Assisted Fluid Management IDE Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Assisted Fluid Management IDE Study (NCT03469570), a na na study. The primary outcome is Effectiveness of the Assisted Fluid Management Feature.

2. Clinical Question

In practical terms, the study asks whether the feature’s fluid recommendations correspond to a stroke-volume change that meets the selected strategy. That is a validation question about a device-guided process, not a randomized estimate of the difference between two assigned treatments. The record does not specify the clinician-selected strategy in enough detail to assess how it was standardized across participants, nor does it report the result used to judge prediction.

3. Trial Design

The Assisted Fluid Management IDE Study is recorded as a completed, single-group device study. The intervention was the EV1000 Clinical Platform with Assisted Fluid Management. Its stated objective was to evaluate the feature’s performance in predicting fluid responsiveness. The record lists 330 enrolled participants and identifies non-cardiac or non-thoracic, abdominal, pelvic, and major peripheral vascular surgery as relevant conditions.

The study is not described as randomized: allocation is NA, and the record explicitly indicates that the study is not a randomized comparison. Masking is listed as none. These design details matter because the results cannot be interpreted as a randomized, blinded comparison of the feature with a control intervention. The record does not identify a separate control arm.

Participants’ fluid-management decisions were guided by the feature, while the Principal Investigator could accept or decline fluid recommendations. This means recommendations were not necessarily followed in every case, but the record does not report the acceptance pattern or how it entered the endpoint assessment. The primary outcome timeframe is 30 Day follow-up.

The study status is completed. The recorded start date is 2018-04-19, and the primary completion date is 2018-12-21. The registry record does not report the protocol details needed to assess how the study selected its sample size, whether there were interim analyses, or how the primary endpoint was statistically analyzed. It lists one outcome measure and zero analyses. Those registry entries do not establish that no analysis was performed; they indicate that no analysis is listed in the record. The primary outcome result itself is not reported in the registry record.

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.

Overall Study

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: Arterial Pressure Monitoring (enrolled) N=330. Two pilot subjects were permitted per site for training purposes N=16. Pivotal Cohort (Safety) N=314.

6. Primary Endpoint

The registry lists the primary outcome as Effectiveness of the Assisted Fluid Management Feature, measured over 30 Day follow-up.

The primary endpoint is a participant count: the number of participants whose stroke volume change met the clinician-selected fluid strategy. The registry record assigns a 30 Day follow-up timeframe and describes the endpoint as a measure of the Assisted Fluid Management feature’s effectiveness. Its stated objective is prediction of fluid responsiveness.

As a binary outcome, each participant’s status is represented as meeting or not meeting the specified criterion, with the result expressed as a count. A count can describe how many participants fulfilled the criterion, but alone it does not show how well a prediction performed. To evaluate prediction, interpretation would depend on details such as how fluid responsiveness and the clinician-selected strategy were defined, how predictions were matched to observed responses, and what analysis was planned. Those details and the endpoint result are not reported in the registry record.

In general, a prespecified endpoint helps focus a study on a defined question rather than on whichever result looks most favorable after data collection. Here, the endpoint links the feature’s recommendations to a clinically selected fluid strategy through stroke volume change. The registry record does not give enough information to judge whether the criterion was applied consistently or to assess the precision of the observed endpoint estimate.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry record identifies the outcome as a binary participant count, but does not report the primary result or an analysis. The concepts below are general statistical explanations, not claims about analyses used in this study.

What does a participant count represent?

A binary endpoint classifies each participant according to whether a defined criterion was met. The count records how many participants fall into the event category. To interpret that count, readers need its denominator, a clear definition of the event, and information about participants whose status is unknown. The registry record describes the endpoint criterion and timeframe but does not report the primary count, denominator for the endpoint, or missing endpoint data.

Counts are straightforward to communicate, but they are not automatically comparative measures. A count from one group does not, by itself, tell a reader whether an outcome is more common than it would have been under another strategy. Comparison requires an appropriate reference, and estimation of uncertainty requires an analysis suited to the data and design.

How does a validation question differ from a treatment comparison?

A validation study asks whether a tool’s output corresponds to a defined target or observed response. A treatment comparison asks whether outcomes differ between strategies assigned to comparable groups. This trial is recorded as single-group and not randomized, so it is not a randomized comparison of fluid-management strategies. The registry record states that the feature predicts fluid responsiveness, but does not report the statistical measures used to evaluate prediction. For example, measures of agreement or classification performance would require definitions and results not present in the record.

