# Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning

Clement Bogey, Aymeric Rouchaud, Jean-Christophe Gentric, Edouard Beaufreton, Serge Timsit, Frederic Clarencon, Jildaz Caroff, Romain Bourcier, François Zhu, Cyril Dargazanli, et al.

► To cite this version:

Clement Bogey, Aymeric Rouchaud, Jean-Christophe Gentric, Edouard Beaufreton, Serge Timsit, et al. Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning. Journal of Neuroscience Methods, 2025, 416, pp.110376. 10.1016/j.jneumeth.2025.110376. hal-05036887

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# Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning

Clement Bogey<sup>a</sup>, Aymeric Rouchaud<sup>b,c</sup>, Jean-Christophe Gentric<sup>d</sup>, Edouard Beaufreton<sup>d</sup>, Serge Timsit<sup>e</sup>, Frederic Clarencon<sup>f</sup>, Jildaz Caroff<sup>g</sup>, Romain Bourcier<sup>h</sup>, François Zhu<sup>i</sup>, Cyril Dargazanli<sup>j</sup>, Jean-François Hak<sup>k</sup>, Gregoire Boulouis<sup>l</sup>, Heloise Ifergan<sup>l</sup>, Raoul Pop<sup>m</sup>, Geraud Forestier<sup>a</sup>, Bertrand Lapergue<sup>n</sup>, Julien Ognard<sup>o,p,w</sup>, Etis Investigators<sup>l</sup>

## ARTICLE INFO

**Keywords:**

Stroke

Machine learning

Predictive models

Outcome

## ABSTRACT

**Background and purpose:** Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment individually for each patient, the aim of this study was to evaluate the performances of Machine Learning to predict clinical outcome (mRS) at 3 months after MT.

**Material and methods:** From the ETIS French prospective multicenter registry, data from patients who underwent MT for anterior circulation stroke with large vessel occlusion between January 2018 and December 2020 were extracted. Three machine learning models (Support Vector Machine, Random Forest and XGBoost) have been trained with clinical, biological and brain imaging data available in emergency conditions from the cohort of patients treated from 2018 to 2019. Models' performances to predict good outcome (3-months mRS <3) were evaluated on patients treated in 2020. Performances were evaluated with AUC, accuracy, sensitivity and specificity, then ROC curves AUC were compared with the best performing model.

**Results:** 4297 patients were included, 1737 (40 %) with good outcome and 2560 (60 %) with bad outcome were used to train models and 599 patients treated in 2020 were used to evaluate their performances. The best model was obtained with XGBoost: AUC = 0.77, accuracy = 69.3 % but no statistically significant difference existed between models.

**Conclusion:** Our study shows satisfying performances of machine learning to predict clinical outcome after MT using data easily available at initial diagnosis and before the decision to treat.

<footnote><sup>a</sup> Neuroradiology Department, Limoges University Hospital, Limoges, France</footnote>

<footnote><sup>b</sup> Limoges University Hospital, Department of radiology, Limoges, France</footnote>

<footnote><sup>c</sup> University of Limoges, Department of radiology, Limoges F-87000, France</footnote>

<footnote><sup>d</sup> University of Brest, GETBO, INSERM UMR1304, Neuroradiology, University Hospital of Brest, Brest, France</footnote>

<footnote><sup>e</sup> University of Brest, INSERM UMR1078, Neurology, University Hospital of Brest, Brest, France</footnote>

<footnote><sup>f</sup> Department of Neuroradiology, La Pitie Salpetrière Hospital, Paris, France</footnote>

<footnote><sup>g</sup> NEURI Vascular Center, Bicêtre Hospital Interventional Neuroradiology, Le Kremlin-Bicêtre, France</footnote>

<footnote><sup>h</sup> Department of Neuroradiology, Nantes University Hospital, Tours, France</footnote>

<footnote><sup>i</sup> Department of Neuroradiology, Nancy University Hospital, Tours, France</footnote>

<footnote><sup>j</sup> Department of Neuroradiology, Montpellier University Hospital, Tours, France</footnote>

<footnote><sup>k</sup> Department of Neuroradiology, Marseille University Hospital, Tours, France</footnote>

<footnote><sup>l</sup> Department of Neuroradiology, Tours University Hospital, Tours, France</footnote>

<footnote><sup>m</sup> Department of Neuroradiology, Strasbourg University Hospital, Tours, France</footnote>

<footnote><sup>n</sup> Department of Neurology, Foch Hospital, Suresnes, France</footnote>

<footnote><sup>o</sup> University of Brest, LATIM, INSERM UMR1101, Brest France</footnote>

<footnote><sup>p</sup> Department of Radiology, Mayo Clinic Rochester, MN, USA</footnote>

<footnote>* Correspondence to: MAYO CLINIC – Radiology – Neurovascular Research Lab- Alfred Building, 9-446 200 First St. SW Rochester, MN 55905, USA.
E-mail address: ognard.julien@mayo.edu (J. Ognard).</footnote>

<footnote n="1">¹ List of the ETIS INVESTIGATORS, See Appendix A.</footnote>
# 1. Introduction

Acute ischemic stroke with large vessel occlusion (LVO) represents a serious medical emergency with significant morbidity and mortality. Mechanical Thrombectomy (MT) has emerged as the standard of care for anterior circulation stroke with LVO, significantly improving mortality and functional prognosis (Goyal et al., 2016). However, a part of MT benefits for extended stroke, distal occlusion, very old patients, and those beyond the time window for thrombolysis, as well as the role of thrombolysis in combination with MT, remain uncertain (Abdelrady et al., 2023; Bai et al., 2023; Broocks et al., 2022; Meyer et al., 2023; Sarraj et al., 2018). Despite advances in technology and techniques, outcomes following MT remain variable. Moreover, given the risks and morbidity associated with MT, it is critical to identify patients who would most likely benefit from endovascular revascularization (Pilgram-Pastor et al., 2021).

In recent years, artificial intelligence and machine learning (ML) algorithms have shown high performance in predicting clinical outcomes and are now used in a wide range of medical applications (Obermeyer and Emanuel, 2016). Machine learning algorithms can identify complex patterns in large datasets, which may be difficult for humans to detect, and can generate accurate predictions based on these patterns. Moreover, machine learning algorithms are adaptive, meaning they can learn and improve from new data; it may also allow to identify clinical factors that are most predictive of favorable outcomes after MT, even when those factors are not traditionally considered in clinical decision-making (Dumitriu LaGrange et al., 2023; Siddiqui et al., 2023).

However, machine learning algorithms have some limitations, including the potential for overfitting, or the creation of models that are too complex and specific to the dataset used for training, which may result in poor performance when applied to new datasets. Indeed, previous studies have used ML to predict mRS scores after MT, but the sample sizes have been limited, and the models have not been validated across multiple centers. Additionally, the datasets used in these studies have often been limited to specific patient populations, which may not be representative of the wider stroke population (Alaka et al., 2020; Alawieh et al., 2019; Brugnara et al., 2020; Heo et al., 2019; Hu et al., 2022; Lu et al., 2022; Mutke et al., 2022; Nishi et al., 2019; van Os et al., 2018).

In this study, we aimed to predict clinical outcomes after MT for anterior circulation stroke with LVO using machine learning algorithms and data that are readily available in emergency conditions. We leveraged data from 3 years of the ETIS French national registry, which contains information on patients who have undergone MT in various clinical settings. We evaluated several machine learning algorithms, including decision trees, random forests, support vector machines, and compared their performance in predicting clinical outcomes.

# 2. Material and methods

## 2.1. Study design and population

The study is an investigator-initiated retrospective analysis of a prospectively collected multi-center database of an ongoing observational trial. Its reporting follows the actual recommendations for Reporting Machine Learning Analyses in Clinical Research, and CONSORT-AI (Stevens et al., 2020).

