Leveraging machine learning to ascertain the implications of preoperative body mass index on surgical outcomes for 282 patients with preoperative obesity and lumbar spondylolisthesis in the Quality Outcomes Database.
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2023-02
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Abstract
Objective
Prior studies have revealed that a body mass index (BMI) ≥ 30 is associated with worse outcomes following surgical intervention in grade 1 lumbar spondylolisthesis. Using a machine learning approach, this study aimed to leverage the prospective Quality Outcomes Database (QOD) to identify a BMI threshold for patients undergoing surgical intervention for grade 1 lumbar spondylolisthesis and thus reliably identify optimal surgical candidates among obese patients.Methods
Patients with grade 1 lumbar spondylolisthesis and preoperative BMI ≥ 30 from the prospectively collected QOD lumbar spondylolisthesis module were included in this study. A 12-month composite outcome was generated by performing principal components analysis and k-means clustering on four validated measures of surgical outcomes in patients with spondylolisthesis. Random forests were generated to determine the most important preoperative patient characteristics in predicting the composite outcome. Recursive partitioning was used to extract a BMI threshold associated with optimal outcomes.Results
The average BMI was 35.7, with 282 (46.4%) of the 608 patients from the QOD data set having a BMI ≥ 30. Principal components analysis revealed that the first principal component accounted for 99.2% of the variance in the four outcome measures. Two clusters were identified corresponding to patients with suboptimal outcomes (severe back pain, increased disability, impaired quality of life, and low satisfaction) and to those with optimal outcomes. Recursive partitioning established a BMI threshold of 37.5 after pruning via cross-validation.Conclusions
In this multicenter study, the authors found that a BMI ≤ 37.5 was associated with improved patient outcomes following surgical intervention. These findings may help augment predictive analytics to deliver precision medicine and improve prehabilitation strategies.Type
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Agarwal, Nitin, Alexander A Aabedi, Andrew K Chan, Vijay Letchuman, Saman Shabani, Erica F Bisson, Mohamad Bydon, Steven D Glassman, et al. (2023). Leveraging machine learning to ascertain the implications of preoperative body mass index on surgical outcomes for 282 patients with preoperative obesity and lumbar spondylolisthesis in the Quality Outcomes Database. Journal of neurosurgery. Spine, 38(2). pp. 182–191. 10.3171/2022.8.spine22365 Retrieved from https://hdl.handle.net/10161/27975.
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Christopher Ignatius Shaffrey
I have more than 25 years of experience treating patients of all ages with spinal disorders. I have had an interest in the management of spinal disorders since starting my medical education. I performed residencies in both orthopaedic surgery and neurosurgery to gain a comprehensive understanding of the entire range of spinal disorders. My goal has been to find innovative ways to manage the range of spinal conditions, straightforward to complex. I have a focus on managing patients with complex spinal disorders. My patient evaluation and management philosophy is to provide engaged, compassionate care that focuses on providing the simplest and least aggressive treatment option for a particular condition. In many cases, non-operative treatment options exist to improve a patient’s symptoms. I have been actively engaged in clinical research to find the best ways to manage spinal disorders in order to achieve better results with fewer complications.
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