Study to Evaluate the Efficacy & Safety of the INTERCEPT Blood System for RBCs in Complex Cardiac Surgery Patients: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Study to Evaluate the Efficacy & Safety of the INTERCEPT Blood System for RBCs in Complex Cardiac Surgery Patients (NCT03459287), a randomized phase 3 study. The primary outcome is Percentage of Patients Who Have Received at Least One Study Transfusion With a Diagnosis of Renal Impairment Defined as:.

2. Clinical Question

3. Trial Design

The registry describes a randomized, double-blinded, controlled, parallel-group Phase III study. Its official title identifies the design as non-inferiority. The sponsor was Cerus Corporation, and the status is completed. The registry lists enrollment as 581. Study start is recorded as 2018-12-05, with primary completion on 2023-12-04. These are registry milestones; they do not, by themselves, describe when every participant was enrolled or followed.

The two interventions were INTERCEPT-treated red blood cells and conventional red blood cells. The randomized allocation and parallel-group model mean participants were assigned to one of the treatment groups and followed within that group, rather than receiving both interventions in a crossover sequence. The registry specifies quadruple masking. It does not identify in the available record which particular roles were masked, so the exact operational meaning of that label cannot be expanded upon.

The primary outcome was a participant count for renal impairment within 48±4 hours after the end of surgery. The primary analysis population is described as participants who received at least one study transfusion and had outcome data available. The posted primary outcome includes 46.0 participants with the event among 157.0 in the INTERCEPT group and 45.0 among 161.0 in the conventional group. These outcome-specific denominators should not be confused with overall enrollment; the registry does not explain in this record why they differ.

The trial title and registry design identify a non-inferiority aim, but the margin, the original sample-size assumptions, the planned analysis model, and the handling of protocol deviations are not reported in the registry record. Consequently, the reported group counts and recalculated unadjusted comparisons can describe the observed data, but they cannot substitute for a prespecified non-inferiority analysis.

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: 636 subjects consented, 16 subjects did not meet inclusion criteria; 28 subjects met exclusion criteria; 11 other (surgery cancelled, subject chose not to be randomized), 581 subjects enrolled.

Overall Study

Analysis population for the primary outcome, as defined in the registry: Participants who received at least one study transfusion with available data on the outcome.

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 Patients Who Have Received at Least One Study Transfusion With a Diagnosis of Renal Impairment Defined as:, measured over Within 48±4 hours of the end of surgery.

The primary endpoint counts participants diagnosed with renal impairment after receiving at least one study red blood cell transfusion. The registry defines the event as any post-transfusion serum creatinine level at least 0.3 mg/dL (or 26.5 µmol/L) above the pre-surgery baseline, with the rise occurring within 48±4 hours of the end of surgery. The eligible population is participants with at least one study transfusion and available outcome data.

This endpoint captures a laboratory-defined change over a specified postoperative period. In general, serum creatinine is used as a marker related to kidney function, and a threshold-based outcome gives a clear rule for classifying whether a participant met the endpoint. The rule does not describe every aspect of kidney injury, the duration of any change, or its later course. The record also lists a secondary outcome concerning diagnosis of Stage I, II or III acute kidney injury, which is a different stated outcome from the primary creatinine-change definition.

The primary outcome is reported as a count of participants rather than a continuous measurement of creatinine change. That format supports a comparison of the proportion meeting the specified event definition in each group. The registry record does not report the timing of individual measurements beyond the stated window, the amount of change for participants who met the threshold, or additional detail about adjudication of the outcome.

7. Results Posted in the Registry

• INTERCEPT (Test): 29.3% (46/157) versus 28.0% (45/161) on control; risk difference 1.3 percentage points (95% CI -8.6 to 11.3), risk ratio 1.05 (0.74–1.48); unadjusted two-proportion z-test p = 0.790.

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.

Mortality or the Need for RRT

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • INTERCEPT (Test): 10.1% (16/159) versus 4.9% (8/162) on control; risk difference 5.1 percentage points (95% CI -0.6 to 10.9), risk ratio 2.04 (0.90–4.63); unadjusted two-proportion z-test p = 0.081.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between INTERCEPT (Test) and the control. The interval gives the range of effects that remain compatible with the data.

