This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record. Numerical trial results on this page are restricted to the results posted in the ClinicalTrials.gov record; calculations added by Clinical Biostats are labelled as such.
1. Trial at a Glance
This page covers the registry results for Validation of Naevia Medical in Valvulopathies (NCT06392464), a registered phase not stated study. The primary outcome is Number of Relevant Appropriate Recommendations.
| Feature | Registry value |
|---|---|
| ClinicalTrials.gov identifier | NCT06392464 |
| Phase | Phase not stated |
| Condition | Heart Valve Disease |
| Allocation | Not Stated |
| Design model | Not Stated |
| Masking | Not Stated |
| Enrollment | 106 |
| Arms in the primary outcome | n/a |
| Lead sponsor | Dilemma Solutions S.L. |
| Results posted | Yes |
2. Clinical Question
Population
The study concerns patients with heart valve disease, described in the registry as retrospective clinical cases of cardiac valvulopathies. The record reports an enrollment of 106. It does not report further eligibility details, such as disease severity, clinical setting, or how cases were selected. Those omissions limit how specifically the study population can be characterized.
Intervention
The intervention is Naevia Medical, described as a knowledge-based clinical decision support system and registered as software. The study aims to assess the system in relation to clinical benefit and safety. The registry record does not report how clinicians interacted with the software, what information it received, or how its recommendations were incorporated into care.
Comparator
The stated comparison is conventional management at baseline versus assessment after activation of the clinical decision support system. The arm description says the same group is evaluated using a pre-test-post-test design, longitudinal repeated measures, and retrospective validation. The registry record does not explain the allocation process. It is not described as a like-for-like randomized control comparison.
Primary question
The primary endpoint is the number of relevant appropriate recommendations, measured in recommendations over a timeframe of 1 week. Only high-level recommendations classified as class I and IIa are included. In broad terms, the question is whether the count of recommendations meeting the study’s relevance and appropriateness criteria differs between baseline conventional management and assessment after system activation. The endpoint results and the criteria used to judge appropriateness are not reported in the registry record.
3. Trial Design
The registry describes a completed study titled “Validation of Naevia Medical in Valvulopathies.” Its stated purpose is to validate a clinical decision support system for clinical benefit and safety in cases of cardiac valvulopathies. The study uses retrospective clinical cases and describes evaluation during baseline measurement, characterized as conventional management, and after activation of the software.
The arm description says the same group of patients is evaluated in a pre-test-post-test design, with a longitudinal approach and repeated measures. This makes the intended comparison within the same group over two assessment conditions, rather than a comparison between clearly described independent treatment groups. The description also uses the phrase “randomly assigned group,” but the registry record does not state what was assigned, how assignment occurred, or what role it played in the study. The record’s design note cautions that allocation is not stated and that the arms are not a like-for-like control comparison.
The registered intervention is a clinical decision support system (software). The registry reports an enrollment of 106 and lists Heart Valve Disease as the condition. It gives a study start date of 2024-03-27, a primary completion date of 2025-01-24, and a status of COMPLETED. These administrative details do not establish how many cases contributed to each assessment or to the primary endpoint.
The registry lists three outcome measures and zero analyses. It does not report a detailed protocol, the methods used to select or review the retrospective cases, the allocation procedure, or the statistical analysis plan. As a result, the design can be described at the level of the stated before-and-after repeated-measures approach, but details needed to assess implementation and potential sources of bias are 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.
Before assignment: The same group of 106 cases was evaluated before and after the CDSS activation.