Patients enrolled in the prospective, multicenter, Endovascular Treatment of Ischemic Stroke (ETIS) registry between January 2018 and December 2020 were considered to be included in this study. During this period, 20 comprehensive stroke centers in France performed MT in consecutive stroke patients with LVO. There were no exclusion criteria regarding MT indication, which was left at the discretion of the treating physician. ETIS did not impose any threshold regarding patient age, National Institutes of Health Stroke Scale (NIHSS) score, delay since onset, or infarct size. All patients who underwent MT for acute ischemic

stroke in the anterior in these centers were recorded in the ETIS Registry.

For the present analysis, inclusion criteria were as follows: Subjects of 18 years of age or more included in the registry that underwent a MT (from ICA to M2 and A2) for anterior circulation stroke and for which a stroke was diagnosed with brain MRI within 24 h from symptoms onset.

This study involves human participants and was conducted according to actual law and ethics respecting the latest Helsinki declaration amendment, approved by Comité de Protection des Personnes: 2018-09. Informed consent was obtained from all patients or their relatives. ClinicalTrials.gov Identifier: NCT03776877. IDRCB: 2017-A03457-46. All data of the ETIS Registry were collected, stored, and accessed locally according to the recommendations of the "Comité consultatif sur le traitement de l'information en matière de recherche dans le Domaine de santé". Local institutional review boards approved the data collection and analyses.

From the 7024 patients in the registry, a total of 3563 patients were included in the present study after elimination of missing data about 3 months mRS, patients under 18 years old and posterior circulation stroke.

By analyzing a large dataset from the ETIS French national registry, we aim to develop and validate a predictive model that incorporates clinical variables that are readily available in emergency settings. The model will be trained on the data from a subset of patients and evaluated on the remaining data.

## 2.2. Sets

The study population was chronologically divided into a training set and a validation set. The training set consisted of patients treated between January 2018 and December 2019, while the validation set included patients treated in 2020. A total of 3958 patients were included in the training set, and 3066 patients were treated in 2020 and considered as validation. Fig. 1 shows the patient flow chart.

## 2.3. Prediction feature

The prediction feature was a binary outcome as a score of 2 or less on the modified Rankin scale at 90 days following MT (three months functional independence). The modified Rankin scale is a 7-point scale of global disability ranging from 0 (no symptoms) to 6 (death). It was assessed by certified medical personnel or clinical research assistant during a clinical visit or a structured telephone interview.

The primary outcome of the study was the performance of each ML model to predict the three months functional independence through receiver operating characteristic (ROC) curve analysis.

## 2.4. Features

Easily available data at admission and after brain MRI were extracted. Clinical, biological, and imaging data were collected from medical records at admission, including age, medical history, baseline National Institutes of Health Stroke Scale (NIHSS), blood pressure, heart rate, glycemia, pulse oximetry, thrombolysis, time to reperfusion. Other imaging and procedural features like recanalization score, Alberta Stroke Program Early CT Score (ASPECTS), T2 FLAIR hyperintensity, localization of occlusion, and type of recanalization procedure were declared by the local investigator in the ETIS registry.

## 2.5. Encoding

There were no specific exclusion criteria at the level of the input data, except the completeness of all recordable features in the validation set. For the training set, the missing values were imputed using a random forest-based multivariate imputation by chained equation (MICE) algorithm. Listwise deletion was applied to the validation set: a total of 599 patients was finally considered for the analysis. There was a statistical
<figure><img src="image_4.png" /><figcaption>Fig. 1. Flow diagram of the patients included in this study.</figcaption></figure>

difference in the amount of missing data between training set and initial validation set for the following features: intakes of antithrombotic, T2 FLAIR positivity, ASPECT and delay from symptom onset to bMRI. Details of the missing data in both training and validation set are reported in the **Appendix 1**. Thus, 1435 patients from the training set had a data imputed. Some features contained more than 10 % of missing data (weight, SBP, DBP, heart rate) and some more than 20 % (glycemia, SpO2, T2 FLAIR hypersignal). There were no statistical differences between the training set before and after imputation for each feature. Details of the imputation are reported in the **Appendix 2**.

Numerical features were normalized. To avoid data leakage from the validation set to the training set, data was normalized on the training set, then applied to the validation set.

Categorical features with two values were dichotomized, while the outcome was dichotomized as 0 if favorable clinical outcome (mRS ≤ 2) and 1 if unfavorable clinical outcome (mRS > 2). Occlusion type was divided into seven categories using One-Hot encoding (M1, M2, ACI, extra cranial ICA, Tandem occlusion, others and not visible on MRI).

## 2.6. Algorithms

Three different popular machine learning algorithms were used: Support Vector Machine, Random Forest, and XGBoost. Best hyper-parameters were found using the GridSearchCV method. Machine learning algorithms were used to develop predictive models for clinical

outcomes. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve and accuracy. The output was a binary prediction of the functional independance status at 3 months after the MT. There was no downstream contribution to decision-making or other elements of clinical practice: the study evaluated the performance of these models.

The model algorithms were based on publicly available libraries. Data was encoded, trained and evaluated using Python v3.7.6 and SciKitLearn v0.23.2. AI intervention did not require vendor-specific devices, nor specialized computing hardware.

## 2.7. Statistical analysis

Continuous variables were reported as mean ± standard deviation, median with interquartile range (Q1, Q3), and categorical variables were reported as frequencies and percentages. The chi-square test or Fisher's exact test was used to compare categorical variables, and the Mann-Whitney U test was used to compare continuous variables. The significance level was set at p < 0.05. Statistical analyses were made with STATA statistical software, version 13.0 (StataCorp®).
# 3. Results

## 3.1. Population

Data of 2964 patients were analyzed (2365 in the training set; 599 in the validation set). The mean age was 70.6 years, the median NIHSS score was 16. The average ASPECTS score was 7.1213 (41 %) patients achieved functional independence at three months (mRS ≤ 2), while 1751 (59 %) did not.

Characteristics and comparison of groups stratified by this prediction feature are presented in Table 1. The two groups differed significantly in terms of all features, except for diastolic blood pressure, history of coronary heart disease, dyslipidemia and delay from symptom onset to bMRI in univariate analysis. The distribution of patients who had M2 or terminal ICA was different between the two groups. Five variables,

