Independent Predictors of Mortality in Polytrauma Patients: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Independent Predictors of Mortality in Polytrauma Patients (NCT01669577), a registered phase not stated study. The primary outcome is Death.

2. Clinical Question

The study was designed to look for factors associated with mortality among people with severe polytrauma. Its primary outcome was death within the first 30 days, and the registry describes prospective collection of blood-sample and clinical-feature data at several stages of care. The record does not report which factors were evaluated as predictors, the estimates from the analyses, or the number of deaths for the primary outcome. It therefore establishes the question and the intended observation period, but does not let readers assess which predictors were identified or how precisely their associations were estimated.

3. Trial Design

NCT01669577 is listed as a completed, prospective, observational, longitudinal study sponsored by the University of Sao Paulo. The registry gives an enrollment of 200 and lists Multiple Trauma as the condition. The study started in 2010-10, and primary completion is recorded as 2013-06. These dates describe the registered study timeline; the record does not provide details of recruitment dates for individual participants or follow-up completeness.

The study summary says that data were collected at the trauma scene, in the emergency room, and at three and 24 hours of hospital stay. It also says participants would be followed for 30 days after hospital admission. The primary endpoint timeframe is described as within the first 30 days, and the outcome description refers to mortality evaluation 30 days after trauma. The record gives these time references but does not explain any difference between them.

The listed intervention is analysis of blood samples and clinical features, classified as a procedure. This is data collection and analysis rather than an assigned therapeutic intervention. The arm description is for severe trauma patients; the registry does not identify a randomized treatment arm or a comparator group. Allocation is not stated, and the record specifically flags that the arms are not a like-for-like control comparison.

The primary measure is binary in the sense that it counts participants who experienced the event, death. The registry lists one outcome measure and five analyses, but does not provide the predictor set, model specifications, adjustment variables, or detailed analysis populations. The study's observational design can describe associations in the enrolled population, but by itself does not establish that an associated feature caused mortality.

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: Pre assignment were not performed, as patients were not known.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: severe polytrauma patients (ISS\>15)

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: severe polytrauma patients

6. Primary Endpoint

The registry lists the primary outcome as Death, measured over Within the first 30 days.

The primary endpoint is death among severe polytrauma patients (ISS>15), with a stated timeframe of within the first 30 days. The outcome is recorded as a number of participants. The registry description calls it a 30-day-after-trauma mortality evaluation, while the study summary describes follow-up for 30 days after hospital admission. The record does not clarify how those time origins were operationalized.

Mortality is a clinically consequential event and can be expressed as a participant count for a defined population and observation period. In a predictor study, the outcome serves as the event that candidate clinical features or blood-sample findings are evaluated against. The count alone, however, would not show whether a feature predicts the event: that requires the predictor-analysis results and their uncertainty. Neither the primary death count nor the estimates for candidate predictors are reported in the registry record.

The endpoint should not be confused with the adverse-event entries. The registry's primary outcome is death, whereas its adverse-event fields list affected participants and an event term. The registry does not report a death count in those primary-outcome fields. Thus, the adverse-event counts cannot be substituted for the primary mortality result.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The study is described as prospective and observational. In general, prospective data collection means that information is gathered according to a planned sequence as participants are observed, rather than relying solely on a retrospective search of existing records. Observational means that the study does not assign participants to competing treatments for the purpose of estimating a treatment contrast. These design labels matter because predictor associations in observational data are not automatically causal effects. The registry identifies analysis of blood samples and clinical features as the procedure and does not describe an assigned treatment comparison.

What does a binary outcome mean?

The primary measure is the number of participants who experienced death within the specified timeframe. The registry classifies the outcome as binary and describes its meaning as participants with an event, recorded as counts. In general, binary outcomes distinguish whether each participant did or did not experience an event during the defined period. A count records how many participants had the event; interpreting that count usually also requires knowing the relevant population and whether outcome status was ascertained for all participants. The registry does not report the primary death count or details of outcome ascertainment.

What would an independent-predictor analysis require?

In general, analysis of independent predictors involves relating candidate features to an outcome while accounting for other variables in a statistical model. The resulting estimates depend on the model form, predictor definitions, handling of continuous measurements, covariate selection, missing-data approach, and assumptions. The registry lists five analyses but does not report their specifications, the candidate predictors, or the variables included together in any model. It is therefore not possible to identify which model or estimates support the study's stated aim.

For a binary endpoint, analysts may use methods suited to event counts and participant-level outcome status. The particular method used in this study is not reported in the registry record. In general, when events are sparse relative to the number of candidate variables, estimates may be unstable or highly sensitive to modeling choices. Whether that issue applies cannot be determined without the number of deaths and model details.

How should uncertainty be read?

In general, an estimate describes the association calculated from the observed data, while a confidence interval conveys uncertainty about that estimate under the statistical model. A p-value addresses compatibility of the data with a specified null hypothesis and model assumptions; it does not describe the size or clinical importance of an association. The registry does not report estimates, confidence intervals, or p-values for the mortality analyses, so their magnitude and precision cannot be evaluated.

