Objective: This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease.
Methods: We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver-operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation.
Results: The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%-95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%-93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission-to-surgery time interval, C-reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular-cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission-intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%).
Conclusion: A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high-risk patients.
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http://dx.doi.org/10.1111/cns.14002 | DOI Listing |
BMC Surg
December 2024
Department of Orthopedics & Elderly Spinal Surgery, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China.
Objective: Failure to understand long-term quality of life and functional outcomes hinders effective decision making and prognostication. Therefore, the study aims to predict and analyse the unfavorable outcomes (FOs) in elderly patients undergoing lumbar fusion surgery.
Methods: Consecutive 382 patients who underwent lumbar fusion surgery for lumbar degenerative disease from March 2019 to July 2022 were enrolled in this study.
Clin Neurol Neurosurg
December 2024
Case Western Reserve University School of Medicine, Cleveland, OH, United States; Department of Neurological Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States. Electronic address:
Background: Degenerative cervical myelopathy is one of the most common causes of spinal cord dysfunction. Cervical laminoplasty is an excellent surgical procedure that address the underlying pathology along with motion preservation with various advantages over other surgical options. While the advantages are intuitive and are being proven in multiple recent studies, concerns regarding failure still remains precluding wider utilization despite evidence to the contrary.
View Article and Find Full Text PDFEur J Neurol
January 2025
Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Background: Magnetic resonance imaging may suggest spinal cord compression and structural lesions in degenerative cervical myelopathy (DCM) but cannot reveal functional impairments in spinal pathways. We aimed to assess the value of contact heat evoked potentials (CHEPs) in addition to MRI and hypothesized that abnormal CHEPs may be evident in DCM independent of MR-lesions and are related to dynamic mechanical cord stress.
Methods: Individuals with DCM underwent neurologic examination including segmental sensory (pinprick, light touch) and motor testing.
Arthritis Res Ther
December 2024
Department of Internal Medicine, Division of Rheumatology, Rush University Medical Center, Chicago, IL, USA.
Background: Osteoarthritis (OA) is a painful degenerative joint disease and a leading source of years lived with disability globally due to inadequate treatment options. Neuroimmune interactions reportedly contribute to OA pain pathogenesis. Notably, in rodents, macrophages in the DRG are associated with onset of persistent OA pain.
View Article and Find Full Text PDFNeurochirurgie
December 2024
Aix Marseille Univ, APM, UH Timone, Department of Neurosurgery, Marseille, France.
Background: The Da Vinci robot ® (DVR), released in the early 2000s, provided a set of innovation aiming at pushing minimally invasive surgery forward. Its stereoscopic magnified visualization camera, motions that exceed the natural range of the human hand, or tremor reduction enhanced the surgeon's skills and added value in many surgical fields.
Objective: To map the current use of the DVR in spine surgery, identify gaps, address its limits and future perspectives.
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