortality between the randomized groups using an absolute risk difference and/or relative risk measure, together with a confidence interval and an appropriate hypothesis test.
If individual time-to-death data are available and the scientific question includes the timing of deaths rather than only mortality status at d90, Kaplan-Meier estimation and a time-to-event comparison could provide complementary information. A Cox proportional-hazards model could then express the relative event rate as a hazard ratio, provided the model's assumptions are appropriate.
The ClinicalTrials.gov record does not report posted statistical analyses or numerical efficacy results for the registered primary endpoint. No formal statistical analyses were posted to ClinicalTrials.gov.
9. Interpretation of the Registered Primary Endpoint
The primary endpoint is intended to assess whether mortality among patients with ARDS differs at d90 between the randomized treatment strategies. The most direct summary is therefore an between-group comparison of mortality proportions at that time point.
An absolute mortality difference answers how many more or fewer deaths occur per unit of patients at the specified time point. A relative measure such as a risk ratio answers how the mortality proportion compares proportionally between groups. Both perspectives can be useful because a relative reduction can correspond to very different absolute reductions depending on baseline mortality.
A point estimate alone does not show how precisely the trial estimates the treatment contrast. Confidence intervals provide information about statistical uncertainty and should accompany the treatment effect estimate whenever results are available.
If a time-to-event analysis were performed, a hazard ratio below 1 would indicate a lower estimated instantaneous rate of death in the treatment group under the fitted model. It would not mean that the same percentage reduction applied to every patient's individual probability of death by d90.
10. Analysis Population and Missing Data Considerations
The record identifies randomization and a total enrollment of 340 participants but does not report the analysis population used for the primary endpoint, participant disposition, losses to follow-up, withdrawals, or a missing-data or imputation strategy.
For a d90 mortality endpoint, completeness of vital-status information is particularly important. If some participants have unknown mortality status at the specified assessment point, the statistical treatment of those observations can affect the estimated mortality proportion and the uncertainty around the treatment comparison.
11. Randomization and Masking as Statistical Protections
Randomization
Randomized allocation helps balance measured and unmeasured prognostic factors in expectation and provides the foundation for comparing outcomes between assigned treatment groups.
Parallel design
Participants remain in their assigned treatment strategy rather than serving as their own control. The treatment comparison is therefore made between parallel randomized groups.
Quadruple masking
Quadruple masking reduces the potential for knowledge of treatment assignment to influence aspects of trial conduct or outcome assessment.
Placebo comparator
The registry lists placebo as an intervention, providing a comparator against which the active intervention strategy can be evaluated.
The record does not identify the specific individuals or roles included in the quadruple-masking designation. That detail should therefore not be inferred from the label alone.
12. Non-Inferiority, Crossover, Factorial, and Bayesian Features
The registry information summarized here does not identify a non-inferiority margin, crossover design, factorial design, or Bayesian analysis.
| Design topic | Registry information |
|---|---|
| Non-inferiority margin | Not reported |
| Crossover | Not reported |
| Factorial design | Not reported |
| Bayesian methods | Not reported |
| Interim analysis | Not reported |
| Multiplicity procedure | Not reported |
| Stratification factors | Not reported |
These omissions matter because each feature would change how the primary statistical evidence should be interpreted. In particular, non-inferiority requires comparison against a prespecified margin rather than simply testing whether a treatment difference differs from zero, while multiplicity procedures determine how multiple confirmatory tests affect type I error.
13. Statistical Interpretation of a Future Mortality Estimate
If a future registry update reported mortality proportions for the two randomized groups, the most transparent presentation would begin with the absolute risks and then give one or more relative measures.
These expressions describe the structure of a mortality comparison; they are not numerical results for ACURASYS.
A confidence interval would then indicate the precision of the estimated treatment contrast. The interpretation should distinguish the observed effect from the uncertainty around it and should not treat statistical significance as synonymous with clinical importance.