Over-dispersion is a general concern when variation in observed counts exceeds what a chosen statistical model expects. In general, ignoring over-dispersion can make standard errors too small and uncertainty appear narrower than it is. The listed primary outcome is a participant count for a binary event, but the record does not identify a model for which over-dispersion was assessed or report any such assessment. It would be inappropriate to claim that over-dispersion was present or handled in this study.

What do the planning assumptions mean?

The registry's sample-size notes describe planning illustrations computed from an observed effect, with a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation; the effect to detect is described as a stated fraction of the observed effect. The notes specify participants per arm for non-time-to-event illustrations and total events for time-to-event illustrations. These are planning assumptions, not evidence of the study's original sample-size justification. The actual illustration values are not reported in the registry information, and the observational record does not describe a randomized, equal-allocation comparison. No sample-size justification for this study is reported in the registry record. Such illustrations are not observed power and should not be read as a measure of the study's achieved ability to detect an association.

The sponsor's statistical plan, model diagnostics, handling of multiple analyses, and rules for choosing predictors are not reported in the registry record. General explanations of statistical methods cannot substitute for those missing study-specific details. Accordingly, the study's analytic approach and the reliability of any predictor claims cannot be independently assessed from the recorded information.

10. Exact Estimates for Each Arm

No two-arm comparison is shown for this trial, but each arm's result can still be summarized with exact interval estimates computed from the posted counts. The Clopper-Pearson interval is exact and conservative; the Wilson interval is a common closer-to-nominal alternative; the Bayesian credible interval uses a uniform Beta(1, 1) prior. For small groups the three can differ noticeably.

R code and output

arm    <- c("Severe Trauma Patients")
n      <- c(200)
events <- c(52)
for (i in seq_along(n)) {
  ci <- binom.test(events[i], n[i])$conf.int          # Clopper-Pearson exact interval
  cr <- qbeta(c(0.025, 0.975), 1 + events[i], 1 + n[i] - events[i])   # Bayesian, uniform Beta(1,1) prior
  wi <- (function(x, n) { p <- x / n; z <- qnorm(0.975); d <- 1 + z^2 / n
    c((p + z^2 / (2 * n) - z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d, (p + z^2 / (2 * n) + z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d) })(events[i], n[i])
  cat(sprintf("ARM|%s|%d|%d|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f\n", arm[i], n[i], events[i], 100 * events[i] / n[i],
              100 * ci[1], 100 * ci[2], 100 * wi[1], 100 * wi[2], 100 * cr[1], 100 * cr[2]))
}

ARM  Severe Trauma Patients  200  52  26.0  20.1  32.7  20.4  32.5  20.4  32.5

11. Multiplicity, Missing Data and Analysis Populations

The registry lists five analyses for one outcome measure. It does not state whether these analyses correspond to separate candidate predictors, alternative models, subgroup analyses, or another set of comparisons. It also does not report a prespecified primary predictor, an analysis hierarchy, or any adjustment for multiple testing. When many hypotheses are examined, the chance of obtaining an apparently noteworthy result by chance can increase; in general, multiplicity procedures or a clearly prespecified analysis plan can help readers interpret that risk. Whether such procedures were used in this study is not reported in the registry record.

Missing-data handling is also not described. The record does not say how incomplete blood-sample measurements, clinical features, outcome follow-up, or other unavailable information were treated. In general, excluding participants with incomplete data can change the analyzed population and may bias estimates if missingness is related to participant characteristics or outcomes. Other approaches rely on assumptions that should be described and assessed. No study-specific missing-data method or sensitivity analysis is reported.

The analysis populations are not defined beyond the primary endpoint's stated population of severe polytrauma patients (ISS>15). The registry does not report how many enrolled participants met that criterion, how many contributed to each analysis, or whether all participants were followed through the endpoint period. The enrollment figure of 200 is not, by itself, confirmation that all participants were included in every analysis. The record also does not explain how the population for the adverse-event entries relates to the primary-outcome population.

These omissions limit assessment of whether the reported analyses, if available elsewhere, represent a prespecified plan and whether their results might be affected by missingness or multiple testing. The registry record does not provide enough detail to determine these issues.

12. 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 0% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: thrombophlebitis (0.5%).

The registry lists adverse-event information for Severe Trauma Patients. The serious-event fields report 1 affected participant among 200 at risk; the other-event fields report 2 affected participants among 200 at risk. Thrombophlebitis appears among the top serious terms at 0.5%. These are the adverse-event entries recorded for the group and should be read separately from the primary mortality endpoint.

The fields describe participants affected and those at risk; they do not provide a complete narrative of each event. The registry does not report event onset, duration, recurrence, severity details beyond the serious classification, or whether investigators considered an event related to study procedures. The figures therefore do not establish causation or provide the information needed to compare safety across treatment strategies. The study is observational and has no reported like-for-like control comparison.

The primary endpoint concerns death within the first 30 days. The adverse-event figures are not a substitute for its unreported death count. Nor does the listed thrombophlebitis term establish how many distinct events occurred or how they were attributed beyond what the registry explicitly records. These entries are best understood as limited registry safety counts, not as a comprehensive account of clinical harms.

