Identifying factors associated with clinical success in patients treated with NASHA®/Dx injection for fecal incontinence
Bibliographic record
Abstract
Howard Franklin,1 Andrew C Barrett,1 Ray Wolf2 1Department of Medical Affairs, Salix, a Division of Valeant Pharmaceuticals North America LLC, Bridgewater, NJ, USA; 2Department of Medical Affairs, Valeant Pharmaceuticals North America LLC, Wilton Manors, FL, USA Purpose: Injection with the bulking agent consisting of non-animal stabilized hyaluronic acid/dextranomer (NASHA®/Dx) is well tolerated and efficacious for the treatment of fecal incontinence (FI); however, the patient population that may derive maximum benefit has not been established. This post hoc responder analysis assessed demographic and baseline characteristics predictive of responsiveness to NASHA/Dx treatment. Methods: Adults with a Cleveland Clinic Florida fecal incontinence score (CCFIS) ≥10 were randomized to receive NASHA/Dx or sham treatment. The primary end point was response to treatment (ie, decrease from baseline of ≥50% in number of FI episodes) at 6 months; a prespecified secondary end point was change in fecal incontinence quality of life (FIQL) score at 6 months. Post hoc subgroup analyses were performed for baseline and demographic characteristics and prior FI treatments. Results: Overall, response to treatment was significantly greater with NASHA/Dx versus sham injection (52.7% vs 32.1%; P=0.0089). All subgroups analyzed demonstrated evidence of improvement, favoring NASHA/Dx versus sham treatment for both response to treatment and change in the FIQL coping/behavior subscale score. For the primary end point, a significantly greater percentage of patients with CCFIS ≤15, FI symptoms ≤5 years’ duration, or obstetric causes of FI responded to NASHA/Dx treatment versus patients receiving sham treatment (51.1% vs 28.3%, P=0.0169; 55.4% vs 25.7%, P=0.0026; and 53.6% vs 23.1%, P=0.0191, respectively). The mean change in the FIQL coping/behavior score significantly favored NASHA/Dx versus sham treatment for patients with CCFIS ≤15 (P=0.0371), FI symptoms ≤5 years’ duration (P=0.0289), or obstetric causes of FI (P=0.0384). Patients without a history of specific FI treatments (eg, antidiarrheal medications, biofeedback, surgery) were more likely to respond to NASHA/Dx versus sham treatment for both end points. Conclusion: Although all subgroups analyzed showed evidence of quantitative and qualitative benefit from NASHA/Dx therapy, patients with characteristics indicative of mild-to-moderate FI may exhibit the greatest benefit. Keywords: responder analysis, quality of life, coping/behavior, Solesta
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 teacher head, 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".