Future Directions of Spine Surgical Research - Use of Machine Learning Models to Optimize Treatment and Outcome Prediction in Degenerative Spondylolisthesis
Final Report Abstract
Degenerative changes in the lumbar spine are among the leading causes of chronic back pain and neurological symptoms globally, frequently treated by spine surgeons. Degenerative spondylolisthesis (DS), characterized by spinal instability and progressive degeneration leading to spinal canal narrowing, is now one of the most common indications for spinal fusion surgery, which involves relieving and stabilizing affected segments. Minimally invasive microsurgical decompression presents an alternative treatment option. However, clinical uncertainty persists regarding whether standalone decompression is sufficient or if additional spinal fusion is necessary to reduce risks such as secondary instability and the need for subsequent revision surgeries. Conversely, spinal fusion, particularly when performed openly, may increase stress on adjacent spinal segments, potentially leading to further surgical interventions. Therefore, understanding patients' individual risk profiles is essential for selecting optimal treatment strategies and ensuring patient-centered care. This project aimed to identify clinical and radiographic factors that facilitate personalized risk assessment and the development of postoperative monitoring systems for at-risk patients. Our study successfully identified threshold values for early postoperative pain scores, which help predict patients at higher risk of long-term functional limitations (measured using the Oswestry Disability Index, ODI) after lumbar fusion. Additionally, our research showed that the duration of preoperative symptoms did not negatively affect postoperative outcomes, whereas poor preoperative mental health was partly associated with less favorable clinical outcomes. These findings significantly contribute to tailoring therapeutic decisions to individual patient needs and early identification and support of at-risk patients.
Publications
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Beyond the Label: Extended Indications for Cervical Disc Arthroplasty. Contemporary Spine Surgery, 25(12), 1-7.
Bay, Annika; Zhao, Eric R.; Kwas, Cole T.; Simon, Chad Z.; Asada, Tomoyuki & Qureshi, Sheeraz A.
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Beyond the Surface - Exploring Cervical Muscle Health in Spine Surgery Vertebral Columns - International Society for the Advancement of Spine Surgery (ISASS); Winter 2024
Bay, Annika
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Precision in Practice - Alignment in Non-deformity Spine Surgery Vertebral Columns - International Society for the Advancement of Spine Surgery (ISASS); Spring 2024
Bay, Annika
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SMISS 2024: Heightened preoperative disability is reversible in patients with longer preoperative symptom duration at one- and two-year follow-up after single-level MIS fusion for degenerative spondylolisthesis
Bay, Annika
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IMAST 2025: IGF-1 Serum Levels are Associated with Early Recovery and In-Hospital Complications After Spinal Fusion
Bay, Annika
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ISASS 2025: Reactivity of the L5-S1 Disc Space Following Motion-Sparing Long Fusion in Adult Spinal Deformity Surgery
Bay, Annika
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SPINE SUMMIT 2025: Postoperative Procollagen type 1 N-Propeptide (P1NP) Increase is Associated with Pseudarthrosis at One-Year After Anterior Cervical Discectomy and Fusion (ACDF)
Bay, Annika
