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

Nitin AgarwalDepartment of Neurological Surgery, University of California, San Francisco, California;

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Alexander A. AabediDepartment of Neurological Surgery, University of California, San Francisco, California;

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Andrew K. ChanDepartment of Neurological Surgery, University of California, San Francisco, California;

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Vijay LetchumanDepartment of Neurological Surgery, University of California, San Francisco, California;

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Saman ShabaniDepartment of Neurological Surgery, University of California, San Francisco, California;

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Erica F. BissonDepartment of Neurosurgery, University of Utah, Salt Lake City, Utah;

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Mohamad BydonDepartment of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota;

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Steven D. GlassmanNorton Leatherman Spine Center, Louisville, Kentucky;

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Kevin T. FoleyDepartment of Neurosurgery, Semmes-Murphey Neurologic and Spine Institute, University of Tennessee, Memphis, Tennessee;

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Christopher I. ShaffreyDepartments of Neurosurgery and
Orthopedic Surgery, Duke University, Durham, North Carolina;

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Eric A. PottsDepartment of Neurological Surgery, Goodman Campbell Brain and Spine, Indianapolis, Indiana;

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Mark E. ShaffreyDepartment of Neurosurgery, University of Virginia, Charlottesville, Virginia;

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Domagoj CoricNeuroscience Institute, Carolina Neurosurgery & Spine Associates, Carolinas Healthcare System, Charlotte, North Carolina;

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John J. KnightlyAtlantic Neurosurgical Specialists, Morristown, New Jersey;

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Paul ParkDepartment of Neurosurgery, University of Michigan, Ann Arbor, Michigan;

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Michael Y. WangDepartment of Neurological Surgery, University of Miami, Florida;

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Kai-Ming FuDepartment of Neurological Surgery, Weill Cornell Medical Center, New York, New York;

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Jonathan R. SlotkinGeisinger Health, Danville, Pennsylvania; and

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Anthony L. AsherNeuroscience Institute, Carolina Neurosurgery & Spine Associates, Carolinas Healthcare System, Charlotte, North Carolina;

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Michael S. VirkDepartment of Neurological Surgery, Weill Cornell Medical Center, New York, New York;

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Regis W. Haid Jr.Atlanta Brain and Spine Care, Atlanta, Georgia

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Dean ChouDepartment of Neurological Surgery, University of California, San Francisco, California;

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Praveen V. MummaneniDepartment of Neurological Surgery, University of California, San Francisco, California;

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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.

ABBREVIATIONS

ASA = American Society of Anesthesiologists; BMI = body mass index; MCID = minimal clinically important difference; NRSBP = numeric rating scale for back pain; ODI = Oswestry Disability Index; PRO = patient-reported outcome; QOD = Quality Outcomes Database; SVA = sagittal vertical axis.

Supplementary Materials

    • Supplementary Table 1 and Fig. 1 (PDF 492 KB)
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Illustration from Alvernia et al. (pp 233–241). © Jorge Alvernia, published with permission.
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