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

Introduction: 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.

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.

Results: 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.

Discussion: 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.