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

作者：Andrea Cina, Jacopo Antonino Vitale, Daniel Haschtmann, Markus Loibl, T. Fekete, Frank Kleinstück, Fabio Galbusera, Catherine R. Jutzeler, Anne F. Mannion · 发表于：European Spine Journal · 年份：2024 · DOI：10.1007/s00586-024-08537-7 · 被引用次数：4 · 研究领域：Delphi Technique in Research、Scoliosis diagnosis and treatment、Health Systems, Economic Evaluations, Quality of Life

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.