Development of a mapping function ("crosswalk") for the conversion of scores between the Oswestry Disability Index (ODI) and the Core Outcome Measures Index (COMI).

INTRODUCTION: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?

BACKGROUND: The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables. METHODS: Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11'523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (N = 8078) and test (N = 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (N = 341) and external validation (N = 171) samples. RESULTS: In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82-0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke's pseudo-R2 for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs. < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82-0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81-0.84), 0.85 ± 0.02 (95%CI, 0.80-0.89), and 0.77 ± 0.04 (95%CI,0.69-0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples. CONCLUSIONS: It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.