Predicting SF-6D utility scores from the neck disability index and numeric rating scales for neck and arm pain.

STUDY DESIGN

Cross-sectional cohort.

OBJECTIVE

This study aims to provide an algorithm to estimate Short Form-6D (SF-6D) utilities using data from the Neck Disability Index (NDI), neck pain, and arm pain scores.

SUMMARY OF BACKGROUND DATA

Although cost-utility analysis is increasingly used to provide information about the relative value of alternative interventions, health state values or utilities are rarely available from clinical trial data. The Neck Disability Index (NDI) and numerical rating scales for neck and arm pain are widely used disease-specific measures in patients with cervical degenerative disorders. The purpose of this study is to provide an algorithm to allow estimation of SF-6D utilities using data from the NDI, and numerical rating scales for neck and arm pain.

METHODS

SF-36, NDI, neck and arm pain rating scale scores were prospectively collected before surgery, at 12 and 24 months after surgery in 2080 patients undergoing cervical fusion for degenerative disorders. SF-6D utilities were computed, and Spearman correlation coefficients were calculated for paired observations from multiple time points between NDI, neck and arm pain scores, and SF-6D utility scores. SF-6D scores were estimated from the NDI, neck and arm pain scores were estimated using a linear regression model. Using a separate, independent dataset of 396 patients in which NDI scores were available, SF-6D was estimated for each subject and compared to their actual SF-6D.

RESULTS

The mean age for those in the development sample was 50.4 ± 11.0 years and 33% were male. In the validation sample, the mean age was 53.1 ± 9.9 years and 35% were male. Correlations between the SF-6D and the NDI, neck and arm pain scores were statistically significant (P < 0.0001) with correlation coefficients of 0.82, 0.62, and 0.50, respectively. The regression equation using NDI aloneto predict SF-6D had an R of 0.66 and a root mean square error of 0.056. In the validation analysis, there was no statistically significant difference (P 5 0.961) between actual mean SF-6D (0.49 6 0.08) and the estimated mean SF-6D score (0.49 6 0.08), using the NDI regression model.

CONCLUSION

This regression-based algorithm may be a useful tool to predict SF-6D scores in studies of cervical degenerative disease that have collected NDI but not utility scores.