The registry’s secondary outcomes cover additional aspects of postoperative kidney injury and a combined outcome of mortality or need for renal replacement therapy. The latter has posted group counts and an unadjusted comparison. Results for the listed acute kidney injury staging outcome are not reported in the registry record. Thus, the available secondary results do not provide a complete picture of the registry’s stated secondary-outcome set.

The posted mortality-or-renal-replacement-therapy comparison has more recorded events in the INTERCEPT group than in the conventional group, but its unadjusted interval crosses no difference and its p-value is 0.081. This is an uncertain pattern rather than a definitive separation between groups. It does not overturn or confirm the primary endpoint result: the outcomes differ in definition and observation period, and both comparisons have uncertainty.

Secondary outcomes generally carry less confirmatory weight than a prespecified primary endpoint. They may be exploratory, may be one of several outcomes examined, and may have less statistical precision. When multiple outcomes are tested, the chance of obtaining at least one apparently noteworthy result can increase unless the analysis accounts for multiplicity. The registry record does not report a multiplicity adjustment or a hierarchy for interpreting these outcomes. Accordingly, the secondary comparison should be viewed as supporting context, not as a standalone confirmatory conclusion.

The registry lists ten outcome measures and does not report secondary analyses in the available analysis listing. That does not show that investigators did not conduct analyses; it means that the corresponding analysis details are not reported in the registry record. Interpretation should distinguish the posted endpoint summaries from a prespecified, fully documented analysis plan.

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

• Percentage of Patients With a Diagnosis of Stage I, II or III Acute Kidney Injury
• Mortality or the Need for RRT

9. Statistical Interpretation

10. Statistical Methods Explained

What does a risk difference describe?

For a binary outcome, each participant is classified as having or not having the specified event. A risk difference compares the event proportions between two groups by subtracting one group’s proportion from the other. Its scale is an absolute difference in event probability. A value of zero represents no difference in the proportions; a positive or negative value indicates the direction according to the subtraction order. The posted comparison is for INTERCEPT relative to conventional red blood cells, and its recalculated unadjusted estimate is positive.

The endpoint counts and denominators are the ingredients for such a comparison. They are not interchangeable with the total registry enrollment. The primary endpoint population includes participants with at least one study transfusion and available outcome data. The registry record does not give a detailed account of why some enrolled participants are absent from those outcome-specific denominators or how any unavailable measurements were handled.

What does an unadjusted comparison mean?

An unadjusted comparison uses the observed group summaries without accounting for other participant characteristics or covariates. It is useful as a direct description of the posted counts, but it may not be the same as the trial’s planned primary analysis. The registry record does not report the model, covariate adjustment, stratification factors, or other details for the sponsor’s primary analysis. The recalculated risk differences and p-values should therefore be read as unadjusted summaries, not as a reconstruction of an unreported model.

A p-value describes how compatible the observed data are with a specified null hypothesis under the assumptions of the test. It is not the probability that the null hypothesis is true, and it does not measure the clinical importance of an effect. A larger p-value does not demonstrate equivalence or non-inferiority. In this trial, the unadjusted primary p-value is 0.79. The registry’s non-inferiority label does not make that ordinary unadjusted p-value a substitute for comparison with a prespecified non-inferiority margin.

Why use confidence intervals as well as p-values?

A confidence interval expresses a range of effect values compatible with the data under the method used. It communicates both the estimated direction and the precision of the comparison. For the primary risk difference, the recalculated interval includes zero, which is consistent with no difference as well as with effects in either direction within the interval. This is a statement about uncertainty, not proof that all values in the range are equally plausible or that the groups are equivalent.

For non-inferiority, the interval must be interpreted relative to a prespecified margin and in the appropriate direction. If the interval is not assessed against that margin, the usual question of whether it crosses zero does not answer the non-inferiority question. The margin and formal decision procedure are not reported in the registry record, so the posted unadjusted interval cannot support a complete non-inferiority determination.

What do relative measures add?

Risk ratios and odds ratios express comparisons on relative rather than absolute scales. A ratio of one corresponds to no relative difference. The recalculated relative-measure intervals for the primary comparison include one, which likewise indicates that the data are compatible with no relative difference under those calculations. Relative and absolute measures answer different descriptive questions; neither removes the uncertainty in the observed data. Because the more detailed interval limits include more than three decimal places in the record, they are not reproduced.

