Improvements in Functioning with Esketamine Nasal Spray versus Quetiapine Extended Release in Patients with Treatment Resistant Depression
Bibliographic record
Abstract
Introduction In the ESCAPE-TRD study, esketamine nasal spray (ESK-NS) significantly increased chance of remission at Week 8 versus (vs) quetiapine extended release (Q-XR) in patients (pts) with treatment resistant depression (TRD; Reif et al. NEJM 2023; 389:1298–309). Changes in disability and functional impairment due to depressive symptoms assessed with the Sheehan Disability Scale (SDS) are reported. Objectives To assess the effect of ESK-NS vs Q-XR on pts’ daily functioning using SDS, considering their symptom evolution. Methods ESCAPE‑TRD was a randomised phase IIIb trial comparing the efficacy of ESK-NS vs Q-XR, both alongside an ongoing selective serotonin/serotonin-norepinephrine reuptake inhibitor, in pts with TRD. Clinical response (CRes) was defined as ≥50% improvement in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline or total score ≤10, clinical remission (CRem) was defined as total MADRS score of ≤10, and functional remission (FRem) was defined as SDS total score ≤6. The Kaplan-Meier method was used for time to event analyses, and hazard ratios (HRs) were estimated using Cox regression models. Time in each state was estimated by treatment arm and compared between arms using analysis of covariance. Results 336 and 340 pts were randomised to ESK-NS and Q-XR, respectively. Significantly more ESK-NS treated pts achieved CRes, CRem and FRem (HRs: 1.848, 1.711 and 1.819, respectively; all p<0.001), and achieved these faster, compared to Q-XR (Figure 1). In each arm and at each time point, more pts reached CRes than CRem, and more reached CRem than FRem, illustrating that FRem is more difficult to achieve (Figure 1). Total time in CRes was 5.4 weeks greater for ESK-NS compared with Q-XR; total time in CRem was 3.7 weeks greater and in FRem 2.0 weeks greater for ESK-NS vs Q-XR, respectively (Table 1). Image 1: Image 2: Conclusions These data support a temporal cascade of events from CRes to CRem to FRem; ESK-NS improved time to, and in, each outcome vs Q-XR. Treatments that reduce clinical symptoms better and faster provide the best chance of improving functional impairment. Disclosure of Interest None Declared
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".