Clotild® Smart Guidewire System (CSGS) Evaluation in EndovascUlar Thrombectomy Procedure

Tthe proportion of patients having intracranial vessel perforation and / or dissection due to Clotild® usage at the site of usage in intracranial vessels will be assessed by Interventional Neuroradiologist during the procedure and final adjudication of the DSA (Digital Subtraction Angiography) by the Data Safety Monitoring Board.

• The performance of the CSGS model will be assessed by the performance metric Area Under the Receiver Operator Characteristic curve (AUC of ROC) computed from the RBC-content score predicted on the validation dataset only.
• The ROC curve will be defined by the sensitivity (true positive rate) on the Y-axis and 1- specificity (false positive rate) on the X-axis. The ground truth is defined as the binary classification of the local regions into RBC-positive or RBC-negative content performed by SENSOME experts.
• The comparator will be the binary classification score determined by the CSGS model. The CSGS model will be developed using interventions from the development phase (Development Performance Population) while it will be validated on the impedance data from the validation phase (Local-Scale Performance Population).

This endpoint evaluated the clot-scale predicted RBC% as compared to histological evaluation of RBC%.

The analyzed dataset comprised the interventions from the clot-scale validation performance population for which at least one tagged measurement was predicted with clot contact.

The concordance is to be evaluated by the slope of the linear regression of the independent variable "RBC percentage predicted at clot scale" against the dependent variable "RBC percentage measured by histology".

No additional factors or covariates were included when assessing the linear regression.

This endpoint sought to evaluate the ability of study device (CSGS) to detect the proximal end of the occlusion, as compared to the physician's labelling. The concordance was judged by the Area Under the Curve of Receiver Operating Characteristic (AUC of ROC) on the validation dataset. Evaluation included those interventions from the validation set where the 'PRE CLOT' and 'CLOT' tagged measurements were acquired with at most one missing or anomalous individual measurement

Procedural success defined as the ability to navigate the investigational device to the occlusion and measure electrophysiological properties of the occlusion. Procedural success was achieved from the moment that at least one evaluable measurement in the occlusion was captured by the investigational device.

The ability to perform binary classification was evaluated by the performance metric AUC of ROC (Area under the Curve of Receiver Operating Characteristic) computed from the platelet-content score predicted on the validation dataset. The same methodology was used as for the primary performance endpoint.

This endpoint evaluated the clot-scale predicted platelet content as compared to histological evaluation (CD42b immunostaining). The analysis was performed using the clot-scale validation performance population.

• To determine the histological results of the clot, the platelet % composition (value between 0 and 100) was averaged over the 3 analyzed slides provided by the Core Laboratory.
• Significant correlation was judged at the 0.05 significance level by the t-statistic of the linear coefficient of the ordinary linear regression between the independent variable "platelet % content as output by the prediction model" and the dependent variable "platelet % content as quantified by the IHC CD42b immunochemistry staining histological analysis". A complimentary analysis was carried out considering the MSB 'Platelets and other' percentage quantification provided by the histology core lab.

No additional factors or covariates were included when assessing the linear regression

The ability to perform binary classification was evaluated by the performance metric AUC of ROC (Area Under the Curve of Receiver Operating Characteristic) computed from the platelet-content score predicted on the validation dataset. The same methodology was used as for the primary performance endpoint.

This endpoint evaluated the clot-scale predicted fibrin content as compared to histological evaluation (MSB staining), using the clot-scale validation performance population.

• To determine the histological results of the clot, the fibrin percentage composition (value between 0 and 100) was averaged over the 3 analyzed slides provided by the core lab.
• Significant correlation was judged at the 0.05 significance level by the t-statistic of the linear coefficient of the ordinary linear regression between the independent variable "fibrin percentage content as output by the prediction model" and the dependent variable "fibrin percentage content as quantified by the MSB-staining histological analysis".

No additional factors or covariates were included when assessing the linear regression.