<table><caption>**Table 1**

Characteristics and comparison of data by clinical outcome.</caption><thead><tr><th>Features</th><th>Global</th><th>Favorable clinical outcome (mRS-M3 ≤ 2)</th><th>Unfavorable clinical outcome (mRS-M3 > 2)</th><th>P value</th></tr></thead><tbody><tr><td>Patients, n (total)</td><td>2964</td><td>1213</td><td>1751</td><td></td></tr><tr><td>Age, (median, IQR)</td><td>73 (61–82)</td><td>67 (55–77)</td><td>77 (67–84)</td><td>&lt; 0.001</td></tr><tr><td>Comorbidities (n, %)</td><td></td><td></td><td></td><td></td></tr><tr><td>Hypertension</td><td>1778 (21 %)</td><td>623 (52 %)</td><td>1155 (67 %)</td><td>&lt; 0.001</td></tr><tr><td>Diabetes</td><td>494 (17 %)</td><td>153 (13 %)</td><td>341 (20 %)</td><td>&lt; 0.001</td></tr><tr><td>Smoking</td><td>551 (19 %)</td><td>300 (26 %)</td><td>251 (15 %)</td><td>&lt; 0.001</td></tr><tr><td>Coronary Disease History</td><td>487 (17 %)</td><td>185 (16 %)</td><td>309 (18 %)</td><td>0.117 *</td></tr><tr><td>Dyslipidemia</td><td>900 (31 %)</td><td>347 (29 %)</td><td>553 (32 %)</td><td>0.06 *</td></tr><tr><td>Biological (median, IQR)</td><td></td><td></td><td></td><td></td></tr><tr><td>Weight (kg)</td><td>73 (63–84)</td><td>75 (65–85)</td><td>71 (60–82)</td><td>&lt; 0.001</td></tr><tr><td>Glycemia (mmol/g)</td><td>6.7 (5.7–8.1)</td><td>6.4 (5.5–7.5)</td><td>7 (5.8–8.5)</td><td>&lt; 0.001</td></tr><tr><td>SpO2 (%)</td><td>97 (95–99)</td><td>97 (96–99)</td><td>97 (95–98)</td><td>&lt; 0.001</td></tr><tr><td>Heart Rate</td><td>77 (67–90)</td><td>75 (66–88)</td><td>79 (68–92)</td><td>&lt; 0.001</td></tr><tr><td>Systolic Blood Pressure (mmHg)</td><td>146 (130–164)</td><td>142 (126–159)</td><td>148 (131–168)</td><td>&lt; 0.001</td></tr><tr><td>Diastolic Blood Pressure (mmHg)</td><td>81 (71–92)</td><td>80 (71–91)</td><td>81 (70–93)</td><td>0.25 *</td></tr><tr><td>Intakes of Antithrombotic (n, %)</td><td>1271 (44 %)</td><td>421 (36 %)</td><td>850 (50 %)</td><td>&lt; 0.001</td></tr><tr><td>bMRI</td><td></td><td></td><td></td><td></td></tr><tr><td>Flair+ (n, %)</td><td>798 (33 %)</td><td>307 (30 %)</td><td>491 (36 %)</td><td>0.005</td></tr><tr><td>ASPECTS (median, IQR)</td><td>8 (6–9)</td><td>8 (7–9)</td><td>7 (6–9)</td><td>&lt; 0.001</td></tr><tr><td>Occlusion type (n, %)</td><td></td><td></td><td></td><td></td></tr><tr><td>M1</td><td>972 (58 %)</td><td>685 (58 %)</td><td>972 (58 %)</td><td>0.79</td></tr><tr><td>M2</td><td>175 (10 %)</td><td>190 (16 %)</td><td>175 (10 %)</td><td>&lt; 0.001 *</td></tr><tr><td>Distal ICA</td><td>281 (17 %)</td><td>136 (12 %)</td><td>281 (17 %)</td><td>&lt; 0.001 *</td></tr><tr><td>Extra Cranial ICA</td><td>27 (2 %)</td><td>19 (1.6 %)</td><td>27 (1.6 %)</td><td>0.98</td></tr><tr><td>Tandem</td><td>202 (12 %)</td><td>137 (12 %)</td><td>202 (12 %)</td><td>0.78</td></tr><tr><td>Others</td><td>11 (0.6 %)</td><td>4 (0.3 %)</td><td>11 (0.6 %)</td><td>0.25</td></tr><tr><td>Not visible</td><td>21 (1.2 %)</td><td>9 (0.8 %)</td><td>21 (1.2 %)</td><td>0.21</td></tr><tr><td>Pre-mRS (median, IQR)</td><td>0 (0–0)</td><td>0 (0–0)</td><td>0 (0–1)</td><td>&lt; 0.001</td></tr><tr><td>NIHSS at admission (median, IQR)</td><td>16 (11–20)</td><td>14 (8–18)</td><td>18 (14–21)</td><td>&lt; 0.001</td></tr><tr><td>Symptom onset to bMRI time (median, IQR)</td><td>122 (90–167)</td><td>120 (89–162)</td><td>124 (91–171)</td><td>0.2 *</td></tr><tr><td>IV-tPA (n, %)</td><td>1480 (50 %)</td><td>684 (56 %)</td><td>796 (46 %)</td><td>&lt; 0.001</td></tr></tbody></table>

including age, NIHSS, glucose, ASPECTS, and pre-morbid mRS, were found to be the best predictors of clinical outcomes.

The Table 2 shows the characteristics and comparison of the training set and validation set. Analysis of the differences between data of patients analyzed during the 2018–2019 and 2020 periods revealed statistically significant differences in ASPECTS scores ASPECTS (p = 0.02) and history of diabetes and coronary disease (p < 0.001). The 2020 dataset had a higher percentage of patients with ASPECTS scores of 6 or less, indicating consideration of mechanical thrombectomy for a larger ischemic area, but no difference in age, NIHSS or mRS.

## 3.2. Models performances

Table 3 shows the performances of each models and the comparison with the best one. The XGBoost algorithm was found to be the best performing model, with an area under the ROC curve of 0.77 and an accuracy of 69.3 %. The SVM algorithm had an area under the ROC curve of 0.76 and an accuracy of 70.4 %, while the random forest algorithm had an area under the ROC curve of 0.75 and an accuracy of 67.6 %. There was no statistical difference between their diagnostic performance measured by AUC (XGBoost VS Support Vector Machine: p = 0.9; XGBoost VS Random Forest: p = 0.06). Fig. 2 shows the AUC of each model.

# 4. Discussion

To our knowledge this is one of the largest prospectively maintained database used to predict 3 months mRS after MT thanks to machine learning in the literature as well as the one with the largest number of centers included (20) thus representing at best the heterogeneity of practice in France. This study included real life selected patients who underwent MT for anterior circulation stroke with LVO, and it is to note

<table><caption>**Table 2**

Characteristics and comparison between training set and validation set.</caption><thead><tr><th>Features</th><th>Training set</th><th>Validation set</th><th>P value</th></tr></thead><tbody><tr><td>Patients, n</td><td>2365</td><td>599</td><td></td></tr><tr><td>Age, (median, IQR)</td><td>73 (61–82)</td><td>73 (62–82)</td><td>0.87</td></tr><tr><td>Comorbidities (n, %)</td><td></td><td></td><td></td></tr><tr><td>Hypertension</td><td>1422 (61 %)</td><td>356 (59 %)</td><td>0.36</td></tr><tr><td>Diabetes</td><td>400 (17 %)</td><td>94 (16 %)</td><td>0.33</td></tr><tr><td>Smoking</td><td>444 (20 %)</td><td>107 (18 %)</td><td>0.27</td></tr><tr><td>Coronary disease history</td><td>408 (18 %)</td><td>79 (13 %)</td><td>0.007 *</td></tr><tr><td>Dyslipidemia</td><td>709 (31 %)</td><td>191 (32 %)</td><td>0.64</td></tr><tr><td>Biological (median, IQR)</td><td></td><td></td><td></td></tr><tr><td>Weight (kg)</td><td>73 (62–84)</td><td>74 (65–84)</td><td>0.13</td></tr><tr><td>Glycemia (mmol/g)</td><td>6.7 (5.7–8.1)</td><td>6.7 (5.6–8)</td><td>0.15</td></tr><tr><td>SpO2 (%)</td><td>97 (95–99)</td><td>97 (95–99)</td><td>0.8</td></tr><tr><td>Heart Rate</td><td>77 (67–90)</td><td>77 (67–90)</td><td>0.63</td></tr><tr><td>Systolic Blood Pressure (mmHg)</td><td>145 (129–164)</td><td>147 (130–164)</td><td>0.04</td></tr><tr><td>Diastolic Blood Pressure (mmHg)</td><td>81 (71–92)</td><td>80 (70–92)</td><td>0.63</td></tr><tr><td>Intakes of antithrombotic (n, %)</td><td>1026 (45 %)</td><td>245 (41 %)</td><td>0.074</td></tr><tr><td>bMRI</td><td></td><td></td><td></td></tr><tr><td>Flair+ (n, %)</td><td>1203 (51 %)</td><td>277 (46 %)</td><td>0.46</td></tr><tr><td>ASPECTS (median, IQR)</td><td>8 (6–9)</td><td>8 (6–10)</td><td>&lt; 0.001 *</td></tr><tr><td>Occlusion type</td><td></td><td></td><td>0.17</td></tr><tr><td>M1</td><td>1303 (27 %)</td><td>354 (59 %)</td><td></td></tr><tr><td>M2</td><td>283 (12 %)</td><td>82 (14 %)</td><td></td></tr><tr><td>Distal ICA</td><td>345 (15 %)</td><td>72 (12 %)</td><td></td></tr><tr><td>Extra cranial ICA</td><td>38 (2 %)</td><td>8 (1 %)</td><td></td></tr><tr><td>Tandem</td><td>262 (12 %)</td><td>77 (13 %)</td><td></td></tr><tr><td>Others</td><td>15 (0.5 %)</td><td>0 (0 %)</td><td></td></tr><tr><td>Not visible</td><td>24 (1 %)</td><td>6 (1 %)</td><td></td></tr><tr><td>Pre-mRS (median, IQR)</td><td>0 (0–0)</td><td>0 (0–0)</td><td>0.4</td></tr><tr><td>NIHSS at admission (median, IQR)</td><td>17 (11–20)</td><td>16 (10–20)</td><td>0.14</td></tr><tr><td>Symptom onset to bMRI time (median, IQR)</td><td>120 (90–168)</td><td>126 (98–165)</td><td>0.02 *</td></tr><tr><td>IV-tPA (n, %)</td><td>1203 (51 %)</td><td>277 (46 %)</td><td>0.001 *</td></tr><tr><td>3-Months mRS ≤ 2</td><td>960 (41 %)</td><td>253 (42 %)</td><td>0.46</td></tr></tbody></table>
<table><caption>**Table 3**
Performances and comparison of each model.</caption><thead><tr><th>Algorithms</th><th>AUC</th><th>Accuracy (%)</th><th>Specificity / Sensitivity (%)</th><th>P value</th></tr></thead><tbody><tr><td>Support Vector Machine</td><td>0.76 (0.73–0.80)</td><td>70.4</td><td>73/66</td><td>0.9</td></tr><tr><td>Random Forest</td><td>0.75 (0.71–0.79)</td><td>67.6</td><td>69/65</td><td>0.06</td></tr><tr><td>XGBoost</td><td>0.77 (0.73–0.80)</td><td>69.3</td><td>72/65</td><td>ref</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Models performances.</figcaption></figure>

