Development of a mapping function ("crosswalk") for the conversion of scores between the Oswestry Disability Index...

The Oswestry Disability Index (ODI) and the Core Outcome Measures Index (COMI) are two commonly used self-rating outcome instruments in patients with lumbar spinal disorders. No formal crosswalk between them exists that would otherwise allow the scores of one to be interpreted in terms of the other. We aimed to create such a mapping function.

We performed a secondary analysis of ODI and COMI data previously collected from 3324 patients (57 ± 17y; 60.3% female) at baseline and 1y after surgical or conservative treatment. Correlations between scores and Cohen's kappa for agreement (κ) regarding achievement of the minimal clinically important change (MCIC) score on each instrument (ODI, 12.8 points; COMI, 2.2 points) were calculated, and regression models were built. The latter were tested for accuracy in an independent set of registry data from 634 patients (60 ± 15y; 56.8% female).

All pairs of measures were significantly positively correlated (baseline, 0.73; 1y follow-up (FU), 0.84; change-scores, 0.73). MCIC for COMI was achieved in 53.9% patients and for ODI, in 52.4%, with 78% agreement on an individual basis (κ = 0.56). Standard errors for the regression slopes and intercepts were low, indicating excellent prediction at the group level, but root mean square residuals (reflecting individual error) were relatively high. ODI was predicted as COMI × 7.13-4.20 (at baseline), COMI × 6.34 + 2.67 (at FU) and COMI × 5.18 + 1.92 (for change-score); COMI was predicted as ODI × 0.075 + 3.64 (baseline), ODI × 0.113 + 0.96 (FU), and ODI × 0.102 + 1.10 (change-score). ICCs were 0.63-0.87 for derived versus actual scores.

Predictions at the group level were very good and met standards justifying the pooling of data. However, we caution against using individual values for treatment decisions, e.g. attempting to monitor patients over time, first with one instrument and then with the other, due to the lower statistical precision at the individual level. The ability to convert scores via the developed mapping function should open up more centres/registries for collaboration and facilitate the combining of data in meta-analyses.