An observational study in Microemboli, Stroke and Transcranial Doppler Ultrasound, sponsored by Universidade do Porto. Enrolling by invitation at 1 site in Portugal. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-12-10.
Sponsored by Universidade do Porto · Observational
Microembolic signals (MES) is a powerful predictor of future embolic events. This study aims to develop and validate a accurate model of classification of MES obtained by transcranial Doppler. monitoring of However, MES detection is technically demanding and requires expert interpretation. By providing a reproducible framework for MES interpretation, this work aims to facilitate MES integration into future clinical trials and decision-making.
Rationale
The presence of microembolic signals (MES) is a powerful predictor of future embolic events. However, MES detection is technically demanding and requires expert interpretation.
Aim We aim to develop and validate a supervised prediction model for MES classification using features extracted from transcranial Doppler (TCD) signals. The model is intended to support expert consensus and enhance classification concordance by utilizing standardized, pre-specified signal features.
Sample size estimates Sample size was estimated using the pmsampsize R package. Based on five predictors, a 1:1 proportion of MES in final dataset, a maximum Nagelkerke R² of 0.75, a shrinkage factor of 90% (to minimize overfitting), and a mean absolute error in predicted probabilities ≤ 0.05, the required sample size is 850 clips. The calculations included an 80:20 training/testing split and a 10% dropout rate.
Methods and Design The "Multicenter Study to Optimize Microembolic Signal Classification Based on Double-blind Multiparametric Assessment by Human Experts Using a Universal Graphical Interface" (MESOMEGA trial) is a prospective, randomized, double-blind, diagnostic validation study. All members of World Organization of Neurosonology, their national affiliated societies, and worldwide TCD users in the medical community will be invited to submit TCD monitoring 20-second clips of presumed solid MES or non-MES high-intensity transient signals recorded using a 2 MHz transducer from the proximal middle cerebral artery. Exclusion criteria include inseparable multiple MES (e.g., curtain) or any gaseous embolic form. Each clip will be independently assessed by two randomly allocated experts. Expert reading will be using TCDPlayer and will be blinded to clinical data, source information, and other assessments. They will manually annotate six predefined signal features: characteristic audible signal increase, characteristic wave-like of raw Doppler signals, Emboli-to-Background Ratio, Emboli-to-Mirror Ratio, signal duration, and average velocity of maximum intensity. Analysis will be completed within 90 days. A supervised decision tree model will be developed on the training dataset and validation set. Performance will be assessed using stratified k-fold cross-validation, reporting accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Following model development, a Delphi consensus process will be used to evaluate and validate model outputs, aiming for expert agreement on model acceptability and readiness for clinical application. The study will be conducted under appropriate ethical approval and in accordance with international report standards. The study will be conducted under ethical guidelines and approval.
Study Outcomes The primary outcome is the classification of clips as MES or non-MES, using expert consensus as ground truth. The model will aim for ≥ 90% classification accuracy. Secondary outcomes include model performance without auditory parameter, interrater concordance and variability, and Delphi consensus strength.
Discussion This study will assess the performance of a supervised decision tree model for MES classification and benchmark it against prior MES detection approaches. By providing a reproducible framework for MES interpretation, this work aims to facilitate MES integration into future clinical trials and decision-making.
7,286 studies on the registry are indexed under Stroke; 2,007 are open to participants now.
This study's planned enrollment of 850 is above the median of 160 across 1,692 observational studies indexed under Stroke.
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Clips of Transcranial Doppler monitoring either MES or non-MES high intensity transient signals (e.g. artifacts, speckle).
MES of presumed solid form or non-MES high intensity transient signals
Obtained from on a human subject with age equal to or more than 18years old
Obtained from proximal middle cerebral artery (M1 segment)
Clip with 20 seconds duration with clearly event of interest marked using TCDPlayer
With an overall background spectrum of reasonable quality to be analyzed
Exclusion Criteria:
MES in gaseous form
Use of ultrasound contrast agent or agitated saline in the previous 24 hours
Obtained from patients with mechanical valve
Obtained from patient during any cardiac surgery or endovascular procedure1
Obtained from patient with recent severe trauma
Clips with multiples inseparable MES (e.g. curtain)
Clips of MES and non-MES events. Each clip will be 20 seconds (-10 and +10 seconds in reference to the marked event). The data presented will not be modified from its original form. The final database that will be used for expert evaluation will include the necessary clips and proportions to ensure maximum reproducibility and generalization of the data. Clips will be obtained from at least 3 different types or brands of TCD machines. A single machine cannot be the source of more than 50% of the final data set. MES will be from a variety of sources including patients with atherosclerotic disease, cardioembolic stroke, or embolic stroke of unknown source.
Diagnostic Test: Expert double-blind evaluation
Expert reading will be using TCDPlayer and will be blinded to clinical data, source information, and other assessments. They will manually annotate six predefined signal features: characteristic audible signal increase, characteristic wave-like of raw Doppler signals, Emboli-to-Background Ratio, Emboli-to-Mirror Ratio, signal duration, and average velocity of maximum intensity. Analysis will be completed within 90 days.
Classification of each signal as MES or Non-MES
A MES or non-MES will be considered as such the two experts agree. If the two experts do not agree, a special board (RA, WM) will decide on classification. "Recordings that remain undetermined, or those classified as undetermined by both experts, will be excluded from the primary analysis of this study. All experts will be blinded to each other, any identification tag or clinical information. We will include five predictors in the primary analysis which are: the presence of characteristic audible signal increase, characteristic wave-like raw Doppler signals, the Emboli-to-Background Ratio (EBR), Emboli-to-mirror-ratio (EMR), time length and average velocity of maximum intensity. The calculation and definition of each predictor is detailed in table 3 and in supplemental information (Proposal for a systematic analysis and reporting of microembolic signal detection of the Microembolic Signal Detection Working Groups of the World Organization of Neurosonology).
Time frame: From enrollment until December of 2026
Trimmed version of the model
To compare the model performance with and without auditory parameter To compare the subgroups of exclude MES vs non-MES how model performance
Time frame: From enrollment until December of 2026
Model with extra-features
To compare the model performance with and without a derived Embolus Distance parameter as calculated by the formula: Embolus Distance (mm) = Time length x average velocity of maximum intensity
Time frame: From enrollment until December of 2026
Inter-expert variability in feature extraction.
Time frame: From enrollment until December of 2026
Delphi process
Strength of the Delphi process
Time frame: From enrollment until December of 2026
Plan to share: Yes — Publication of protocol and associate report sheets
Supporting information: Study protocol, Sap, Icf, Csr, Analytic code
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