Exploratory clinical efficacy and patient-reported outcomes from NOVA: A randomized controlled study of intravenous natalizumab 6-week dosing versus continued 4-week dosing for relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Natalizumab (TYSABRI®) 300 mg administered intravenously every-4-weeks (Q4W) is approved for treatment of relapsing-remitting multiple sclerosis but is associated with increased risk of progressive multifocal leukoencephalopathy (PML). Extended natalizumab dosing intervals of approximately every-6-weeks (Q6W) are associated with a lower risk of PML. Primary and secondary clinical outcomes from the NOVA randomized clinical trial (NCT03689972) suggest that effective disease control is maintained in patients who were stable during treatment with natalizumab Q4W for ≥12 months and who then switched to Q6W dosing. We compared additional exploratory clinical and patient-reported outcomes (PROs) from NOVA to assess the efficacy of Q6W dosing. METHODS: Prespecified exploratory clinical efficacy endpoints in NOVA included change from baseline in Expanded Disability Status Scale (EDSS) score, Timed 25-Foot Walk (T25FW), dominant- and nondominant-hand 9-Hole Peg Test (9HPT), and Symbol Digit Modalities Test (SDMT). Exploratory patient-reported outcome (PRO) efficacy endpoints included change from baseline in the Treatment Satisfaction Questionnaire for Medication (TSQM), Neuro-QoL fatigue questionnaire, Multiple Sclerosis Impact Scale (MSIS-29), EuroQol 5 Dimensions (EQ-5D-5 L) index score, Clinical Global Impression (CGI)-Improvement (patient- and clinician-assessed) and CGI-Severity (clinician-assessed) rating scales. Estimated proportions of patients with confirmed EDSS improvement were based on Kaplan-Meier methods. Estimates of mean treatment differences for Q6W versus Q4W in other outcomes were assessed by least squares mean (LSM) and analyzed using a linear mixed model of repeated measures or ordinal logistic regression (CGI-scale). RESULTS: Exploratory clinical and patient-reported outcomes were assessed in patients who received ≥1 dose of randomly assigned study treatment and had ≥1 postbaseline efficacy assessment (Q6W group, n = 247, and Q4W group, n = 242). Estimated proportions of patients with EDSS improvement at week 72 were similar for Q6W and Q4W groups (11.7% [19/163] vs 10.8% [17/158]; HR 1.02 [95% confidence interval [CI], 0.53-1.98]; P = 0.9501). At week 72, there were no significant differences between Q6W and Q4W groups in LSM change from baseline for T25FW (0.00, P = 0.975), 9HPT (dominant [0.22, P = 0.533] or nondominant [0.09, P = 0.862] hand), or SDMT (-1.03, P = 0.194). Similarly, there were no significant differences between Q6W and Q4W groups in LSM change from baseline for any PRO (TSQM, -1.00, P = 0.410; Neuro-QoL fatigue, 0.52, P = 0.292; MSIS-29 Psychological, 0.67, P = 0.572; MSIS-29 Physical, 0.74, P = 0.429; EQ-5D-5 L, 0.00, P = 0.978). For the EQ-5D-5 L, a higher proportion of Q6W patients than Q4W patients demonstrated worsening (≥0.5 standard deviation increase in the EQ-5D-5 L index score; P = 0.0475). From baseline to week 72 for Q6W versus Q4W, odds ratio (ORs) of LSM change in CGI scores did not show meaningful differences between groups (CGI-Improvement [patient]: OR [95% CI] 1.2 [0.80-1.73]; CGI-Improvement [physician]: 0.8 [0.47-1.36]; CGI-Severity [physician]: 1.0 [0.71-1.54]). CONCLUSIONS: No significant differences were observed in change from baseline to week 72 between natalizumab Q6W and Q4W groups for all exploratory clinical or PRO-related endpoints assessed. For the EQ-5D-5 L, a higher proportion of Q6W than Q4W patients demonstrated worsening.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".