An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment. (June 2022)

• Record Type: Journal Article
• Authors: Lu, Han; Xue, Gaici; Li, Sisi; Mu, Yangjiayi; Xu, Yi; Hong, Bo; Huang, Qinghai; Li, Qiang; Yang, Pengfei; Zhao, Rui; Fang, Yibin; Luo, Qiang; Zhou, Yu; Liu, Jianmin
• Is Part Of: Therapeutic advances in neurological disorders. Volume 15(2022)
• Publication Date: 2022-06
• Subjects: endovascular treatment -- imbalanced data -- machine learning -- prognostic prediction -- reinforcement learning; Nervous system -- Diseases -- Periodicals; Nervous system -- Degeneration -- Periodicals; Nervous system -- Diseases -- Treatment -- Periodicals; Nervous System Diseases -- therapy -- Periodicals; Neurodegenerative Diseases -- Periodicals; Système nerveux -- Maladies -- Périodiques; Système nerveux -- Dégénérescence -- Périodiques; Système nerveux; Système nerveux -- Maladies -- Traitement -- Périodiques; 616.805
• DOI: 10.1177/17562864221099473
• Languages: English
• ISSNs: 1756-2856

Abstract

Background: Endovascular treatment for aneurysmal subarachnoid hemorrhage (aSAH) has high fatality and permanent disability rates. It remains unclear how the prognosis is determined by the complex interaction between clinical severity and aneurysm characteristics. Objective: This study aimed to design an accurate prognostic prediction model for aSAH patients after endovascular treatment and elucidate the interaction between clinical severity and aneurysm characteristics. Methods: We used a clinically homogeneous data set with 1029 aSAH patients who received endovascular treatment to develop prognostic models. Aneurysm characteristics were measured by variables, such as aneurysm size, neck size, and dome-to-neck ratio, while clinical severity on admission was measured by both comorbidities and neurological condition. In total, 18 clinical variables were used for prognostic prediction. Considering the imbalance between the favorable and the poor outcomes in this clinical population, both ensemble learning and deep reinforcement learning approaches were used for prediction. Results: The random forest (RF) model was selected as the best approach for the prognostic prediction for all patients and also for patients with good-grade aSAH. Using an independent test data set, the model made accurate predictions (AUC = 0.869 ± 0.036, sensitivity = 0.709 ± 0.087, specificity = 0.805 ± 0.034) with the clinical severity on admission as a leading contributor to the prediction. For patients with good-grade aSAH, the RF model performed the best (AUC = 0.805 ± 0.034, sensitivity = 0.620 ± 0.172, specificity = 0.696 ± 0.043) with aneurysm characteristics as leading contributors. The classic scoring systems failed in this patient group (AUC < 0.600; sensitivity = 0.000, specificity = 1.000). Conclusion: The proposed prognostic prediction model outperformed the classic scoring systems for patients with aSAH after endovascular treatment, especially when the classic scoring systems failed to make any informative prediction for patients with good-grade aSAH, who constitute the majority group (79%) of this clinical population.