Preoperative Resilience and Early Postoperative Outcomes following Lumbar Spinal Fusion.

Background: Previous studies identified a correlation between preoperative resilience scores and patient reported outcome measures in several surgical subspecialities. No previous studies, to our knowledge, have analyzed preoperative resilience and patient reported outcomes in lumbar spinal fusion.

Methods: Patients undergoing lumbar spinal fusion completed the Brief Resilience Scale (BRS) preoperatively, in addition to measures of disability (Oswestry Disability index [ODI]), quality of life (PROMIS global physical and mental health scales and EuroQol5), and leg and back pain (VAS) at pre- and 3-months postoperatively.

An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion.

Study Design: Level III retrospective database study.

Objectives: The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF).

Methods: The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF.

Supervised Machine Learning for Predicting Length of Stay After Lumbar Arthrodesis: A Comprehensive Artificial Intelligence Approach.

Introduction: Few studies have evaluated the utility of machine learning techniques to predict and classify outcomes, such as length of stay (LOS), for lumbar fusion patients. Six supervised machine learning algorithms may be able to predict and classify whether a patient will experience a short or long hospital LOS after lumbar fusion surgery with a high degree of accuracy.

Methods: Data were obtained from the National Surgical Quality Improvement Program between 2009 and 2018.

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery.

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon's NSQIP dataset.

Transforaminal 360° lumbar endoscopic foraminotomy in postfusion patients: technical note and case series.

Objective: This report describes a minimally invasive lumbar foraminotomy technique that can be applied in patients who underwent complex spine decompression procedures or in patients with severe foraminal stenosis.

Methods: Awake, endoscopic decompression surgery was performed in 538 patients over a 5-year period between 2014 and 2019. Transforaminal endoscopic foraminal decompression surgery using a high-speed endoscopic drill was performed in 34 patients who had previously undergone fusions at the treated level.

Spinal Cord Stimulation via Percutaneous and Open Implantation: Systematic Review and Meta-Analysis Examining Complication Rates.

Background: Spinal cord stimulation (SCS) has become a successful therapeutic option for combating chronic pain and can be implanted via percutaneous or open (laminotomy/laminectomy) techniques. This study aimed to systematically review the complications that occur after SCS placement via percutaneous and open (laminotomy/laminectomy) in failed back surgery syndrome (FBSS), complex regional pain syndrome (CRPS), and chronic back (lumbosacral)/leg pain.

Methods: The PubMed and Embase databases were searched from inception to June 2020; prospective studies using SCS in patients with FBSS, CRPS, and chronic low back pain that reported both complications and the implantation method used were included.

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

Background: Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in analysis of risk factors for readmission and can help predict the likelihood of this occurrence.

Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach.

Objective: Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classification algorithm to predict mortality following spinal tumor excision within 30 days of surgery.