Cited by 176Spine and Intervertebral Disc PathologyMusculoskeletal pain and rehabilitationCervical and Thoracic Myelopathy

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An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery: predicting likely individual patient outcomes for shared decision-making

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McGirt, Matthew J., Bydon, Mohamad, Archer, Kristin R., Devin, Clinton J., Chotai, Silky, Parker, Scott L., Nian, Hui, Harrell, Frank E., Speroff, Theodore, Dittus, Robert S., Philips, Sharon E., Shaffrey, Christopher I., Foley, Kevin T., Asher, Anthony L.·2017·Journal of Neurosurgery: Spine

DOI 10.3171/2016.11.spine16526

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Abstract

OBJECTIVE: Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifically, clinical registry data are progressively informing health care decision-making. In this analysis, the authors used data from a national prospective outcomes registry (the Quality Outcomes Database) to develop a predictive model for 12-month postoperative pain, disability, and quality of life (QOL) in patients undergoing elective lumbar spine surgery. METHODS: Included in this analysis were 7618 patients who had completed 12 months of follow-up. The authors prospectively assessed baseline and 12-month patient-reported outcomes (PROs) via telephone interviews. The PROs assessed were those ascertained using the Oswestry Disability Index (ODI), EQ-5D, and numeric rating scale (NRS) for back pain (BP) and leg pain (LP). Variables analyzed for the predictive model included age, gender, body mass index, race, education level, history of prior surgery, smoking status, comorbid conditions, American Society of Anesthesiologists (ASA) score, symptom duration, indication for surgery, number of levels surgically treated, history of fusion surgery, surgical approach, receipt of workers’ compensation, liability insurance, insurance status, and ambulatory ability. To create a predictive model, each 12-month PRO was treated as an ordinal dependent variable and a separate proportional-odds ordinal logistic regression model was fitted for each PRO. RESULTS: There was a significant improvement in all PROs (p < 0.0001) at 12 months following lumbar spine surgery. The most important predictors of overall disability, QOL, and pain outcomes following lumbar spine surgery were employment status, baseline NRS-BP scores, psychological distress, baseline ODI scores, level of education, workers’ compensation status, symptom duration, race, baseline NRS-LP scores, ASA score, age, predominant symptom, smoking status, and insurance status. The prediction discrimination of the 4 separate novel predictive models was good, with a c-index of 0.69 for ODI, 0.69 for EQ-5D, 0.67 for NRS-BP, and 0.64 for NRS-LP (i.e., good concordance between predicted outcomes and observed outcomes). CONCLUSIONS: This study found that preoperative patient-specific factors derived from a prospective national outcomes registry significantly influence PRO measures of treatment effectiveness at 12 months after lumbar surgery. Novel predictive models constructed with these data hold the potential to improve surgical effectiveness and the overall value of spine surgery by optimizing patient selection and identifying important modifiable factors before a surgery even takes place. Furthermore, these models can advance patient-focused care when used as shared decision-making tools during preoperative patient counseling.

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Authors: McGirt, M. J., Bydon, M., Archer, K. R., Devin, C. J., Chotai, S., Parker, S. L., Nian, H., Harrell, F. E., Speroff, T., Dittus, R. S., Philips, S. E., Shaffrey, C. I., Foley, K. T., & Asher, A. L.
Affiliations: Carolinas Healthcare System (US)
Mayo Clinic (US)
Vanderbilt University (US)
Vanderbilt University Medical Center (US)
Department of Orthopedic Surgery and Neurological Surgery, Vanderbilt Spine Center, Vanderbilt University Medical Center, Nashville, Tennessee;
University of Virginia Hospital (US)
University of Tennessee Health Science Center (US)
ORCID: Bydon, Mohamad
Archer, Kristin R.
Devin, Clinton J.
Chotai, Silky
Nian, Hui
Harrell, Frank E.
Shaffrey, Christopher I.
Foley, Kevin T.
Asher, Anthony L.
Venue: Journal of Neurosurgery: Spine
Publisher: Journal of Neurosurgery Publishing Group (JNSPG)
Published: 2017-10
Volume: 27
Issue: 4
Pages: 357-369
ISSN: 1547-5646 (ISSN-L)
1547-5654
DOI: 10.3171/2016.11.spine16526
OpenAlex: W2613104890
Cited by: 176
References: 69
Citations by year: 2026: 9 · 2025: 11 · 2024: 13 · 2023: 22 · 2022: 21 · 2021: 31 · 2020: 30 · 2019: 19 · 2018: 20
FWCI: 14.82
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Journal of Neurosurgery Spine (publishedVersion)
PubMed (publishedVersion)
Funders: Neurosurgery Research and Education Foundation
Topics: Health Sciences › Medicine › Orthopedics and Sports Medicine › Spine and Intervertebral Disc Pathology
Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Musculoskeletal pain and rehabilitation
Health Sciences › Medicine › Surgery › Cervical and Thoracic Myelopathy
Keywords: lumbar spine surgery, patient-reported outcomes, disability, quality of life, postoperative pain, prediction model, shared decision-making, Quality Outcomes Database, workers' compensation, psychological distress, leg pain, back pain, employment status, prognostic factors, spinal fusion, surgical approach, insurance status, racial disparities, educational attainment, smoking status, ASA physical status, symptom duration, ordinal logistic regression
MeSH terms: Aged, Back Pain, Decision Making, Disability Evaluation, Female, Follow-Up Studies, Humans, Longitudinal Studies, Lumbar Vertebrae, Male, Middle Aged, Pain Measurement, Postoperative Pain, Prognosis, Prospective Studies, Quality of Life, Registries, Sensitivity and Specificity, Socioeconomic Factors, Spinal Diseases, Databases, Factual, Treatment Outcome

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An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery: predicting likely individual patient outcomes for shared decision-making

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