General principle: predictive performance depends on how the prediction is defined, what counts as the target response, when each is assessed, and how discordant or indeterminate cases are handled. If clinicians can accept or decline recommendations, that process may also matter when connecting a recommendation to a subsequent outcome. The record notes that recommendations could be accepted or declined but does not describe the frequency or analysis of those decisions.

Why does a confidence interval matter?

In general, a confidence interval expresses uncertainty around an estimate under specified assumptions. It helps distinguish an estimate that is precise from one that remains uncertain. A point estimate alone cannot show the range of values compatible with the observed data and chosen method. No primary endpoint estimate or confidence interval is reported in the registry record, so its precision cannot be evaluated.

A p-value, when used appropriately, addresses compatibility between the observed data and a specified statistical model or null hypothesis. It does not measure the size or practical importance of an effect, and it cannot replace an estimate with an uncertainty interval. The record lists zero analyses and does not report a p-value for the primary endpoint. The absence of a listed analysis should not be taken to mean that an analysis was not performed; it means the registry record does not report one.

What can a planning illustration tell readers?

In general, sample-size planning estimates how many participants might be needed to detect a prespecified effect under stated assumptions. Such a calculation is not observed power and does not show what a completed study found. The planning assumptions associated with this record describe a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. They also describe the effect to detect as a stated fraction of the observed effect. These assumptions are planning illustrations, not evidence that the study itself used a two-arm design; the trial is recorded as single-group.

The record does not report numerical planning outputs or the trial’s own sample-size justification. It is therefore not possible to assess from the registry record whether enrollment was selected to achieve a particular level of precision or power. A planning illustration based on an observed effect would also depend on that effect estimate and its uncertainty; it should not be interpreted as proof that a future study will reproduce the same result.

Why can over-dispersion matter in general?

Over-dispersion occurs when observed variability is greater than a statistical model assumes. When present, standard errors from a model that ignores it can be too small, leading to overly narrow intervals or misleading tests. The registry record does not identify an over-dispersion analysis, and the primary outcome result and analysis are not reported. This general issue should not be taken to imply that over-dispersion was present or relevant to the study’s endpoint.

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("Assisted Fluid Management")
n      <- c(314)
events <- c(307)
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  Assisted Fluid Management  314  307  97.8  95.5  99.1  95.5  98.9  95.5  98.9

11. Multiplicity, Missing Data and Analysis Populations

The registry record lists one outcome measure and zero analyses. It does not report a statistical analysis plan, a multiplicity adjustment, or details about how the primary outcome was selected relative to other outcomes. Therefore, the record does not establish whether multiple hypotheses were tested or how any multiplicity was handled. General principle: testing many outcomes or analyses can increase the chance of a nominally unusual result, and prespecified methods can help control that risk. No particular multiplicity procedure can be attributed to this study from the record.

Missing-data methods are also not reported. The record does not say how missing follow-up, unavailable stroke-volume measurements, or participants without a classifiable endpoint were handled. It does not give the number of participants included in the primary endpoint analysis or define an analysis population such as all enrolled participants or an evaluable subset. These omissions matter because the denominator and handling of incomplete observations can affect an endpoint count and its interpretation.

The record’s enrollment figure and the safety population listed for adverse events are not a substitute for a reported primary analysis population. The safety record gives 314 participants at risk for the displayed adverse-event outcomes, while the primary endpoint denominator is not reported in the registry record. No assumptions should be made about why those figures differ or how participants were selected for either assessment. Without endpoint-specific denominators, missing-data methods, and analysis-population definitions, readers cannot assess the possible effect of missing observations on the primary result.

12. 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% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: wound infection (3.2%), sepsis (2.5%), acute kidney injury (2.5%), arrhythmia (1.6%), anastomotic leak (1.3%).

The adverse-event record lists 314 participants at risk in the Assisted Fluid Management group for serious adverse events and 314 at risk for other adverse events. It records 88 participants with serious adverse events and 226 with other adverse events. Deaths are recorded as 0. These are group-level counts; the registry record does not provide a control group for comparison.

The most frequently listed serious adverse-event terms are wound infection at 3.2%, sepsis at 2.5%, acute kidney injury at 2.5%, arrhythmia at 1.6%, anastomotic leak at 1.3%, and deep vein thrombosis at 1.3%. These percentages are reported as listed in the record. They should be read as term-specific safety summaries, not added together or treated as a single overall rate. The record does not explain whether one participant could have more than one listed event, or give additional detail about event timing, severity criteria, or attribution.