Overall Study
| Stage | Patients With Heart Valve Disease |
|---|---|
| STARTED | 106 |
| Pre-test | 106 |
| Post-test | 106 |
| COMPLETED | 106 |
| NOT COMPLETED | 0 |
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: 106 clinical cases of aortic stenosis, aortic insufficiency, mitral stenosis, and mitral insufficiency were selected with stratified random sampling. The conventional clinical management was retrospectively evaluated by three expert cardiologists using updated guidelines.
| Characteristic | Unit | Patients With Heart Valve Disease |
|---|---|---|
| Participants analysed | 106 | |
| Age, Continuous | Years | 73.35 (40 to 92) |
| Sex: Female, Male: Female | Participants | 42 |
| Sex: Female, Male: Male | Participants | 64 |
| Race and Ethnicity Not Collected | Participants | — |
6. Primary Endpoint
The registry lists the primary outcome as Number of Relevant Appropriate Recommendations, measured over 1 week.
| Element | Registry entry |
|---|---|
| Result type | Mean |
| Measure of spread | Standard Deviation |
| Unit | Recommendations |
| Endpoint classification | Continuous Endpoint |
The primary endpoint is the number of relevant appropriate recommendations, with the unit recorded as recommendations. The registry specifies a mean as the summary parameter and standard deviation as the measure of dispersion. The timeframe is 1 week. The description limits the endpoint to high-level recommendations, specifically class I and IIa.
In general, a count of recommendations can describe the volume of system outputs that meet prespecified criteria. Restricting the endpoint to recommendations judged relevant and appropriate focuses attention on a selected subset, rather than all recommendations generated. A mean can summarize the average count across the cases included in an assessment, while a standard deviation describes how much those counts vary across cases. These summaries are informative only when the assessment rules and the number of observations contributing to each summary are clear.
The endpoint is intended to help evaluate recommendation output before and after system activation. However, the registry record does not report the observed means, standard deviations, denominators, or the method used to determine relevance and appropriateness. It also does not report results for the primary endpoint. Without those details, the endpoint’s numerical value and the precision of any comparison cannot be assessed from the registry record.
7. Why No Re-analysis Is Shown
8. Statistical Interpretation
The registry record identifies the primary outcome and study design, but it does not include numerical results for the primary endpoint. There is no reported mean number of relevant appropriate recommendations, standard deviation, comparison estimate, confidence interval, or p-value. The record lists zero analyses. Accordingly, the posted information does not establish the size or precision of a before-and-after difference in recommendation counts.
The intended comparison is within the same group: baseline conventional management and assessment after activation of the clinical decision support system. In general, repeated measurements from the same cases can help control for stable characteristics of those cases. They also require an analysis that respects the dependence between measurements. Treating paired observations as if they came from unrelated groups can misrepresent uncertainty. The registry record does not report the statistical method used to account for repeated measures, so it is not possible to determine how that dependence was handled.
The record’s design note is also important for interpretation. Although the arm description mentions a randomly assigned group, the record does not explain the assignment or identify a like-for-like control comparison. It therefore does not support interpreting the design as a conventional randomized comparison between independent groups. A before-and-after difference, if reported, could reflect changes in assessment conditions as well as the software; the registry does not provide enough methodological detail to separate those possibilities.
The endpoint concerns recommendations, not patient health outcomes. A count of recommendations meeting the study’s criteria can describe an aspect of system output, but by itself it does not establish whether recommendations were adopted, whether care changed, or whether patient outcomes improved. Those outcomes are not reported in the registry record. The study’s stated aim includes safety, but the record does not provide enough information to draw a safety conclusion from the listed adverse-event fields.
The registry’s planning assumptions describe sample-size illustrations based on a stated fraction of an observed effect, a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. These are planning assumptions, not observed power. The corresponding effect magnitude and numerical planning estimates are not reported in the registry record, and the planning setup should not be confused with evidence that the study used equal allocation or a randomized control design.
Overall, the registry supports a description of what the study intended to measure and how the assessment was broadly organized. It does not provide the outcome estimates needed to judge the observed change or its statistical uncertainty. Any interpretation beyond the design description would require results and analysis details that are not reported in the registry record.
9. Statistical Methods Explained
Statistical concepts below are general explanations, not descriptions of methods confirmed as used in this study. The registry record identifies a mean and standard deviation for a continuous measure, but it does not report the analysis procedures or results. The distinction matters: a planned or descriptive summary is not the same as an inferential analysis of change.
What does the mean summarize?
