Cavernous Malformations and Artificial Intelligence: Machine Learning Applications

Significant progress has been made in the use of artificial intelligence (AI) in clinical medicine over the past decade, but the clinical development of AI faces challenges. Although the spectrum of AI applications is growing within clinical medicine, including in subspecialty neurosurgery, applications focused on cerebral cavernous malformations (CCMs) are relatively scarce. The recently introduced brainstem cavernous malformation (BSCM) grading scale, approach triangles, and safe entry zone systems provide a discrete framework to explore future machine learning (ML) applications of AI systems. Given the immense scalability of these models, significant resources will likely be allocated to pursuing these future efforts

Frailty in Aneurysmal Subarachnoid Hemorrhage: The Risk Analysis Index

BACKGROUND: Few studies have evaluated frailty in the setting of aneurysmal subarachnoid hemorrhage (aSAH) using large-scale data. The risk analysis index (RAI) may be implemented at the bedside or assessed retrospectively, differentiating it from other indices used in administrative registry-based research. METHODS: Adult aSAH hospitalizations were identified in the National Inpatient Sample (NIS) from 2015 to 2019. Complex samples statistical methods were performed to evaluate the comparative effect size and discriminative ability of the RAI, the modified frailty index (mFI), and the Hospital Frailty Risk Score (HFRS). Poor functional outcome was determined by the NIS-SAH Outcome Measure (NIS-SOM), shown to have high concordance with modified Rankin Scale scores \u3e 2. RESULTS: 42,300 aSAH hospitalizations were identified in the NIS during the study period. By both ordinal [adjusted odds ratio (aOR) 3.20, 95% confidence interval (CI) 3.05, 3.36, p \u3c 0.001] and categorical stratification [frail aOR 3.59, 95% CI 3.39, 3.80, p \u3c 0.001; severely frail aOR 6.67, 95% CI 5.78, 7.69, p \u3c 0.001], the RAI achieved the largest effect sizes for NIS-SOM in comparison with the mFI and HFRS. Discrimination of the RAI for NIS-SOM in high-grade aSAH was significantly greater than that of the HFRS (c-statistic 0.651 vs. 0.615). The mFI demonstrated the lowest discrimination in both high-grade and normal-grade patients. A combined Hunt and Hess-RAI model (c-statistic 0.837, 95% CI 0.828, 0.845) for NIS-SOM achieved significantly greater discrimination than both the combined models for mFI and HFRS (p \u3c 0.001). CONCLUSION: The RAI was robustly associated with poor functional outcomes in aSAH independent of established risk factors