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

The registry record shows that investigators set out to study mortality predictors in severe polytrauma patients, planned prospective observation, and defined death within the first 30 days as the primary outcome. It also documents an enrollment of 200, collection of blood-sample and clinical-feature data, and five analyses. These details describe the study question and registered design.

The record does not report the primary death count, any predictor-specific association, uncertainty intervals, or the statistical models used. It consequently does not establish which features were associated with mortality, whether any association remained after adjustment, or how precise an estimate might be. The study's stated objective should not be mistaken for a finding.

In general, observational predictor studies can help describe patterns and generate or evaluate hypotheses about associations in a defined population. An association is not by itself proof that a feature causes an outcome. Differences between participants and unmeasured factors may contribute to observed relationships, and the interpretation of adjusted estimates depends on the variables measured and analytic choices. The registry does not provide the details needed to assess those issues for this study.

The safety entries report 1 serious-event-affected participant among 200 at risk and 2 affected participants for other events among 200 at risk, with thrombophlebitis listed at 0.5% among the top serious terms. These entries do not answer the primary mortality question and should not be treated as evidence about comparative treatment effects or causal safety risks.

Thus, the record documents a relevant observational research question and its planned follow-up, but leaves the main empirical conclusions unavailable. Any claim about the value of a particular predictor, the magnitude of mortality risk, or the study's precision would go beyond what the registry reports.

14. Why This Trial Matters Statistically

This study is statistically notable for asking a prediction question in a population described as having severe polytrauma, rather than testing an assigned treatment against a control. That distinction changes the interpretation: the main intended evidence concerns associations between measured clinical or blood-sample features and mortality, not randomized treatment effects.

The prospective, longitudinal description also makes timing central. Measurements were planned at the trauma scene, in the emergency room, and at three and 24 hours of hospital stay, with follow-up described as 30 days after hospital admission. The primary outcome is framed as death within the first 30 days, while its description refers to 30 days after trauma. Clear alignment of measurement times, prediction windows, and outcome follow-up is important in prediction research. The registry states the time references but does not explain their operational details.

The study's aim of identifying independent predictors raises questions about model construction, adjustment, and validation. In general, predictors selected and evaluated in the same dataset may appear more informative than they are in new participants, particularly when many candidate features are examined. External validation and transparent reporting of model performance can help assess whether findings generalize. The registry does not report whether validation was planned or performed, or how predictors were selected.

The record's listing of five analyses without their details also highlights why transparent reporting of multiplicity and analysis populations matters. Without results and methods, readers cannot judge whether findings were prespecified, how uncertain they were, or how robust they might be. The study's statistical importance therefore lies in its clearly stated observational mortality question, while the registry's limited analytic reporting prevents evaluation of the resulting evidence.

15. 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.

A principal limitation of the registry information is that it does not report the primary mortality result. The outcome is defined, but the number of deaths among the relevant participants is not available in the registry record. Predictor estimates, confidence intervals, p-values, and model descriptions are also not reported, preventing assessment of both effect size and precision.

The population description is limited. The primary outcome refers to severe polytrauma patients with ISS>15, but the record does not provide participant characteristics, the number meeting that threshold, or counts analyzed for each predictor. The registered enrollment of 200 does not establish that all enrolled participants had complete measurements or outcome follow-up.

The timing language is not fully reconciled: the primary timeframe is within the first 30 days, the outcome description refers to 30 days after trauma, and the summary describes follow-up for 30 days after hospital admission. The registry does not explain how these time origins were applied. This matters for interpreting the endpoint and relating measurements to subsequent outcomes.

The record lists five analyses but does not identify their purpose, candidate predictors, adjustment variables, model assumptions, or multiplicity approach. Missing-data handling, analysis populations, and validation are likewise not reported. As an observational study without a like-for-like control comparison, it cannot on its own establish causal effects of measured features. These limitations concern what can be assessed from the registry record; they do not establish what methods may have been used outside the record.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Independent Predictors of Mortality in Polytrauma Patients — NCT01669577.
• Registry record last updated 2015-01-09; status last verified 2015-01.
• Results first posted 2015-01-09.
• 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.

19. Record Summary

NCT01669577 is a completed prospective observational longitudinal study sponsored by the University of Sao Paulo. The registry lists an enrollment of 200, the condition Multiple Trauma, a start date of 2010-10, and primary completion in 2013-06. Its stated objective was to seek independent predictors of mortality in polytrauma patients.

The primary endpoint was death among severe polytrauma patients (ISS>15) within the first 30 days. Data collection was described at the trauma scene, in the emergency room, and at three and 24 hours of hospital stay; follow-up was described as 30 days after hospital admission. The listed procedure was analysis of blood samples and clinical features. The registry reports one outcome measure and five analyses but does not provide their results or detailed methods.

The primary death count, predictor findings, uncertainty estimates, missing-data approach, and analysis populations are not reported in the registry record. Adverse-event fields list 1 serious-event-affected participant among 200 at risk and 2 other-event-affected participants among 200 at risk; thrombophlebitis is listed at 0.5% among the top serious terms.