Predicting SF-6D utility scores from the Oswestry disability index and numeric rating scales for back and leg pain

Spine (Phila Pa 1976). 2009 Sep 1;34(19):2085-9. doi: 10.1097/BRS.0b013e3181a93ea6.


Study design: Cross-sectional cohort.

Objective: The purpose of this study is to provide a model to allow estimation of utility from the Short Form (SF)-6D using data from the Oswestry Disability Index (ODI), Back Pain Numeric Rating Scale (BPNRS), and the Leg Pain Numeric Rating Scale (LPNRS).

Summary of background data: Cost-utility analysis provides important information about the relative value of interventions and requires a measure of utility not often available from clinical trial data. The ODI and numeric rating scales for back (BPNRS) and leg pain (LPNRS), are widely used disease-specific measures for health-related quality of life in patients with lumbar degenerative disorders. The purpose of this study is to provide a model to allow estimation of utility from the SF-6D using data from the ODI, BPNRS, and the LPNRS.

Methods: SF-36, ODI, BPNRS, and LPNRS were prospectively collected before surgery, at 12 and 24 months after surgery in 2640 patients undergoing lumbar fusion for degenerative disorders. Spearman correlation coefficients for paired observations from multiple time points between ODI, BPNRS, and LPNRS, and SF-6D utility scores were determined. Regression modeling was done to compute the SF-6D score from the ODI, BPNRS, and LPNRS. Using a separate, independent dataset of 2174 patients in which actual SF-6D and ODI scores were available, the SF-6D was estimated for each subject and compared to their actual SF-6D.

Results: In the development sample, the mean age was 52.5 +/- 15 years and 34% were male. In the validation sample, the mean age was 52.9 +/- 14.2 years and 44% were male. Correlations between the SF-6D and the ODI, BPNRS, and LPNRS were statistically significant (P < 0.0001) with correlation coefficients of 0.82, 0.78, and 0.72, respectively. The regression equation using ODI, BPNRS,and LPNRS to predict SF-6D had an R of 0.69 and a root mean square error of 0.076. The model using ODI alone had an R of 0.67 and a root mean square error of 0.078. The correlation coefficient between the observed and estimated SF-6D score was 0.80. In the validation analysis, there was no statistically significant difference (P = 0.11) between actual mean SF-6D (0.55 +/- 0.12) and the estimated mean SF-6D score (0.55 +/- 0.10) using the ODI regression model.

Conclusion: This regression-based algorithm may be used to predict SF-6D scores in studies of lumbar degenerative disease that have collected ODI but not utility scores.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • Back Pain / diagnosis*
  • Back Pain / physiopathology
  • Back Pain / surgery
  • Cross-Sectional Studies
  • Disability Evaluation*
  • Female
  • Humans
  • Leg / physiopathology*
  • Lumbar Vertebrae / surgery
  • Male
  • Middle Aged
  • Pain / diagnosis*
  • Pain / physiopathology
  • Pain / surgery
  • Pain Measurement*
  • Predictive Value of Tests
  • Prospective Studies
  • Regression Analysis
  • Reproducibility of Results
  • Severity of Illness Index
  • Spinal Fusion
  • Surveys and Questionnaires*
  • Treatment Outcome