The Influence of Body Mass Index on Outcomes in Patients Undergoing Mechanical Thrombectomy for Anterior Circulation Large Vessel Occlusion: Institutional Experience and Meta-analysis.

BACKGROUND: An obesity paradox, whereby patients with higher body mass index (BMI) experience improved outcomes, has been described for ischemic stroke. It is unclear whether this applies to patients undergoing mechanical thrombectomy (MT) for large vessel occlusion (LVO). METHODS: Mechanical thrombectomies for anterior circulation LVO between 2015 and 2021 at a single institution were reviewed. Multivariable logistic regressions were used to determine the association between BMI and favorable functional outcome (90-day modified Rankin Scale 0-2), intracranial hemorrhage, and malignant middle cerebral infarction. A systematic review was performed to identify studies reporting the effect of BMI on outcomes among patients receiving MT for LVO. The data from the systematic review were combined with the institutional data by using a random effects model. RESULTS: The institutional cohort comprised 390 patients with a median BMI of 27 kg/m2. Most patients were obese [36.7% (BMI ≥ 30 kg/m2)], followed by overweight [30.5% (BMI ≥ 25 and < 30 kg/m2)], normal [27.9% (BMI ≥ 18.5 and < 25 kg/m2)], and underweight [4.9% (BMI < 18.5 kg/m2)]. As a continuous variable, BMI was not associated with any of the outcomes. When analyzing BMI ordinally, obesity was associated with lower odds of favorable 90-day modified Rankin Scale (odds ratio 0.42, 95% confidence interval 0.20-0.86). The systematic review identified three eligible studies comprising 1,348 patients for a total of 1,738 patients. In the random effects model, there was no association between obesity and favorable outcome (odds ratio 0.89, 95% confidence interval 0.63-1.24). CONCLUSIONS: Obesity is not associated with favorable outcomes in patients undergoing MT for LVO.

Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion.

OBJECTIVE: Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO. METHODS: All patients who underwent MT for ACLVO between 2015 - 2021 at a single institution were reviewed. Data was divided into 80% training and 20% test sets. 10 models were evaluated on the training set. The top 3 models underwent hyperparameter tuning using grid search with nested 5-fold CV to optimize the area under the receiver operating curve (AUROC). Tuned models were evaluated on the test set and compared to logistic regression. RESULTS: A total of 381 patients met the inclusion criteria. There were 50 (13.1%) patients who developed MMI. Out of the 10 ML models screened on the training set, the top 3 performing were neural network (median AUROC 0.78, IQR 0.72 - 0.83), support vector machine ([SVM] median AUROC 0.77, IQR 0.72 - 0.83), and random forest (median AUROC 0.75, IQR 0.68 - 0.81). On the test set, random forest (median AUROC 0.78, IQR 0.73 - 0.83) and neural network (median AUROC 0.78, IQR 0.73 - 0.83) were the top performing models, followed by SVM (median AUROC 0.77, IQR 0.70 - 0.83). These scores were significantly better than those for logistic regression (AUROC 0.72, IQR 0.66 - 0.78), individual risk factors, and the Malignant Brain Edema score (p < 0.001 for all). CONCLUSION: ML models predicted MMI with good discriminative ability. They outperformed standard statistical techniques and individual risk factors.