Validating therapy decisions by reasons for therapy switch in relapsing-remitting multiple sclerosis

Models for individualized treatment recommendations in relapsing–remitting multiple sclerosis often select therapies based on predicted risks of relapse and/or 3-month confirmed disability progression (3m-CDP). Recommendations should be interpretable, align with guidelines, and reflect patient perspectives. We validated recently developed prognostic algorithms in eight subgroups defined by reason for therapy switch (intolerance, lack of efficacy, pregnancy desire, serious adverse events, programmed stop, personal convenience, therapy initiation, drug-holiday end). From the OFSEP registry, we analyzed 3768 therapy cycles (2017–2021) with six commonly used therapies (interferon beta, glatiramer acetate, teriflunomide, dimethyl fumarate, fingolimod, natalizumab). Algorithms produced ranked therapy lists per outcome; certainty was quantified by entropy (0 = clear; 4.78 = maximal uncertainty). Recommendations were more consistent for relapse (median entropy 0.54, IQR 0.39–0.61) than for 3m-CDP (0.95, IQR 0.88–1.01). Across subgroups, natalizumab most often ranked highest for relapse, whereas teriflunomide or interferon beta frequently ranked highest for 3m-CDP. Calibration varied across switcher subgroups and outcomes, while discrimination was comparable to overall set: C-index 0.776 (95% CI 0.753–0.797) for 3m-CDP and 0.638 (95% CI 0.615–0.661) for relapse. Guideline and relapse-based recommendations did not always align, notably in the pregnancy-desire subgroup. For 3m-CDP, guideline-based recommendations are scarce, precluding systematic comparison with model outputs. Agreement between the best-ranked therapy for relapse and 3m-CDP was zero (κ = 0), underscoring outcome-dependent divergence. Algorithm-generated recommendations depend on the chosen outcome and may diverge from guideline-based practice or patient perspectives. Transparent communication of uncertainty and outcome trade-offs is essential for shared decision-making.