S2323 Addressing an Emerging Clinical Need: Nasal Metoclopramide's Impact on Diabetic Gastroparesis in Patients Taking GLP-1 Agonists
Bibliographic record
Abstract
Introduction: Diabetic gastroparesis (DGP) is a chronic upper gastrointestinal disorder characterized by delayed gastric emptying without mechanical obstruction, causing nausea, vomiting, and abdominal pain. Glucagon-like peptide 1(GLP-1) agonists, used to treat type 2 diabetes, can exacerbate these symptoms by delaying gastric emptying. This study compares healthcare resource utilization (HRU) in DGP patients treated with nasal (NMCP) vs oral metoclopramide (OMCP) with recent GLP-1 agonist use. Methods: A retrospective, matched cohort of NMCP- and OMCP-treated patients (257 per group) was derived from specialty pharmacy data and the Symphony Integrated Dataverse, an open claims database. Adult patients with a DGP diagnosis and ≥6 months pre- and post-index (treatment initiation) continuous data were included. Propensity score matching reduced imbalances in age, region, payor, Charlson Comorbidity Score, and 6-month pre-index hospitalization/emergency department (ED) use. Patients with a GLP-1 prescription filled ≤6 months pre-index were analyzed. Post-treatment all-cause and DGP-related HRU (gastroparesis, nausea, vomiting) were compared using a multivariable negative binomial regression model, with results as conditional incident rate ratios (cIRR) and 95% confidence intervals (CI). Results: The subgroup included 51 NMCP and 41 OMCP patients with a prior GLP-1 prescription. NMCP patients were slightly older (55.1 vs 53.1 years) and had more pre-index hospitalization/ED admissions (31.4% vs 19.5%). For NMCP patients, all-cause ED visits decreased by 55% (mean [SD]: 0.25 [1.13] post-index vs 0.55 [1.30] pre-index; P=0.063) and DGP-related ED visits decreased by 28% (mean [SD]: 0.18 [0.99] post-index vs 0.25 [1.28] pre-index; P=0.203).All-cause and DGP-related ED visits were 91% lower (cIRR: 0.09, 95% CI: 0.01, 0.42; P=0.001) and 89% lower (cIRR: 0.11, 95% CI: 0, 0.93; P=0.046) for NMCP vs OMCP. Furthermore, all-cause and DGP-related office visits were 41% lower (cIRR: 0.59, 95% CI: 0.37, 0.94; P=0.027) and 66% lower (cIRR: 0.34, 95% CI: 0.017, 0.65; P=0.001) for NMCP vs OMCP. All-cause clinic, outpatient, and inpatient visits showed similar trends favoring NMCP vs OMCP (Figure 1). Conclusion: In DGP patients with a prior claim for GLP-1, NMCP use was associated with numerically and significantly reduced all-cause and DGP-related HRU compared to pre-treatment utilization and OMCP-treated controls.Figure 1.: Comparison of healthcare resource utilization in glucagon-like peptide 1 agonist patients with diabetic gastroparesis.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".