Browse

You are looking at 1 - 2 of 2 items for

  • By Author: Philips, Sharon E. x
  • By Author: Dittus, Robert S. x
  • By Author: Harrell, Frank E. x
Clear All
Full access

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

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.

Full access

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

OBJECTIVE

Current costs associated with spine care are unsustainable. Productivity loss and time away from work for patients who were once gainfully employed contributes greatly to the financial burden experienced by individuals and, more broadly, society. Therefore, it is vital to identify the factors associated with return to work (RTW) after lumbar spine surgery. In this analysis, the authors used data from a national prospective outcomes registry to create a predictive model of patients’ ability to RTW after undergoing lumbar spine surgery for degenerative spine disease.

METHODS

Data from 4694 patients who underwent elective spine surgery for degenerative lumbar disease, who had been employed preoperatively, and who had completed a 3-month follow-up evaluation, were entered into a prospective, multicenter registry. Patient-reported outcomes—Oswestry Disability Index (ODI), numeric rating scale (NRS) for back pain (BP) and leg pain (LP), and EQ-5D scores—were recorded at baseline and at 3 months postoperatively. The time to RTW was defined as the period between operation and date of returning to work. A multivariable Cox proportional hazards regression model, including an array of preoperative factors, was fitted for RTW. The model performance was measured using the concordance index (c-index).

RESULTS

Eighty-two percent of patients (n = 3855) returned to work within 3 months postoperatively. The risk-adjusted predictors of a lower likelihood of RTW were being preoperatively employed but not working at the time of presentation, manual labor as an occupation, worker’s compensation, liability insurance for disability, higher preoperative ODI score, higher preoperative NRS-BP score, and demographic factors such as female sex, African American race, history of diabetes, and higher American Society of Anesthesiologists score. The likelihood of a RTW within 3 months was higher in patients with higher education level than in those with less than high school–level education. The c-index of the model’s performance was 0.71.

CONCLUSIONS

This study presents a novel predictive model for the probability of returning to work after lumbar spine surgery. Spine care providers can use this model to educate patients and encourage them in shared decision-making regarding the RTW outcome. This evidence-based decision support will result in better communication between patients and clinicians and improve postoperative recovery expectations, which will ultimately increase the likelihood of a positive RTW trajectory.