In general, the mean is the arithmetic average of the measured values. For this endpoint, the measured value is a count of relevant appropriate recommendations for a case or assessment, expressed in recommendations. A mean can convey the average recommendation count in the analyzed observations, but it can conceal variation across cases. A few cases with many recommendations, for example, can influence an average; whether that is a concern depends on the distribution of the data. No distributional information is reported in the registry record.
The endpoint is a count, even though the registry classifies its summary as a mean of a continuous measure. Counts take nonnegative integer values, while a mean of counts can take fractional values. In general, the suitability of a mean-based analysis depends on the distribution and design, including whether observations are paired. The registry does not state whether a particular model or test was selected, nor does it report diagnostic checks.
What does the standard deviation communicate?
In general, the standard deviation describes the spread of individual observations around their mean. It is a measure of variability among observations, not a measure of how precisely the mean or a treatment contrast has been estimated. Precision for an estimated contrast is more directly conveyed by a standard error or confidence interval. Neither the standard deviation values nor confidence intervals for the primary outcome are reported in the registry record.
For a repeated-measures study, the variability of individual observations at each assessment is not enough to determine the uncertainty in change. The relationship between a case’s baseline and post-activation values also matters. If values from the same case tend to move together, the variability of within-case changes can differ from the variability suggested by each assessment considered separately. The registry describes repeated measures but does not report the paired data, their correlation, or an analysis that uses this relationship.
Why does over-dispersion matter?
In general, over-dispersion occurs when observed count variability exceeds what a selected statistical model expects. If a count model assumes less variation than is present, standard errors may be too small and confidence intervals too narrow. If the model is too conservative, precision can be understated. This is a general consideration for count outcomes, not evidence that over-dispersion occurred in this study. The registry record does not report a count model, a dispersion assessment, or any adjustment for over-dispersion.
What would a confidence interval add?
In general, a confidence interval expresses the uncertainty around an estimated quantity under the assumptions of the analysis. For a before-and-after study, an interval around the estimated change would help distinguish a precise estimate from one compatible with a wide range of changes. It would not, by itself, remove bias from case selection, measurement, or changes over time. No confidence interval or estimate of change is reported in the registry record, so the precision of the intended comparison cannot be evaluated.
How should a p-value be understood?
In general, a p-value describes how unusual data at least as incompatible with a specified null hypothesis would be, if the model assumptions and null hypothesis were true. It does not measure the size or clinical importance of an effect, and it does not give the probability that the null hypothesis is true. Interpreting a p-value also requires knowing the hypothesis, test, analysis population, and handling of multiple outcomes. Those analysis details and any p-value for the primary endpoint are not reported in the registry record.
What do the planning assumptions represent?
The registry’s sample-size assumptions describe illustrations using a two-sided significance level of 0.05, equal allocation between two arms, a normal approximation, and an effect defined as a stated fraction of the observed effect. The assumptions characterize a planning exercise; they do not establish the study’s actual design or show that a target power was achieved. The registry record does not report a sample-size justification for the trial, the numerical effect used in the illustrations, or the resulting planning estimates. Such calculations are not observed power, and they cannot substitute for the missing outcome estimate and uncertainty.
In general, a planning estimate depends on assumptions about the effect to detect, variability, significance level, allocation, and analysis. If the actual study design is a repeated-measures comparison, a planning approach that assumes equal allocation between two arms may not mirror that structure. The record cautions that the arms are not a like-for-like control comparison and does not state allocation. The planning assumptions should therefore be read as illustrations rather than a verified account of how the study was designed or analyzed.
What can be concluded from the available statistical detail?
The record specifies the endpoint’s summary type and design at a broad level, but it does not report the analysis method, estimates, or uncertainty. General statistical principles can clarify why paired-data handling, count distribution, and precision matter; they cannot fill in absent trial results. A reader cannot reconstruct the primary comparison or assess its statistical strength from the registry record alone.
