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

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

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

OBJECTIVE Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifcally, 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 proportionalodds ordinal logistic regression model was ftted for each PRO. RESULTS There was a signifcant 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-specifc factors derived from a prospective national outcomes registry signifcantly in?uence 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 modifable 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.

Original languageEnglish (US)
Pages (from-to)357-369
Number of pages13
JournalJournal of Neurosurgery: Spine
Volume27
Issue number4
DOIs
StatePublished - Oct 1 2017

Fingerprint

Decision Making
Spine
Quality of Life
Databases
Pain
Registries
Back Pain
Workers' Compensation
Leg
Insurance Coverage
Logistic Models
Smoking
Liability Insurance
Education
Patient-Centered Care
Aptitude
Postoperative Pain
Patient Selection
Patient Reported Outcome Measures
Counseling

Keywords

  • Disability
  • Lumbar
  • Pain
  • Patient-reported outcomes
  • Predictive model
  • QOD
  • Quality of life
  • Quality Outcomes Database

ASJC Scopus subject areas

  • Surgery
  • Neurology
  • Clinical Neurology

Cite this

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. / 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.

In: Journal of Neurosurgery: Spine, Vol. 27, No. 4, 01.10.2017, p. 357-369.

Research output: Contribution to journalArticle

McGirt, MJ, Bydon, M, Archer, KR, Devin, CJ, Chotai, S, Parker, SL, Nian, H, Harrell, FE, Speroff, T, Dittus, RS, Philips, SE, Shaffrey, CI, Foley, KT & Asher, AL 2017, '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', Journal of Neurosurgery: Spine, vol. 27, no. 4, pp. 357-369. https://doi.org/10.3171/2016.11.SPINE16526
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. / 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. In: Journal of Neurosurgery: Spine. 2017 ; Vol. 27, No. 4. pp. 357-369.
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T1 - An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery

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AU - Bydon, Mohamad

AU - Archer, Kristin R.

AU - Devin, Clinton J.

AU - Chotai, Silky

AU - Parker, Scott L.

AU - Nian, Hui

AU - Harrell, Frank E.

AU - Speroff, Theodore

AU - Dittus, Robert S.

AU - Philips, Sharon E.

AU - Shaffrey, Christopher I.

AU - Foley, Kevin T.

AU - Asher, Anthony L.

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N2 - OBJECTIVE Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifcally, 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 proportionalodds ordinal logistic regression model was ftted for each PRO. RESULTS There was a signifcant 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-specifc factors derived from a prospective national outcomes registry signifcantly in?uence 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 modifable 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.

AB - OBJECTIVE Quality and outcomes registry platforms lie at the center of many emerging evidence-driven reform models. Specifcally, 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 proportionalodds ordinal logistic regression model was ftted for each PRO. RESULTS There was a signifcant 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-specifc factors derived from a prospective national outcomes registry signifcantly in?uence 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 modifable 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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KW - Lumbar

KW - Pain

KW - Patient-reported outcomes

KW - Predictive model

KW - QOD

KW - Quality of life

KW - Quality Outcomes Database

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