• Eur Spine J · Dec 2024

    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.

    • Andrea Cina, Jacopo Vitale, Daniel Haschtmann, Markus Loibl, Tamas F Fekete, Frank Kleinstück, Fabio Galbusera, Catherine R Jutzeler, and Anne F Mannion.
    • Department of Health Sciences and Technology (D-HEST), ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland. andrea.cina@hest.ethz.ch.
    • Eur Spine J. 2024 Dec 1; 33 (12): 441544254415-4425.

    IntroductionThe 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.MethodsData 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.ResultsFrom 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.DiscussionMCIC 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.© 2024. The Author(s).

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