that nearly 40 % of these patients had a functional independence at 3 months. This binary cutoff at mRS ≤ 2 versus mRS > 2 is commonly used to distinguish patients who have achieved functional independence (no or slight disability) from those who experience significant disability or death. While mRS 0–2 is the most prevalent definition of favorable functional outcome, some investigators use mRS 0–3 as an alternative or supplementary endpoint that might better reflect real-world successes, especially in older or more frail populations. In certain contexts (e.g., extended time windows, elderly cohorts), achieving mRS ≤ 3 still represents a meaningful reduction in disability compared to the natural history of severe stroke (Nunn et al., 2016). The study also used a variety of accessible demographic, clinical, and biological features to predict clinical outcomes, and the five best features were identified as age, NIHSS, glycemia, ASPECTS, and premorbid mRS. Three different

machine learning algorithms were used to train the models, with XGBoost performing the best in terms of AUC and accuracy. The performances of the models were statistically similar.

The average AUC was 0.76 and the average accuracy was 69.1 %, which shows promising performances to predict a favorable clinical outcome based on mRS with data available at admission but is not sufficient to be used alone to help decision-making process due to the high number of incorrect predictions (around 30 %). Overall, performances are similar to previous studies reported in the literature, summarized in the Table 4 (Mainali et al., 2021).

In comparison with the meta-analyze of Zeng and al of 2022, which summarize ML pre-thrombectomy prognostic models for LVO stroke, [ourstudy is the largest sample size with 2964 patients analyzed versus 1401 patients for the second largest sample size (Monteiro et al., 2018; Zeng et al., 2022). Among the twelves studies found in the literature using conventional ML algorithms, the predicted outcomes were favorable functional outcomes at 90 days for 7 studies, poor functional outcomes for 3 studies and successful reperfusion at the first attempt for 2 studies. We examined ML-based outcome prediction models for patients with stroke who underwent MT, only for favorable outcome (mRS 0–2 vs. 3–6) measured at 3 months post stroke. These prediction paradigms have direct implications for clinical decision-making by predicting an outcome of no or only slight disability on the one hand and severe disability or death on the other. In particular, the definition of favorable outcome is generally accepted and was applied in large prospective studies. Some studies found considerable differences between the different outcome paradigms (Mutke et al., 2022). This choice of paradigm is highly debatable and further studies should examine this threshold, the used scales for the outcome, or the kind of output of the ML models.

Concerning the important features identified, nine among the twelve found NIHSS score, 8 age, six pre-morbid MRS. One third found ASPECTS and glucose as important feature. Duration of onset to groin puncture were an important feature in 5 studies among the twelve. In own study, the performances may be limited by the sole presence of clinical, biological, or MRI features only, and additional factors such as functionality and collaterality should be incorporated to improve accuracy. Nevertheless, in previous studies incorporating data concerning speed and extent of MT did improve prediction for a favorable outcome but was not relevant for poor outcome, and MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance in both context of prediction(Mutke et al., 2022)

Several conventional ML models achieved high performances values (AUCs: 0.86–0.93) but they were developed and validated in small

<table><caption>**Table 4**
machine learning outcome prediction models in the setting of MT for anterior ELVO associated stroke.</caption><thead><tr><th>Brugnara et al.</th><th>3 Months-mRS</th><th>GradientBoostingClassifier</th><th>1</th><th>246 (patients > 80 yo)</th><th>14 features, retrospective</th><th>AUC = 0.74</th></tr></thead><tbody><tr><td>Alawieh et al.</td><td>3 Months-mRS</td><td>Random Forest (RF), linear regression; comparing with AUC of NIHSS</td><td>1</td><td>146</td><td>12 features, prospective (no missing data)</td><td>AUC = 0.63 – 0.83</td></tr><tr><td>Nishi et al.</td><td>3 Months-mRS</td><td>Linear SVM, regularized logistic regression, RF; comparing ML with clinical sores</td><td>2</td><td>502</td><td>15 features, retrospective</td><td>AUC = 0.78 – 0.86</td></tr><tr><td>Van Os et al.</td><td>3-Months-mRS</td><td>SVM, RF, ANN, superlearner, logistic regression</td><td>16</td><td>1383 (Mr Clean registry)</td><td>93 features (selection based on prior knowledge), retrospective (registry)</td><td>AUC = 0.77 – 0.79</td></tr><tr><td>Monteiro et al.</td><td>3-Months-mRS</td><td>RF, SVM, XGBoost, LR, decision tree</td><td>1</td><td>425</td><td>49 features, retrospective (registry)</td><td>AUC = 0.79 – 0.81</td></tr><tr><td>Alaka et al.</td><td>3 Months-mRS</td><td>SVM, RF, C5.0, adaptive boost machine, regression tree, least absolute shrinkage, LASSO, LR</td><td>NS (Multi-center)</td><td>614</td><td>NS, retrospective (registry)</td><td>AUC = 0.66 – 0.71</td></tr><tr><td>Heo et al.</td><td>3 Months-mRS</td><td>DNN, RF, logistic regression</td><td>1</td><td>2329 (Exclusion of patients who underwent thrombolysis)</td><td>38 features, retrospective</td><td>AUC = 0.83 – 0.89</td></tr><tr><td>Jang et al.</td><td>3 Months-mRS</td><td>DL, SVM, RF, Xgboost, LR</td><td>10</td><td>6731, retrospective</td><td>18 features,</td><td>AUC = 0.86 – 0.87</td></tr><tr><td>Mutke et al.</td><td>3 Months-mRS</td><td>(GLM), Lasso, ElasticNet, tree boosting (Catboost implementation), (SVMC), Naive Bayes (NB), and a Multilayer Perceptron (MLP).</td><td>1</td><td>210</td><td>21 features</td><td>AUC = 0.67–0.73</td></tr></tbody></table>
dataset drawn from similar sampling frames (Alawieh et al., 2019).