What is the role of endpoint definition?

Binary endpoints depend on a classification rule: the measurement, threshold, timing, and analysis population all matter. Here, the primary definition uses a serum creatinine rise from the pre-surgery baseline after a study transfusion, within the stated postoperative window. A participant who meets that definition contributes to the event count; the count does not retain the magnitude of the rise. General statistical interpretation should therefore keep the binary threshold outcome distinct from continuous creatinine values or from the separately listed acute kidney injury staging outcome.

The registry identifies the population with available primary outcome data, but it does not report how missing outcome data were treated analytically, whether any sensitivity analyses were performed, or whether the outcome was centrally adjudicated. Those omissions limit assessment of how robust the comparison is to choices about unavailable measurements or outcome classification.

Why does the non-inferiority label require care?

In general, a non-inferiority design asks whether the intervention is not worse than the comparator by more than a defined margin. The margin must be stated in advance and interpreted in relation to the outcome scale and direction. The study title identifies a non-inferiority design, but the registry record does not report the margin, the sample-size justification, the analysis population required for the primary non-inferiority test, or the decision rule. These design details should not be inferred from the observed counts or from a conventional test for a difference.

The registry also does not report a sponsor analysis among the listed analyses for the secondary outcomes. The unadjusted calculations are therefore descriptive recalculations from posted summary statistics, rather than evidence about the exact statistical procedure used by the trial investigators. They help quantify the posted group comparison but do not fill the gaps in the registered analysis details.

11. Confidence Intervals and Precision

The primary comparison has uncertainty that is important to retain when interpreting the observed counts. The recalculated risk-difference interval crosses zero and includes values on both sides of no difference. It is therefore compatible with the possibility that the event proportion was lower in the INTERCEPT group, with no difference, or with the possibility that it was higher, within the bounds of the interval. This range is too broad to support a precise claim about the direction or size of the underlying difference.

The same caution applies to the relative measures: their recalculated intervals include one. An interval that includes the no-effect value does not prove there is no effect. It indicates that the posted data, analyzed with the stated unadjusted approach, do not pin down a relative difference with high precision. The p-value of 0.79 is consistent with this uncertainty but does not replace the interval or express the probability that the groups are equal.

The secondary mortality-or-renal-replacement-therapy comparison is also imprecise. Its risk-difference interval crosses zero, and its relative-measure interval includes one. The observed counts differ, but the data do not sharply distinguish a true group difference from no difference. The registry does not provide a prespecified non-inferiority margin against which the primary interval can be judged, so the precision needed for that specific design question cannot be evaluated from the posted record alone.

Confidence intervals describe statistical uncertainty under their calculation assumptions; they do not account automatically for missing data, outcome misclassification, departures from the planned analysis, or multiplicity. Those considerations matter because the registry record does not report several related analysis details. Precision should therefore be considered together with the study’s endpoint definition and the incompleteness of the statistical reporting.

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 figures are planning illustrations, not observed enrollment requirements or evidence about the trial’s original design. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The target effect is set to the stated fraction of the observed effect. The trial’s own original design assumptions and sample-size justification are not reported in the registry record.

For these binary outcomes, the listed figures mean participants per arm. They are not total event counts. The planning values for the primary comparison are 17,622 participants per arm for 80% power and 23,590 participants per arm for 90% power when planning for 100% of the observed effect. For 75% of the observed effect, the corresponding values are 31,217 and 41,791 participants per arm. For 50% of the observed effect, they are 69,988 and 93,694 participants per arm.

The 80% power column represents a planning scenario in which the calculation is designed to have an 80% chance of detecting the specified effect under the stated assumptions. The 90% column uses a higher planned chance of detection and therefore requires more participants in the listed scenarios. Smaller target effects require more participants because they are harder to distinguish from random variation. The values are illustrative projections based on the observed effect, not observed power for this completed trial.

The secondary planning figures use the same assumptions and are also participants per arm. They are 414 and 554 for 100% of its observed effect, 678 and 907 for 75%, and 1,394 and 1,865 for 50%, for the 80% and 90% planning levels respectively. These projections are especially sensitive to the observed event pattern and should not be treated as recommended future study sizes without a prespecified design and justified target effect.