10. Confidence Intervals for Each Arm
No two-arm comparison is shown for this trial, but each arm's mean can be given with a t-based 95% confidence interval computed from the posted mean, spread and number of participants.
| Arm | Participants | Mean | Standard deviation | 95% CI for the mean |
|---|---|---|---|---|
| Patients with heart valve disease (pre-test) | 105 | 2.350 | 1.970 | 1.969 to 2.731 |
| Patients with heart valve disease (post-test) | 105 | 6.200 | 2.570 | 5.703 to 6.697 |
R code and output
arm <- c("Patients with heart valve disease (pre-test)", "Patients with heart valve disease (post-test)")
n <- c(105, 105)
m <- c(2.35, 6.2)
sp <- c(1.97, 2.57)
sd <- sp # posted standard deviations
for (i in seq_along(n)) {
se <- sd[i] / sqrt(n[i]); h <- qt(0.975, n[i] - 1) * se
cat(sprintf("CARM|%s|%d|%.3f|%.3f|%.3f|%.3f\n", arm[i], n[i], m[i], sd[i], m[i] - h, m[i] + h))
}
CARM Patients with heart valve disease (pre-test) 105 2.350 1.970 1.969 2.731
CARM Patients with heart valve disease (post-test) 105 6.200 2.570 5.703 6.69711. Multiplicity, Missing Data and Analysis Populations
The registry lists three outcome measures and zero analyses. The detailed information available for the primary endpoint identifies the number of relevant appropriate recommendations, but the registry record does not describe the other outcome measures in detail or state which outcome was used for any formal analysis. It does not report a hierarchy of outcomes, a prespecified primary hypothesis, or a multiplicity adjustment.
In general, examining multiple outcomes or multiple comparisons can increase the chance of a nominally significant finding arising by chance. Methods for addressing multiplicity may include defining a hierarchy or adjusting the testing procedure. These are general options, not methods confirmed for this study. Because the analysis plan and results are not reported in the registry record, it is not possible to assess how multiplicity was handled or whether it was considered.
Missing-data handling is also not reported in the registry record. The record does not give the number of cases with evaluable baseline and post-activation measurements, explain how incomplete retrospective records were handled, or describe exclusions from the primary analysis. In general, missingness can affect both the estimate and its precision, particularly if cases with missing values differ systematically from those with complete observations. The amount and pattern of missing data, and any method used to address it, cannot be determined from the record.
The analysis populations are not reported in the registry record. It is therefore unclear whether the reported enrollment corresponds to all cases assessed, all cases meeting eligibility criteria, or a subset with complete endpoint information. The record also does not specify whether analyses were planned for all registered cases or for a defined evaluable population. These omissions prevent assessment of the relationship between enrollment, endpoint denominators, and any analysis population.
12. What the Result Does, and Does Not, Mean
The study addresses a defined statistical question about counts of recommendations judged relevant and appropriate, comparing baseline conventional management with assessment after activation of a clinical decision support system. The registry describes the outcome’s unit, summary parameter, dispersion measure, and timeframe. It also states that only class I and IIa recommendations are included. This information clarifies the intended endpoint, but it does not provide an observed outcome.
The registry record does not report the primary endpoint’s mean, standard deviation, comparison estimate, confidence interval, or p-value. It therefore cannot establish whether the recommendation count changed, how large any change was, or how precisely it was estimated. The completed status and enrollment do not, by themselves, answer those statistical questions. The record’s listing of three outcome measures and zero analyses further means that the posted information does not describe a completed inferential analysis.
Even if a difference in recommendation counts were reported, general interpretation would remain limited by the design and endpoint. The same-group pre-test-post-test structure can support a within-case comparison, but the registry does not describe the allocation process or a like-for-like control group. Such a design does not, by itself, isolate the software from other differences between assessment conditions. The record does not give enough detail to evaluate how retrospective case selection or recommendation assessment might have affected the comparison.
The endpoint is a measure of recommendation output, not a direct measure of whether clinicians followed recommendations or whether patients experienced a change in health. Those outcomes are not reported in the registry record. The study’s stated aim includes safety, but the available adverse-event fields do not provide sufficient context about ascertainment or the population at risk to support a safety interpretation.
