Endoscopic and Nonendoscopic Approaches to Single-Level Lumbar Spine Decompression: Propensity Score-Matched Comparative Analysis and Frailty-Driven Predictive Model

Objective The endoscopic spine surgery (ESS) approach is associated with high levels of patient satisfaction, shorter recovery time, and reduced complications. The present study reports multicenter, international data, comparing ESS and non-ESS approaches for single-level lumbar decompression, and proposes a frailty-driven predictive model for nonhome discharge (NHD) disposition. Methods Cases of ESS and non-ESS lumbar spine decompression were queried from the American College of Surgeons National Surgical Quality Improvement Program database (2017–2020). Propensity score matching was performed on baseline characteristics frailty score (measured by risk analysis index [RAI] and modified frailty index-5 [mFI-5]). The primary outcome of interest was NHD disposition. A predictive model was built using logistic regression with RAI as the primary driver. Results Single-level nonfusion spine lumbar decompression surgery was performed in 38,686 patients. Frailty, as measured by RAI, was a reliable predictor of NHD with excellent discriminatory accuracy in receiver operating characteristic (ROC) curve analysis: C-statistic: 0.80 (95% confidence interval [CI], 0.65–0.94) in ESS cohort, C-statistic: 0.75 (95% CI, 0.73–0.76) overall cohort. After propensity score matching, there was a reduction in total operative time (89 minutes vs. 103 minutes, p = 0.049) and hospital length of stay (LOS) (0.82 days vs. 1.37 days, p < 0.001) in patients treated endoscopically. In ROC curve analysis, the frailty-driven predictive model performed with excellent diagnostic accuracy for the primary outcome of NHD (C-statistic: 0.87; 95% CI, 0.85–0.88). Conclusion After frailty-based propensity matching, ESS is associated with reduced operative time, shorter hospital LOS, and decreased NHD. The RAI frailty-driven model predicts NHD with excellent diagnostic accuracy and may be applied to preoperative decision-making with a user-friendly calculator: nsgyfrailtyoutcomeslab.shinyapps.io/lumbar_decompression_dischargedispo

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

A taxonomy for brainstem cavernous malformations: subtypes of pontine lesions. Part 1: basilar, peritrigeminal, and middle peduncular

OBJECTIVE: Brainstem cavernous malformations (BSCMs) are complex, difficult to access, and highly variable in size, shape, and position. The authors have proposed a novel taxonomy for pontine cavernous malformations (CMs) based upon clinical presentation (syndromes) and anatomical location (findings on MRI). METHODS: The proposed taxonomy was applied to a 30-year (1990-2019), 2-surgeon experience. Of 601 patients who underwent microsurgical resection of BSCMs, 551 with appropriate data were classified on the basis of BSCM location: midbrain (151 [27%]), pons (323 [59%]), and medulla (77 [14%]). Pontine lesions were then subtyped on the basis of their predominant surface presentation identified on preoperative MRI. Neurological outcomes were assessed according to the modified Rankin Scale, with a score ≤ 2 defined as favorable. RESULTS: The 323 pontine BSCMs were classified into 6 distinct subtypes: basilar (6 [1.9%]), peritrigeminal (53 [16.4%]), middle peduncular (MP) (100 [31.0%]), inferior peduncular (47 [14.6%]), rhomboid (80 [24.8%]), and supraolivary (37 [11.5%]). Part 1 of this 2-part series describes the taxonomic basis for the first 3 of these 6 subtypes of pontine CM. Basilar lesions are located in the anteromedial pons and associated with contralateral hemiparesis. Peritrigeminal lesions are located in the anterolateral pons and are associated with hemiparesis and sensory changes. Patients with MP lesions presented with mild anterior inferior cerebellar artery syndrome with contralateral hemisensory loss, ipsilateral ataxia, and ipsilateral facial numbness without cranial neuropathies. A single surgical approach and strategy were preferred for each subtype: for basilar lesions, the pterional craniotomy and anterior transpetrous approach was preferred; for peritrigeminal lesions, extended retrosigmoid craniotomy and transcerebellopontine angle approach; and for MP lesions, extended retrosigmoid craniotomy and trans-middle cerebellar peduncle approach. Favorable outcomes were observed in 123 of 143 (86%) patients with follow-up data. There were no significant differences in outcomes between the 3 subtypes or any other subtypes. CONCLUSIONS: The neurological symptoms and key localizing signs associated with a hemorrhagic pontine subtype can help to define that subtype clinically. The proposed taxonomy for pontine CMs meaningfully guides surgical strategy and may improve patient outcomes