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 = 0.280; treatment proportion p₁ = 0.293; average p̄ = 0.286.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6392 = 1.2529.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6392 = 0.5379.
4. n = (1.2529 + 0.5379)² / (0.0135)² = 17,621.4, rounded up to 17,622 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 <- 0.279503105590062                          # control proportion
p1 <- 0.292993630573248                          # 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(3459287)
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 = 17,622 (80% power), 23,590 (90% power)
 75% of observed effect: n per arm = 31,217 (80% power), 41,791 (90% power)
 50% of observed effect: n per arm = 69,988 (80% power), 93,694 (90% power)
power.prop.test: 17621.4 
Simulated power at n = 17,622 per arm: 0.797 (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. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study evaluates multiple endpoints, comparisons, time points, or analysis populations. In general, examining many results can increase the chance that at least one appears statistically notable by chance. Prespecified endpoint hierarchy, adjustment procedures, or clear designation of exploratory outcomes can help readers understand how to interpret a collection of tests. The registry record does not report whether multiplicity was addressed, whether a formal testing hierarchy was used, or how the listed secondary outcomes were intended to support the primary question.

The registry lists ten outcome measures, while the posted secondary outcomes include kidney injury staging and mortality or need for renal replacement therapy. A result for acute kidney injury staging is not reported in the registry record. The available listing also does not supply an analysis plan for the secondary outcomes. Without that information, the unadjusted secondary p-value should not be treated as if it were a multiplicity-controlled confirmatory result.

Missing-data handling is another uncertainty. The primary population is explicitly defined as participants who received at least one study transfusion and had available outcome data. The record does not report the number or reasons for unavailable primary outcomes, the method used to address them, or sensitivity analyses exploring alternative assumptions. The outcome-specific denominators differ between groups, but that observation alone does not explain why or establish the impact of unavailable data.

Analysis populations are also important in a non-inferiority study. General statistical practice often examines carefully defined populations because deviations from assigned treatment or incomplete follow-up may affect interpretation. The registry record does not report whether the primary analysis used an intention-to-treat population, a per-protocol population, or both, nor how protocol deviations were handled. It also does not report an adjusted primary analysis, interim analyses, or the sponsor’s formal analysis details. These omissions limit independent assessment of the trial’s intended confirmatory procedure.

15. 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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: acute kidney injury (9.4%), respiratory failure (7.4%), atrial fibrillation (5.0%), multiple organ dysfunction syndrome (4.4%), hypotension (3.8%).

The serious adverse event summaries list 66 affected participants among 159 at risk in the INTERCEPT group and 57 among 162 at risk in the conventional group. Deaths are listed as 13 and 10, respectively. These are reported counts, not a causal comparison or a complete safety analysis. No risk differences or uncertainty intervals for these safety summaries are reported in the registry record.

The most frequently listed serious adverse event terms include acute kidney injury at 9.4%, respiratory failure at 7.4%, atrial fibrillation at 5.0%, multiple organ dysfunction syndrome at 4.4%, hypotension at 3.8%, and hepatobiliary disorders at 3.1%. The registry record does not provide enough information to infer event relatedness to treatment or compare each listed term between groups. A term’s frequency does not establish its cause.

Serious adverse events and the primary renal-impairment endpoint are different summaries with different definitions and denominators. The safety counts should be interpreted alongside the available event definitions and limitations, rather than taken alone as a judgment about the overall balance of outcomes.

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

The posted primary outcome describes renal impairment under a specific laboratory threshold and postoperative timing rule among participants who received at least one study transfusion and had available data. The event counts are similar in the two groups, and the unadjusted comparison is uncertain. This is a description of the registry results, not a conclusion that the treatments are identical or that a particular treatment effect has been established.

Although the study is identified as non-inferiority, the registry record does not report the margin needed to interpret that design’s central decision. The ordinary unadjusted risk difference, its interval, and its p-value cannot establish non-inferiority without the margin and the planned analysis procedure. A formal conclusion would require those design details and the relevant trial analysis, which are not reported in the registry record.

The secondary outcome of mortality or need for renal replacement therapy shows an uncertain observed pattern, and the result for the listed kidney-injury staging outcome is not reported in the registry record. Serious adverse event counts and listed event terms add safety context, but they do not provide a complete causal or comparative assessment. The available summaries also do not resolve the effects of unavailable outcome data, multiplicity, or the analysis population used by investigators.