In our study, 20 centers took part in the inclusion of patients, which is reproducible from the day-to-day management of the patients. In addition, we carried out an external validation. Only five models (26.3 %) reviewed were validated externally. The dataset was divided according to a temporal split: training on data from 2018–2019 and validating on data from 2020, was chosen over a random split to improve external validity. Clinically, it accounts for evolving practice patterns and guidelines (Nishi et al., 2019; Zeng et al., 2022). From a machine-learning perspective, it helps prevent data leakage and provides a more stringent test of external validity by simulating a future cohort. However, focusing on a single year for validation can reduce the representativeness of the dataset and risk year-specific anomalies; substantial changes in treatment practices between periods may also complicate generalizability (Banks and Marotta, 2007; Forestier et al., 2020; van Os et al., 2018). Indeed, the validation set had a higher proportion of patients with an ASPECTS score ≤ 6, suggesting more inclusive MT practice in more extended infarct cores in 2020 compared to 2018–2019, and there was a statistical difference between the two sets regarding IV-tPA use, also suggesting changes in practice in recent years (treating patients that had IV thrombolysis or not) (Rozes et al., 2022). Also, a statistically significant difference was observed in the history of diabetes and coronary disease between the two sets, this likely reflects more global variable changes in referral patterns.

The use of machine learning algorithms has several advantages in this context, it can handle high-dimensional datasets and identify nonlinear relationships between variables, which would be more difficult to analyze using traditional statistical methods. Nishi et al. reported the worst prognostic performance of standard statistical models, in contrary to van Os et al. that found no differences between ML algorithm and best performing logistic regression model (based on prior knowledge) in the MR CLEAN registry, to predict functional independence at 3 months of MT for LVO (Nishi et al., 2019; van Os et al., 2018). Our study supports the use of machine learning models for predicting stroke outcomes, especially considering that they can be self-taught with additional data, allowing for continuous improvement and adjustment.

We chose to use a set of unsorted features, because they were accessible in the acute phase before mechanical thrombectomy, to be used in clinical practice. It remains that these data are most likely already sorted because they were found to be relevant to obtain in the acute phase of the ischemic stroke. Therefore, there could be other potentially interesting parameters to consider that are not fully known. Additionally, our study only considered variables that are typically obtainable or evaluated in most cases, and the prediction might be slightly influenced by the availability of these variables when incorporating data from other centers or kind of practice.

The retrospective nature of the study may have introduced some selection bias. Some features had a high percentage of missing data, which may have affected the accuracy of the machine learning models, especially using data imputation. This is the case of the biological data, while average values remained relatively stable, missing data rates were higher. Nonetheless, our imputation strategies for the training set helped ensure no systematic difference in the final analysis. Finally, the study only used clinical, biological, and imaging data to predict clinical outcomes, which may not be sufficient to define the finesse of the precise neurological state and the capacity of patients to recover.

XGBoost is a type of decision tree-based algorithm that works by iteratively building a sequence of weak models and combining them to form a stronger model. Each weak model is trained to predict the residuals of the previous model, and the final prediction is obtained by summing the predictions of all the weak models. Random Forest, on the other hand, is an ensemble learning method that uses a collection of decision trees to make predictions, it is known for its ability to handle noisy or incomplete data and can handle both categorical and continuous data. However, Random Forest can suffer from overfitting, may require more training data to achieve optimal performance and can be

biased toward categorical features with many levels. SVM can handle both linearly separable and non-linearly separable data by using different kernel functions. It is less prone to overfitting than other machine learning algorithms, as it seeks to maximize the margin between the decision boundary and the data points. It is particularly adapted while dealing with high-dimensional data. The combination of these three algorithms provided a comprehensive and accurate analysis of the dataset. Another approach is also to combine multiple algorithms to create an ensemble model. This can help to reduce the variance in the predictions and improve the overall performance of the model.

The choice of hyperparameters can have a significant impact on the performance of the model, and the optimal values may vary depending on the specific dataset. Here we used a grid search approach to optimize the hyperparameters models, which involves testing different combinations of hyperparameters to find the best-performing model. This approach can be computationally intensive, but it allows for a systematic exploration of the hyperparameter space and can lead to better performance than using default hyperparameters.

The internal validity of the study was ensured using appropriate statistical methods and a well-defined population. The selection of the training and validation sets further improved the internal validity of the study. However, the external validity of the study may be limited since the study was conducted in a specific geographic location. Further studies in different populations and geographical locations may be necessary to confirm the findings of this study.

The sole feature that had multiple class and could introduce bias while dealing with imbalanced classes was the clot location. The models could have suffered from a reduction of the accuracy due to this categorization; oversampling the minority class or using cost-sensitive learning algorithms are proposed solution to handle imbalanced classes.

Interestingly, the dataset was constituted of data from patients that were selected for MT. Thus, the direct implications of these results may be that these features are only the representatives of these situation, and not the one of the cerebral revascularization. To have a more impact of the clinical decision level, further studies should focus on incorporating a control group, without MT. Hence the feature would be able to reveal maybe impactful features to now when selecting patient for MT in debatable situations regarding the literature knowledge. Also, data were real life and not fully indication for MT.

In conclusion, we developed machine learning models that predicted the clinical outcomes of patients with acute ischemic stroke who underwent MT using mixed data available at admission. The models demonstrated satisfactory performance, with an average AUC of 0.76 and an average accuracy of 69.1 %. The models' performance was limited by the availability of clinical and imaging data only, and further studies are needed to incorporate additional features to improve the accuracy of the models. Our study represents an important step toward the development of clinical decision support tools that can help clinicians make more informed treatment decisions for patients with acute ischemic stroke.

## Declaration of Competing Interest

Authors declare no conflict of interest.

