Mapping between EQ-5D-5L and EQ-5D-3L in lumbar spine surgery patients: Machine learning compared to crosswalk algorithms

Introduction

The transition from EQ-5D-3L to EQ-5D-5L poses challenges for comparing health-related quality of life across studies and clinical settings. Although official EuroQol crosswalk algorithms are commonly used to map between versions, alternative mapping approaches may be relevant for disease-specific populations. Research question: This study evaluated the performance of machine-learning models for bidirectional mapping between EQ-5D-3L and EQ-5D-5L in patients undergoing evaluation for elective lumbar spine surgery for degenerative conditions. Material and methods: Paired EQ-5D-3L and EQ-5D-5L single-center responses (n = 766) were analyzed using repeated cross-validation. Response-level mapping was performed using parametric ordinal regression models (VGAM, ordinalNet) and tree-based machine-learning models (XGBoost, CatBoost, ordinalForest). Both hard and soft prediction approaches were evaluated on held-out data and compared with the official crosswalk algorithms using RMSE, MAE, bias (mean signed error), R2, and CCC.