Methodological considerations in calculating the minimal clinically important change score for the core outcome measures index (COMI): insights from a large single-centre spine surgery registry.

Abstract

The Minimal Clinically Important Change (MCIC) is used in conjunction with Patient-Reported Outcome Measures (PROMs) to determine the clinical relevance of changes in health status. MCIC measures a change within the same person or group over time. This study aims to evaluate the variability in computing MCIC for the Core Outcome Measure Index (COMI) using different methods. Data from a spine centre in Switzerland were used to evaluate variations in MCIC for the COMI score. Distribution-based and anchor-based methods (predictive and nonpredictive) were applied. Bayesian bootstrap estimated confidence intervals. From 27,003 cases, 9821 met the inclusion criteria. Distribution-based methods yielded MCIC values from 0.4 to 1.4. Anchor-based methods showed more variability, with MCIC values from 1.5 to 4.9. Predictive anchor-based methods also provided variable MCIC values for improvement (0.3-2.4), with high sensitivity and specificity. MCIC calculation methods produce varying values, emphasizing careful method selection. Distribution-based methods likely measure minimal detectable change, while non-predictive anchor-based methods can yield high MCIC values due to group averaging. Predictive anchor-based methods offer more stable and clinically relevant MCIC values for improvement but are affected by prevalence and reliability corrections.