14. Why This Trial Matters Statistically
ACURASYS provides a useful clinical-trial statistics example because the registry combines a randomized two-arm parallel design with quadruple masking and a clearly defined fixed-time mortality endpoint. That structure makes it possible to distinguish several related but non-identical statistical questions.
| Concept | How it appears in ACURASYS |
|---|---|
| Randomization | Participants were randomly allocated in a 2-arm study. |
| Parallel-group comparison | The registered design model is parallel. |
| Masking | The registry identifies the study as quadruple-masked. |
| Binary outcome | Mortality at d90 can be represented as death versus survival at a specified time point. |
| Absolute effect | A mortality risk difference directly describes the absolute separation between groups. |
| Relative effect | A risk ratio can express mortality proportionally between randomized groups. |
| Time-to-event analysis | Timing of death can be analyzed separately from fixed-time mortality status. |
| Confidence intervals | Uncertainty should accompany any reported treatment-effect estimate. |
15. Important Limitations and Interpretation Issues
- No posted numerical results: the registry record summarized here does not contain statistical results for the primary endpoint.
- Primary analysis method not reported: the record does not specify the formal statistical test, effect measure, confidence-interval method, or regression model used for the primary endpoint.
- Analysis population not reported: the record does not identify whether the primary endpoint was analyzed in an intention-to-treat population, a per-protocol population, or another defined population.
- Missing-data strategy not reported: the registry information does not describe how incomplete d90 mortality information was handled.
- Allocation ratio not reported: the record gives total enrollment and number of arms but does not specify the proportion assigned to each arm.
- Additional design details not reported: the registry information summarized here does not specify stratification factors, interim analyses, multiplicity procedures, crossover, a non-inferiority margin, or Bayesian methods.
- Endpoint time-frame distinction: the primary endpoint specifies mortality at d90 while the registry time frame is listed as 36 months. These fields should not be interpreted as interchangeable.
16. What a Complete Statistical Result Would Need to Show
For the registered d90 mortality endpoint, a statistically informative results presentation would include the number of participants analyzed in each randomized group, the number or proportion who died by d90, the treatment contrast, its confidence interval, and the hypothesis-test result if one was prespecified.
Event counts
The number of deaths in each randomized group establishes the underlying event burden from which mortality proportions are calculated.
Absolute mortality
The mortality proportion in each group shows the absolute risk at the specified d90 assessment point.
Effect estimate
A risk difference or risk ratio summarizes the treatment contrast in a directly interpretable form.
Uncertainty
A confidence interval shows how precisely the treatment contrast has been estimated.
These components should be interpreted together. A relative effect without the underlying event rates can obscure the absolute scale of the outcome, while event rates without an uncertainty interval can make a statistically imprecise estimate appear more definitive than it is.
17. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The registered primary endpoint defines a comparison of mortality at a fixed clinical time point. The randomized, parallel design provides the framework for estimating a treatment contrast, while the appropriate effect measure and uncertainty method would depend on the prespecified analysis.
Clinical interpretation
The clinical question concerns whether systematic early use of a neuromuscular blocking agent changes mortality among patients with ARDS. The registry information establishes the question and design but does not provide a numerical outcome estimate.
18. Record Summary
ACURASYS was a completed phase 4, randomized, parallel-group, quadruple-masked trial enrolling 340 patients with ARDS. The registry identifies a 2-arm design and lists cisatracurium, placebo, and cisatracurium besilate among the interventions. Its registered primary endpoint was reduction of the mortality rate of ARDS patients at d90, with a registry time frame of 36 months.
Statistically, the central feature of ACURASYS is the distinction between a fixed-time mortality endpoint and a time-to-event analysis. The former focuses on the mortality proportion at d90 and is naturally summarized with absolute and relative risk measures; the latter uses the timing of deaths and censoring and can be summarized with Kaplan-Meier estimates and hazard ratios. These approaches answer related but different questions.
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19. Sources
- ClinicalTrials.gov: NCT00299650 — ACURASYS.
- PubMed: PubMed record 27894328.
- PubMed: PubMed record 20843245.