## Appendix A. List of ETIS Investigators

Bertrand Lapergue, MD,1 Adrien Wang, MD,1 Arturo Consoli, MD,1 Oguzhan Coskun, MD,1 Federico Di Maria, MD,1 Silvia Pizzuto, MD,1 Alessandro Sgreccia, MD,1 Charline Benoit, MD,1 Lucas Gorza, MD,1 David Weisenburger-Lile, MD,1 Waliyde Jabeur, MD,1 Tchikviladze Maia, MD,1 Serge Evrard, MD,1 Georges Rodesch, MD,1 Raphaël Blanc, MD,2 Michel Obadia, MD,2 Jean-Philippe Desilles, MD,2 Michel Piotin, MD,2 Pierre Seners, MD,2 Stanislas Smajda, MD,2 Simon Escalard, MD,2 Benjamin Maier, MD,2 Candice Sabben, MD,2 Hocine Redjem, MD,2 Mikael Mazhigi, MD,2 Grace Adwane, MD,2 François Delvoye, MD,2
Amira Al Raaisi isseau, MD,2 Altayeb Adnan, MD,2 Malayer Thomas, MD,2 Robichon Erwan, MD,2 Romain Bourcier, MD,3,4 Benjamin Daumas Duport, MD,3,4 Pierre Louis Alexandre, MD,3,4 Cédric Lenoble, MD,3,4 Hubert Desal, MD,3,4 Solene De Gaalon, MD,3,4 Benoît Guillon, MD,3,4 Cécile Preterre, MD,3,4 Guillaume Tessier, MD,3,4 Arthur Lionnet, MD,3,4 Benjamin Gory, MD,5 Lisa Humbertjean-Selton, MD,5 René Anxionnat, MD,5 Anne-Laure Derelle, MD,5 Liang Liao, MD,5 Emmanuelle Schmitt, MD,5 Sophie Planel, MD,5 Sébastien Richard, MD,5 Gioia Mione, MD,5 Jean-Christophe Lacour, MD,5 Marian Douarinou, MD,5 Emilien Micard, MD,5 Bailiang Chen, MD,5 Gérard Audibert, MD,5 Agnès Masson, MD,5 Lionel Alb, MD,5 Marine Beaufmont, MD,5 Adriana Tabarna, MD,5 Marcela Voicu, MD,5 Grégoire Barthel, MD,5 Iona Podar, MD,5 Madalina Brezeanu, MD,5 Marie Reiter, MD,5 François Zhu, MD,5 Anxionnat René, MD,5 Gaultier Marnat, MD,6 Jean-Sébastien Liegey, MD,6 Pierre Briau, MD,6 Lisa Papillon, MD,6 Igor Sibon, MD,6 Xavier Barreau, MD,6 Jean Papaxanthos, MD,6 Jérôme Berge, MD,6 Sabrina Debruxelles, MD,6 Stéphane Olindo, MD,6 Mathilde Poli, MD,6 Pauline Renou, MD,6 Sharmila Sagnier, MD,6 Thomas Tourdias, MD,6 Thomas Courret, MD,6 Ludovic Lucas, MD,6 Malgorzata Milnerowicz, MD,6 Cyril Dargazanli, MD,7 Vincent Costalat, MD,7 Isabelle Mourand, MD,7 Caroline Arquizan, MD,7 Lucas Corti, MD,7 Adrien Ter Schiphorst, MD,7 Cagnazzo Federico, MD,7 Imad Derraz, MD,7 Mehdi Mahmoudi, MD,7 Pierre-Henri Lefevre, MD,7 Grégory Gascou, MD,7 Quentin Varnier, MD,7 Laurent Spelle, MD,8 Jildaz Caroff, MD,8 Christian Denier, MD,8 Vanessa Chalumeau, MD,8 Cristian Mihalea, MD,8 Nicolas Legris, MD,8 Augustin Ozanne, MD,8 Leon Ikka, MD,8 Olivier Chassin, MD,8 Sophie Gallas, MD,8 Laura Venditti, MD,8 Mariana Sarov, MD,8 Jonathan Cortese, MD,8 Jean-Christophe Ferre, MD,9 Stephane Vannier, MD,9 Thomas Ronziere, MD,9 Maria Veronica Lassalle, MD,9 Jean-Yves Gauvrit, MD,9 Clement Tracol, MD,9 Abdelghani Fakhreddine Boustia, MD,9 Cecile Malrain, MD,9 Edouard Beaufreton, MD,9 Thibault Lapotre, MD,9 Quentin Alias, MD,9 Julien Hissier, MD,9 Maud Guillen, MD,9 François Eugene, MD,9 Boucherit Julien, MD,9 Cyril Chivot, MD,10 Audrey Courselle, MD,10 Elisa Ouin, MD,10 Chantal Lamy, MD,10 Kevin Delaforge, MD,10 Manuel Fernandez, MD,10 Jeremie Vial, MD,10 Quentin Laferte, MD,10 Xavier Desdoit, MD,10 Serge Timsit, MD,11 Aurore Jourdain, MD,11 Jean-Christophe Gentric, MD,11 Julien Ognard, MD,11 Irina Viakhireva, MD,11 Jordan Coris, MD,11 Sabine Prud'hon, MD,11 François-Mathias Merrien, MD,11 Denis Marechal, MD,11 Marie Bruguet, MD,11 Pierre Yves Rousseau, MD,11 Philippe Goas, MD,11 Elise Retho, MD,11 Caroline Lavenant, MD,11 Rozenn Le Gal, MD,11 Leblanc Amelie, MD,11 Marion Boulanger, MD,12 Emmanuel Touze, MD,12 Denis Vivien, MD,12 Charlotte Barbier, MD,12 Romain Schneckenburger, MD,12 Fabrizio Salaris, MD,12 Julien Cogez, MD,12 Sophie Guettier, MD,12 Estelle La Porte, MD,12 Jean Bouchart, MD,12 Charbel Mounayer, MD,13 Aymeric Rouchaud, MD,13 Suzana Saleme, MD,13 Géraud Forestier, MD,13 Frédéric Clarendon, MD,14 Charlotte Rosso, MD,14 Sara Leder, MD,14 Flore Baronnet, MD,14 Sophie Crozier, MD,14 Anne Leger, MD,14 Kevin Premat, MD,14 Shotar Eimad, MD,14 Stéphanie Lenck, MD,14 Nader Sourour, MD,14 Laure Bottin, MD,14 Sam Ghazanfari, MD,14 Marion Yger, MD,14 Sonia Alamowitch, MD,14 Stephen Delorme, MD,14 Aymeric Wittwer, MD,14 Christine Vassilev, MD,14 Allard Julien, MD,14 Olivier Naggara, MD,15 Guillaume Turc, MD,15 Wagih Ben Hassen, MD,15 Basile Kerleroux, MD,15 Denis Trystram, MD,15 Christine Rodriguez-Regent, MD,15 Finoana Lazonenjanahary, MD,15 Ozlem Ozkul-Wermester, MD,16 Chrysanthi Papagiannaki, MD,16 Evelyne Massardier, MD,16 Aude Triquenot, MD,16 Margaux Lefebvre, MD,16 Julien Burel, MD,16 Basille Florient, MD,16 Alain Viguier, MD,17 Christophe Cognard, MD,17 Anne Christine Januel, MD,17 Jean-François Albucher, MD,17 Lionel Calviere, MD,17 Jean-Marc Olivot, MD,17 Jean Darcourt, MD,17 Nicolas Raposo, MD,17 Fabrice Bonneville, MD,17 Guillaume Bellanger, MD,17 Louis Fontaine, MD,17 Philippe Tall, MD,17 Frédéric Bourdain, MD,18 Patricia Bernady, MD,18 Guillaume Ballan, MD,18 Stephanie Bannier, MD,18 Emmanuel Ellie, MD,18 Olivier Flabeau, MD,18 Julia Potenza,

MD,18 Antoine Soulages, MD,18 Laurent Lagoarde-Segot, MD,18 Hélène Cailliez, MD,18 Louis Veunac, MD,18 David Higue, MD,18 Anthony Lebras, MD,19 Sarah Adam, MD,19 Benoit Pegat, MD,19 Arnaud Le Guen, MD,19 François Chedeville, MD,19 Jérémy Jouan, MD,19 Allibert Rémi, MD,19 Alexanre Courtois, MD,19 Sarah Evain, MD,19 Lescot Lucile, MD,19 Stéphanie Demasles, MD,20 Johann Sebastian Richter, MD,20 Bruno Thierry Barroso, MD,20 Camille Dahan, MD,20 Alexis Gonnet, MD,20 Régis Hubrecht, MD,20 Zoé Lepine, MD,20 Hélène Castagnet, MD,20 Raluca Marasescu, MD,20 Olivier Heck, MD,21 Pauline Cuisenier, MD,21 Olivier Detante, MD,21 Isabelle Favre Wiki, MD,21 Clémentine Bonaz, MD,21 Garambois Katia, MD,21 Loic Legris, MD,21 Adrian Kastler, MD,21 Kamel Boubagra, MD,21 Corentin Berthet, MD,21 Stéphane Charara, MD,21 Victor Dumas, MD,22 Neau Jean Philippe, MD,22 Velasco Stéphane, MD,22 Valérie Wolff, MD,23 Raoul Pop, MD,23 Véronique Quenardelle, MD,23 Valérie Lauer, MD,23 Irène Pierre-Paul, MD,23 Roxana Gheoca, MD,23 Malwina Trzeciak, MD,23 Bindila Perisse Daniella, MD,23 Bonnart Camille, MD,23 Solène Moulin, MD,24 Hua Vi Tuan, MD,24 Paolo Pagano, MD,24 Alexandre Doucet, MD,24 Christophe Gelmini, MD,24 Pierre-François Manceau, MD,24 Laurentiu Pajusan, MD,24 Isabelle Serre, MD,24 Sébastien Soize, MD,24 Thi Ngoc Phuong Nguyen, MD,24 Maher Sahnoun, MD,24 Nathalie Caucheteux, MD,24 Pablo Lebindsky, MD,25 Nader Vanessa, MD,25 Federico Bolignini, MD,25 Francis Vuillemet, MD,25 José Rodrigues, MD,25 Anna Ferrier, MD,26 Abderrahim Zerroug, MD,26 Ricardo Moreno, MD,26 Emmanuel Chabert, MD,26 Elie Lteif, MD,26 Pauline Paris, MD,26 Nathalie Bourgois, MD,26 Marie Raquin, MD,26 Anne Pasco-Papon, MD,27 Jean Baptiste Girot, MD,27 Alderic Lecluse, MD,27 Sophie Godard, MD,27 Vincent L'Allinec, MD,27 Corfu Alice, MD,27 Kevin Janot, MD,28 Richard Bibi, MD,28 Marie Gaudron, MD,28 Arnaud Bretonniere, MD,28 Mariam Annan, MD,28 Héloïse Ifergan, MD,28 Grégoire Boulouis, MD,28 Marco Pasi, MD,28 Séverine Debiais, MD,28 Elisabeth Molinier, MD,28 Denis Herbretau, MD,28 Romain Doung, MD,28 Fouzi Bala, MD,28 Clemencie Hoche, MD,28 Anthony Wietrich, MD,29 Valérie Ruche, MD,29 Karine Lavandier, MD,29 Yannick Bejot, MD,30 Brivale Lemogne, MD,30 Fédéric Ricolfi, MD,30 Laura Baptiste, MD,30 Pierre Thouant, MD,30 Gaulthier Duloquin, MD,30 Pierre Olivier Comby, MD,30 Guillaume Charbonnier, MD,31 Louise Bonnet, MD,31 Nicolas Raybaud, MD,31 Benjamin Bouamra, MD,31 Thierry Moulin, MD,31 Alessandra Biondi, MD,31 Pico Fernando, MD,32 Dano Benjamin, MD,32 Patcas Carmen, MD,32 Chaari Dhouha, MD,32 Stanciu Daniela, MD,32 Duang Duc Long, MD,32 Hmida Intissar, MD,32 Marie-Laure Chadenat, MD,32 Suissa Laurent, MD,33 Doche Emilie, MD,33 Hak Jean-François, MD,33 Reyre Anthony, MD,33 Carle Xavier, MD,33 Laksiri Nadia, MD,33 Osman Ophélie, MD,33 Peres Roxane, MD,33 Robinet-Borgomano Emmanuelle, MD,33 Dulau Cécile, MD,33 Kerleroux Basile, MD,33 Rey Caroline, MD,33 Basile Kerleroux, MD,34 Jérôme Berge, MD,34 Fabrice Bing, MD,35 and Mathieu Bonnerot, MD35