Accordingly, the record describes a study question and broad design, not evidence for a numerical effect or a clinical conclusion. Statistical interpretation would require the endpoint results, their denominators and uncertainty, and enough analysis detail to understand how the repeated measurements were handled.
13. Why This Trial Matters Statistically
This study raises a useful statistical issue: evaluating a decision support system often involves measuring the recommendations it produces or the recommendations judged appropriate, rather than measuring a direct patient outcome. Such an endpoint can describe a specific part of system performance. It also requires transparent definitions of what counts as relevant and appropriate, because those judgments determine which recommendations enter the outcome. The registry record does not report the assessment criteria beyond its restriction to class I and IIa recommendations.
The repeated-measures structure is another important feature. When the same cases are assessed before and after an intervention, the statistical analysis should reflect that observations from the same case are related. In general, accounting for that pairing can change the estimated uncertainty compared with treating assessments as independent. The registry identifies repeated measures but does not report how they were analyzed. This makes the distinction between a design description and an inferential result especially important.
The record also illustrates why planning assumptions should be separated from study evidence. Its sample-size assumptions describe an illustration based on a fraction of an observed effect and other stated assumptions, but do not report the numerical planning estimates. Such planning values do not establish that the study had a particular level of power, and they do not replace estimates and confidence intervals from the completed study.
Finally, a completed registry record can still leave central statistical questions unresolved when outcome results and analysis details are absent. Here, the endpoint and broad design are identifiable, but the primary result, its precision, handling of missing data, and analysis population are not reported in the registry record. Those elements are needed to judge what the study’s data show.
14. 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 principal trial-specific limitation is that numerical results for the primary endpoint are not reported in the registry record. The primary outcome is defined, but its mean, standard deviation, denominator, and comparison estimate are absent. The record lists zero analyses, so the intended pre-test-post-test contrast cannot be evaluated statistically from the posted information.
The design description leaves important details unresolved. It states that the same group is evaluated using repeated measures and retrospective validation, while also using the phrase “randomly assigned group.” The registry does not explain the assignment process or what was assigned. Its design note states that allocation is not stated and the arms are not a like-for-like control comparison. The record therefore does not support a more specific characterization of randomization or control.
Details that could affect endpoint interpretation are not reported in the registry record. These include the criteria for judging recommendations relevant and appropriate, the method for selecting retrospective cases, the number of cases contributing data at each assessment, and how any incomplete records were handled. The statistical method for comparing repeated measures is also not reported.
The registry lists three outcome measures, but does not report the analysis plan, multiplicity handling, or analysis populations. The adverse-event fields list zero values for the recorded group, including the at-risk fields; without ascertainment details and meaningful denominators, these entries do not establish that adverse events were absent. Together, these reporting gaps limit assessment of the study’s results, precision, and potential bias.
15. Related Tutorials
16. Related Statistical Calculators
17. Sources
- ClinicalTrials.gov: Validation of Naevia Medical in Valvulopathies — NCT06392464.
- Registry record last updated 2025-12-31; status last verified 2025-12.
- Results first posted 2025-12-31.
- 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.
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18. Record Summary
NCT06392464 is a completed study titled “Validation of Naevia Medical in Valvulopathies,” with the official title “Clinical Validation Study of Naevia Medical, a Clinical Decision Support System, in Heart Valve Disease.” The sponsor is Dilemma Solutions S.L. The registry lists Heart Valve Disease as the condition and an enrollment of 106.
The study started on 2024-03-27, with primary completion on 2025-01-24. The registered intervention is a clinical decision support system (software). The design description identifies retrospective clinical cases and a same-group pre-test-post-test approach with repeated measures. The primary endpoint is the number of relevant appropriate recommendations over 1 week, summarized as a mean with standard deviation. The registry lists three outcome measures and zero analyses. Numerical primary endpoint results and detailed analysis methods are not reported in the registry record.