Comorbidities, safety and persistence in phase III clinical trials in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Associations between comorbidity and reduced persistence to disease-modifying therapies (DMTs) in multiple sclerosis (MS) have been identified. Limited information is available regarding the association of comorbidity with safety outcomes. The study objective was to evaluate the association of comorbidities with safety outcomes and persistence. METHODS: We conducted a two-stage meta-analysis of individual participant data from phase III clinical trials of MS DMTs. Individual comorbidities and comorbidity burden, defined as the sum of all comorbidities (n=15), were examined. Safety outcomes, defined using adverse event (AE) data, were reviewed to identify specific AEs of interest, including infection; treatment-emergent autoimmune disease; cancer; elevated transaminases and lymphopenia. We also examined any early trial discontinuation. RESULTS: We included 17 clinical trials representing 16 794 MS participants. Over a 2-year follow-up, the pooled proportion of AEs was 64% (95% CI 59.4% to 68.9%) and the majority were infection AEs. Increasing comorbidity burden was associated with an increased rate of AEs (rate ratio (95% CI) 1: 1.13 (1.09 to 1.17); 2: 1.19 (1.14 to 1.23); ≥3: 1.25 (1.18 to 1.33)) compared with those with no comorbidity. When pooled across trials, early discontinuation affected 17% of participants (95% CI 13.8% to 20.9%). A higher risk of trial discontinuation was associated with higher comorbidity burden (2: 1.23 (1.07 to 1.42); ≥3: 1.19 (1.01 to 1.40)) compared with those with no comorbidity. Psychiatric disorders were associated with trial discontinuation. CONCLUSIONS: Higher comorbidity burden is associated with increased risk of experiencing safety outcomes and early DMT discontinuation among individuals with MS enrolled in clinical trials of MS-DMTs, highlighting the important role of comorbidities in the safety and persistence of DMTs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".