World Neurosurg
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Anterior lumbar interbody fusion (ALIF) is a surgical treatment that requires a close operative plane to the great vessels, which increases the risk of perioperative complications. To our knowledge, no previous study has investigated the American Society of Anesthesiologists (ASA) Physical Status Classification System as a predictive factor for unfavorable perioperative outcomes in ALIF procedures. We aimed to analyze the ASA score as a predictive factor of intraoperative and postoperative outcomes in patients undergoing ALIFs. ⋯ Among 210 patients identified, 59 (28.1%) had an ASA score >2 and 151 (71.9%) had an ASA score ≤2. On multivariate analysis, an ASA score >2 was predictive of increased 90-day reoperations (P = 0.02), estimated blood loss (EBL) (P = 0.02), and operative time (P = 0.02). Previous lumbar surgery was predictive of increased length of stay (P = 0.005), EBL (P < 0.001), 90-day readmission (P = 0.02), and operative time (P < 0.001). Posterior supplemental fixation was predictive of increased length of stay (P = 0.04). Increased number of operative levels was predictive of increased EBL (P < 0.001) and operative time (P < 0.001). Perioperative anticoagulation use was predictive of increased EBL (P < 0.001) CONCLUSIONS: Increased ASA scores were associated with unfavorable outcomes after ALIF and also can be used as a predictive tool for the risk of reoperations.
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Osteoporosis is a burgeoning public health problem for over 44 million people in the United States. The magnetic resonance imaging-based vertebral bone quality (VBQ) score and cervical VBQ (C-VBQ) score are two novel approaches that use data routinely gathered during preoperative evaluation to assess bone quality. The goal of this study was to investigate the relationship between the VBQ and C-VBQ scores. ⋯ This is the first study, to our knowledge, to assess the degree to which the newly developed C-VBQ score correlates with the VBQ score. We found a strong positive correlation between the scores.
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Artificial intelligence (AI) has the potential to augment clinicians' diagnostic and decision-making capabilities. It is well suited to identify patterns and correlations within data sets and may be applied to identify elements of importance in complex and data-laden areas such as patient selection, diagnostics, treatment, and outcome prediction. The development of modern neurosurgery has been dependent on major technological advances. In line with this, a growing interest is seen in the use of AI to assist in neurosurgical research and enhance neurosurgical practices. ⋯ This review highlights the most-impactful articles pertaining to AI in the field of neurosurgery. Although female authors were significantly underrepresented on the list, their work was at least as impactful as their male peers. Finally, the striking dominance of articles originating from the developed world raises concerns as to the future of AI in attending to the global health crisis.