In general, safety counts describe what was recorded during follow-up; they do not establish that the device or a particular management decision caused an event. A comparative safety conclusion would require an appropriate comparison and sufficient detail on ascertainment and analysis. Those elements are not reported for a randomized comparison in this registry record.

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

The record establishes that the study evaluated a device feature intended to predict fluid responsiveness and that its primary outcome was framed around stroke volume change meeting a clinician-selected fluid strategy. It also establishes that the study was completed and recorded as a single-group study. Those details clarify the question and design, but they do not answer the central effectiveness question because the primary result is not reported in the registry record.

The record does not show how many participants met the primary endpoint, how accurately the feature predicted fluid responsiveness, or how precise any such estimate was. It therefore does not support a conclusion about the extent of predictive performance. The endpoint description alone is not a result. Nor does the absence of a listed analysis establish that the feature failed or succeeded; the record simply does not report the primary analysis.

The safety counts document events in the Assisted Fluid Management group, including the listed serious adverse-event terms and 0 deaths. They do not establish whether these outcomes were more or less common than under another approach. With no randomized control group, no like-for-like comparison is available in the registry record. The counts also do not establish causation or explain whether events were related to the device, surgery, or other factors.

In general, validation evidence is strongest when the prediction target and measurement procedures are clear, the evaluation analysis is prespecified, and the estimate is accompanied by its uncertainty. The registry record does not report the information needed to judge those features for this study. The results should therefore be interpreted as an incomplete registry account of a completed validation study, rather than as a quantified demonstration for or against the feature’s performance.

14. Why This Trial Matters Statistically

This study matters statistically because it illustrates the difference between a stated study objective and a reported result. The registry describes a prediction question and a corresponding endpoint, but does not report the primary count or its analysis. Without an estimate and an indication of its precision, readers cannot assess whether the result is informative, uncertain, or compatible with a range of performance levels.

Its single-group structure is also central to interpretation. A device feature can be evaluated within a validation process, but that does not make the study a randomized comparison of competing management strategies. The registry explicitly records no randomized comparison and no masking. Keeping those distinctions clear prevents descriptive outcome counts from being mistaken for comparative treatment effects.

The study also highlights the importance of defining the target of prediction. The endpoint refers to stroke volume change meeting a clinician-selected fluid strategy, while recommendations could be accepted or declined. The registry does not give the operational details or analysis linking recommendations, clinician decisions, and endpoint classification. Such details are important for understanding exactly what a validation result would represent.

Finally, the safety counts are useful as descriptive reporting, but cannot substitute for a comparative safety analysis. Overall, the registry record makes the intended question visible while leaving critical outcome and methods information unreported. That distinction is important when assessing how much evidence a registry entry can support.

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.

The primary limitation for interpretation is that the primary outcome result is not reported in the registry record. The endpoint count, its denominator, any uncertainty interval, and any analysis of predictive performance are absent. The central effectiveness objective therefore cannot be evaluated from the record.

The study is recorded as single-group, with allocation listed as NA and no masking. It does not provide a randomized control comparison, so it cannot establish a comparative effect against another fluid-management strategy. Recommendations could be accepted or declined by the Principal Investigator, but the record does not report how often this occurred or how such decisions were incorporated into the endpoint assessment.

The endpoint description refers to a clinician-selected fluid strategy, but the record does not provide its detailed definition or report how it was applied. The registry also does not report the primary analysis population, endpoint-specific denominator, or missing-data methods. These gaps limit assessment of how complete and comparable endpoint classifications were.

For safety, the record gives counts and selected term percentages but no comparison group or detailed account of event ascertainment and attribution. It does not explain whether participants could appear under multiple terms. The record also does not report the protocol, sample-size justification, interim analyses, or statistical plan. These omissions prevent a fuller assessment of design choices and analysis decisions; they should not be filled by assumptions.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Assisted Fluid Management IDE Study — NCT03469570.
• Registry record last updated 2024-11-08; status last verified 2019-05.
• Results first posted 2024-11-08.
• 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

The Assisted Fluid Management IDE Study is a completed, single-group device study of the EV1000 Clinical Platform with Assisted Fluid Management. The registry lists 330 enrolled participants, no masking, and a primary objective of evaluating the feature’s ability to predict fluid responsiveness.

The primary endpoint is the count of participants with stroke volume change meeting a clinician-selected fluid strategy at 30 Day follow-up. The primary result and analysis are not reported in the registry record. The adverse-event record lists 314 participants at risk, 88 participants with serious adverse events, 226 with other adverse events, and 0 deaths. With no randomized comparison group, the study’s posted safety counts are descriptive rather than comparative.