1Departments of Neurology and Neuroradiology, Foch Hospital, Versailles Saint-Quentin en Yvelines University, Suresnes, France; 2Departments of Neurology and Neuroradiology, Rothschild Foundation, Paris, France; 3Departments of Neurology and Neuroradiology, University Hospital of Lyon, France; 4Departments of Neurology and Neuroradiology, CHU Nantes, France; 5Departments of Neurology and Neuroradiology, University Hospital of Nancy, France; 6Departments of Neurology and Neuroradiology, University Hospital of Bordeaux, France; 7Departments of Neurology and Neuroradiology, CHU Montpellier, France; 8Departments of Neurology and Neuroradiology, CHU Kremlin Bicêtre, Paris, France; 9Departments of Neurology and Neuro-radiology, CHU Rennes, France; 10Departments of Neurology and Neuroradiology, CHU Amiens, France; 11Departments of Neurology and Neuroradiology, CHU Brest, France; 12Departments of Neurology and Neuroradiology, CHU Caen, France; 13Departments of Neurology and Neuroradiology, CHU Limoges, France; 14Departments of Neurology and Neuroradiology, CHU Pitié-Salpétrière, Paris, France; 15Departments of Neurology and Neuroradiology, Hôpital Saint Anne, Paris, France; 16Departments of Neurology and Neuroradiology, CHU Rouen,
France; 17Departments of Neurology and Neuroradiology, CHU Toulouse, France; 18Departments of Neurology and Radiology, CH Bayonne, France; 19Departments of Neurology and Radiology, CH Vannes, France; 20Departments of Neurology and Radiology, CH Pau, France; 21Departments of Neurology and Neuroradiology, CHU Grenoble, France; 22Departments of Neurology and Neuroradiology, CHU Poitiers, France; 23Departments of Neurology and Neuroradiology, CHU Strasbourg, France; 24Departments of Neurology and Neuroradiology, CHU Reims, France; 25Departments of Neurology and Neuroradiology, CH Colmar, France; 26Departments of Neurology and Neuroradiology, CHU Clermont-Ferrand, France; 27Departments of Neurology and Neuroradiology, CHU Angers, France; 28Departments of Neurology and Neuroradiology, CHU Tours, France; 29Departments of Neurology, CH Bar-le-Duc, France; 30Departments of Neurology and Neuroradiology, CHU Dijon, France; 31Departments of Neurology and Neuroradiology, CHU Besançon, France; 32Department of Neurology, CH Versailles, France; 33Departments of Neurology and Neuroradiology, CHU Mar-seille, France; 34Departments of Neurology and Radiology, CH Bastia, Corse, France; and 35Departments of Neurology and Radiology, CH Annecy, France.

## Appendix B. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jneumeth.2025.110376.

## Data availability

Data will be made available on request.

## References

Abdelrady, M., Derraz, I., Dargazanli, C., Cheddad El Aouni, M., Lefevre, P.-H., Cagnazzo, F., Riquelme, C., Gascou, G., Arquizan, C., Mourand, I., Ben Salem, D., Costalat, V., Gentric, J.-C., Ognard, J., 2023. Complete recanalization predicts favorable outcome in patients with distal M2-M3 middle cerebral artery occlusions following endovascular thrombectomy. J. Neuroradiol. 50, 230–236. https://doi.org/10.1016/j.neurad.2022.11.007.

Alaka, S.A., Menon, B.K., Robbey, A., Williamson, T., Goyal, M., Demchuk, A.M., Hill, M.D., Sajobi, T.T., 2020. Functional outcome prediction in ischemic stroke: a comparison of machine learning algorithms and regression models. Front. Neurol. 11, 889. https://doi.org/10.3389/fneur.2020.00889.

Alawieh, A., Zaraket, F., Alawieh, M.B., Chatterjee, A.R., Spiotta, A., 2019. Using machine learning to optimize selection of elderly patients for endovascular thrombectomy. J. Neurointerv Surg. 11, 847–851. https://doi.org/10.1136/neurintsurg-2018-014381.

Bai, X., Qiu, J., Wang, Y., 2023. Endovascular thrombectomy with or without intravenous alteplase in acute stroke: a systematic review and meta-analysis of randomized clinical trials. J. Neurol. 270, 223–232. https://doi.org/10.1007/s00415-022-11413-3.

Banks, J.L., Marotta, C.A., 2007. Outcomes validity and reliability of the modified rankin scale: implications for stroke clinical trials: a literature review and synthesis. Stroke 38, 1091–1096. https://doi.org/10.1161/01.STR.0000258355.23810.c6.

Broocks, G., Hanning, U., Bechstein, M., Elsayed, S., Faizy, T.D., Brekenfeld, C., Flottmann, F., Kniep, H., Deb-Chatterji, M., Schön, G., Thomalla, G., Kemmling, A., Fiehler, J., Meyer, L., German Stroke Registry-Endovascular Treatment (GSR-ET) Study Group, 2022. Association of thrombectomy with functional outcome for patients with ischemic stroke who presented in the extended time window with extensive signs of infarction. JAMA Netw. Open 5, e2235733. https://doi.org/10.1001/jamanetworkopen.2022.35733.

Brugnara, G., Neuberger, U., Mahmutoglu, M.A., Foltyn, M., Herweh, C., Nagel, S., Schönenberger, S., Heiland, S., Ulfert, C., Ringleb, P.A., Bendszus, M., Möhlenbruch, M.A., Pfaff, J.A.R., Vollmuth, P., 2020. Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning. Stroke 51, 3541–3551. https://doi.org/10.1161/STROKEAHA.120.030287.