In general, a trial result applies most directly to its defined participants, interventions, comparator, outcome, and follow-up period. The primary endpoint concerns a short postoperative measurement window; it does not describe longer-term kidney function or all possible consequences of transfusion. The registry’s complex cardiac surgery population and specified red blood cell products define the setting for the posted comparison. Extrapolation to other populations, products, settings, or outcomes is not established by these results.

The appropriate reading is therefore bounded: the registry reports the observed event counts and unadjusted comparisons, together with a non-inferiority design label, but important details required for a full confirmatory interpretation are not reported. These data can inform statistical understanding of the recorded outcomes without supporting medical advice or a broad claim about treatment efficacy or safety.

17. Why This Trial Matters Statistically

This trial is statistically instructive because its title identifies a non-inferiority question while the posted result most readily visible in the registry is a conventional comparison of binary event counts. Those are related but not interchangeable tasks. A test for a difference and an assessment against a non-inferiority margin answer different questions, so readers need the margin, analysis plan, and relevant confidence-interval interpretation to evaluate the stated design fully.

The renal-impairment endpoint also shows how a binary outcome compresses information. Participants are classified according to whether they meet a stated creatinine-change threshold within a stated window. That makes the outcome interpretable as an event count, while leaving out the magnitude and subsequent course of creatinine changes. The separate staging outcome could add a different perspective, but its result is not reported in the registry record.

Finally, the example highlights why precision, multiplicity, and population definitions matter. The primary and secondary unadjusted comparisons have uncertainty, secondary analyses have less confirmatory weight when their multiplicity handling is unreported, and the primary outcome population is defined by transfusion receipt and data availability. Understanding those features helps prevent a simple comparison of counts or p-values from being mistaken for a complete trial conclusion.

18. 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.

Several trial-specific limitations of interpretation arise from the registry record. The primary outcome is available only for participants with at least one study transfusion and outcome data. The record does not explain the difference between overall enrollment and the outcome-specific denominators, give reasons for unavailable data, or report missing-data sensitivity analyses. This leaves uncertainty about how fully the posted population represents all enrolled participants.

The registry identifies the trial as non-inferiority but does not report the margin, original sample-size justification, primary analysis model, or formal decision rule. The available unadjusted recalculations therefore cannot reproduce or verify the intended non-inferiority analysis. The analysis population, handling of protocol deviations, and any planned adjusted analyses are also not reported in the registry record.

There are additional limits around the outcomes. The primary endpoint is a threshold-based creatinine measure within a short postoperative window, not a report of longer-term kidney outcomes. The result for the listed acute kidney injury staging outcome is not reported. The available secondary comparison is unadjusted, and the registry record does not describe multiplicity procedures or a confirmatory hierarchy.

The safety summaries give counts and selected serious adverse event terms, but the registry record does not provide full comparative uncertainty estimates or the event-level context needed to assess timing and relatedness. The record also does not specify which roles were masked despite listing quadruple masking. These gaps do not negate the posted results; they define what can and cannot be concluded from the available registry information.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Study to Evaluate the Efficacy & Safety of the INTERCEPT Blood System for RBCs in Complex Cardiac Surgery Patients — NCT03459287.
• Registry record last updated 2026-09-22; status last verified 2026-09.
• Results first posted 2025-09-10.
• 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.

22. Record Summary

The registry describes a completed randomized, double-blinded, controlled, parallel-group Phase III non-inferiority study sponsored by Cerus Corporation. It compares INTERCEPT-treated red blood cells with conventional red blood cells in patients undergoing complex cardiac surgery. Enrollment is listed as 581.

The primary endpoint is renal impairment defined by a serum creatinine rise after study transfusion within 48±4 hours after surgery. The posted counts are 46.0 of 157.0 in the INTERCEPT group and 45.0 of 161.0 in the conventional group. The unadjusted comparison is imprecise and does not itself establish non-inferiority.

A secondary mortality-or-renal-replacement-therapy outcome is posted, while results for the listed acute kidney injury staging outcome are not reported in the registry record. The non-inferiority margin, detailed analysis plan, missing-data handling, and multiplicity procedures are also not reported.