Dumitriu LaGrange, D., Hofmeister, J., Rosi, A., Vargas, M.I., Wanke, I., Machi, P., Lövblad, K.-O., 2023. Predictive value of clot imaging in acute ischemic stroke: a systematic review of artificial intelligence and conventional studies. Neurosci. Inform. 3, 100114. https://doi.org/10.1016/j.neuri.2022.100114.

Forestier, G., Kerleroux, B., Janot, K., Zhu, F., Dumas, V., Hak, J.-F., Shotar, E., Ben Hassen, W., Bourcier, R., Soize, S., Berge, J., Naggara, O., Desal, H., Boulouis, G., Rouchaud, A., JENI Research collaboration, 2020. Mechanical thrombectomy

practices in France: exhaustive survey of centers and individual operators. J. Neuroradiol. 47, 410–415. https://doi.org/10.1016/j.neurad.2020.05.001.

Goyal, M., Menon, B.K., van Zwam, W.H., Dippel, D.W.J., Mitchell, P.J., Demchuk, A.M., Dávalos, A., Majoie, C.B., van der Lugt, A., de Miquel, M.A., Donnan, G.A., Roos, Y. B., Bonafe, A., Jahan, R., Diener, H.C., van den Berg, L.A., Levy, E.I., Berkhemer, O. A., Pereira, V.M., Rempel, J., Millán, M., Davis, S.M., Roy, D., Thornton, J., Román, L.S., Ribó, M., Beumer, D., Stouch, B., Brown, S., Campbell, B.C., van Oostenbrugge, R.J., Saver, J.L., Hill, M.D., Jovin, T.G., HERMES collaborators, 2016. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387, 1723–1731. https://doi.org/10.1016/S0140-6736(16)00163-X. Epub 2016 Feb 18. PMID: 26898852.

Heo, J., Yoon, J.G., Park, H., Kim, Y.D., Nam, H.S., Heo, J.H., 2019. Machine learning-based model for prediction of outcomes in acute stroke. Stroke 50, 1263–1265. https://doi.org/10.1161/STROKEAHA.118.024293.

Hu, Y., Yang, T., Zhang, J., Wang, X., Cui, X., Chen, N., Zhou, J., Jiang, F., Zhu, J., Zou, J., 2022. Dynamic prediction of mechanical thrombectomy outcome for acute ischemic stroke patients using machine learning. Brain Sci. 12, 938. https://doi.org/10.3390/brainsci12070938.

Lu, S., Zhang, J., Wu, R., Cao, Y., Xu, X., Li, G., Liu, S., Shi, H., Wu, F., 2022. Use of machine learning algorithms to predict the outcomes of mechanical thrombectomy in acute ischemic stroke patients with an extended therapeutic time window. J. Comput. Assist Tomogr. 46, 775–780. https://doi.org/10.1097/RCT.0000000000001341.

Mainali, S., Darsie, M.E., Smetana, K.S., 2021. Machine learning in action: stroke diagnosis and outcome prediction. Front Neurol. 12, 734345. https://doi.org/10.3389/fneur.2021.734345.

Meyer, L., Politi, M., Alexandrou, M., Roth, C., Kastrup, A., Mpotsaris, A., Hanning, U., Flottmann, F., Brekenfeld, C., Deb-Chatterji, M., Thomalla, G., Kniep, H., Faizy, T.D., Bechstein, M., Broocks, G., Herzberg, M., Feil, K., Kellert, L., Dorn, F., Zelenák, K., Fiehler, J., Papanagiotou, P., German stroke registry-endovascular treatment (GSR-ET), german stroke registry- endovascular treatment (GSR-ET), 2023. Endovascular treatment of acute tandem lesions in patients with mild anterior circulation stroke. J. Neurointerv Surg. 15, e136–e141. https://doi.org/10.1136/jnis-2022-019239.

Monteiro, M., Fonseca, A.C., Freitas, A.T., Pinho E Melo, T., Francisco, A.P., Ferro, J.M., Oliveira, A.L., 2018. Using machine learning to improve the prediction of functional outcome in ischemic stroke patients. IEEE/ACM Trans. Comput. Biol. Bioinform 15, 1953–1959. https://doi.org/10.1109/TCBB.2018.2811471.

Mutke, M.A., Madai, V.I., Hilbert, A., Zihni, E., Potreck, A., Weyland, C.S., Möhlenbruch, M.A., Heiland, S., Ringleb, P.A., Nagel, S., Bendszus, M., Frey, D., 2022. Comparing poor and favorable outcome prediction with machine learning after mechanical thrombectomy in acute ischemic stroke. Front. Neurol. 13, 737667. https://doi.org/10.3389/fneur.2022.737667.

Nishi, H., Oishi, N., Ishii, A., Ono, I., Ogura, T., Sunohara, T., Chihara, H., Fukumitsu, R., Okawa, M., Yamana, N., Imamura, H., Sadamasa, N., Hatano, T., Nakahara, I., Sakai, N., Miyamoto, S., 2019. Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning. Stroke 50, 2379–2388. https://doi.org/10.1161/STROKEAHA.119.025411.

Nunn, A., Bath, P.M., Gray, L.J., 2016. Analysis of the modified rankin scale in randomised controlled trials of acute ischaemic stroke: a systematic review. Stroke Res. Treat. 2016, 9482876. https://doi.org/10.1155/2016/9482876.

Obermeyer, Z., Emanuel, E.J., 2016. Predicting the future - big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219. https://doi.org/10.1056/NEJMp1606181.

van Os, H.J.A., Ramos, L.A., Hilbert, A., van Leeuwen, M., van Walderveen, M.A.A., Kruyt, N.D., Dippel, D.W.J., Steyerberg, E.W., van der SchAAF, I.C., Lingsma, H.F., Schonewille, W.J., Majoie, C.B.L.M., Olabarriaga, S.D., Zwinderman, K.H., Venema, E., Marquering, H.A., Wermer, M.J.H., CLEAN, M.R., Investigators, Registry, 2018. Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front Neurol. 9, 784. https://doi.org/10.3389/fneur.2018.00784.

Pilgram-Pastor, S.M., Piechowiak, E.I., Dobrocky, T., Kaesmacher, J., Den Hollander, J., Gralla, J., Mordasini, P., 2021. Stroke thrombectomy complication management. J. Neurointerv Surg. 13, 912–917. https://doi.org/10.1136/neurintsurg-2021-017349.

Sarraj, A., Hassan, A., Savitz, S.I., Grotta, J.C., Cai, C., Parsha, K.N., Farrell, C.M., Imam, B., Sitton, C.W., Reddy, S.T., Kamal, H., Goyal, N., Elijovich, L., Reishus, K., Krishnan, R., Sangha, N., Wu, A., Costa, R., Malik, R., Mir, O., Hasan, R., Snodgrass, L.M., Requena, M., Graybeal, D., Abraham, M., Chen, M., McCullough, L. D., Ribo, M., 2018. Endovascular thrombectomy for mild strokes: how low should we go? Stroke 49, 2398–2405. https://doi.org/10.1161/STROKEAHA.118.022114.

Siddiqui, Z., Bhatia, K., Corbin, A., Dhar, R., 2023. Rules-based natural language processing to extract features of large vessel occlusion and cerebral edema from radiology reports in stroke patients. Neurosci. Inform. 3, 100129. https://doi.org/10.1016/j.neuri.2023.100129.

Stevens, L.M., Mortazavi, B.J., Deo, R.C., Curtis, L., Kao, D.P., 2020. Recommendations for reporting machine learning analyses in clinical research. Circ. Cardiovasc Qual. Outcomes 13, e006556. https://doi.org/10.1161/CIRCOUTCOMES.120.006556.

Zeng, M., Oakden-Rayner, L., Bird, A., Smith, L., Wu, Z., Scroop, R., Kleinig, T., Jannes, J., Jenkinson, M., Palmer, L.J., 2022. Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: a systematic review and meta-analysis. Front. Neurol. 13, 945813. https://doi.org/10.3389/fneur.2022.945813.