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Record W4403977821 · doi:10.1016/j.clnesp.2024.10.161

Real-world experience with glucagon-like peptide 2 analogues in patients with short bowel syndrome and chronic intestinal failure: Results from an international survey in expert intestinal failure centers

2024· article· en· W4403977821 on OpenAlexafffund
Tim Vanuytsel, Narisorn Lakananurak, Sophie Greif, Elizabeth Wall, Hilary Catron, Jean Herlitz, Lisa Moccia, Vanessa J. Kumpf, David F. Mercer, Mark Berner‐Hansen, Leah Gramlich

Bibliographic record

VenueClinical Nutrition ESPEN · 2024
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of Alberta
FundersFonds Wetenschappelijk OnderzoekAlberta Health Services
KeywordsMedicineIntestinal failureShort bowel syndromeGlucagon-like peptide-2Intensive care medicineInternal medicineGastroenterologyPeptideParenteral nutritionBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND AND AIMS: Glucagon-like peptide 2 (GLP-2) analogues are the first available disease-modifying treatments for patients with intestinal failure (IF) due to short bowel syndrome (SBS). Efficacy in terms of reduction of parenteral support (PS) has been demonstrated in multiple studies and real-world reports. However, it remains unclear how many patients are eligible to receive the treatment, when treatment is started after intestinal resection, how treatment efficacy is assessed outside of clinical trials, and how the treatment is modified in case of non-response or adverse events. The aim of this study was to investigate the real-world management of patients treated with GLP-2 analogues in expert centers around the world. METHODS: A survey questionnaire was developed by a multidisciplinary working group consisting of 52 questions related to various aspects of multidisciplinary care of SBS-IF patients. The 17 questions related to the use of GLP-2 analogues in clinical practice were analyzed for this study. The online survey was sent to 33 participating centers in a phase 3 study of a long-acting GLP-2 analogue. Only responses from countries with access to commercially available GLP-2 analogues were included in the study. A descriptive analysis was performed for each question. Results are presented as median (interquartile range). RESULTS: The responses from the 19 expert IF centers with access to GLP-2 analogues indicated that 10 (10-20) % of patients with SBS-IF were treated with a GLP-2 analogue, which was less than the number of eligible patients (30 (25-40) %). In most centers (10 centers, 53 %), GLP-2 therapy was started 6-12 months after the last intestinal resection, with 5 centers (26 %) starting later (12-24 months). Multiple parameters were used in combination to determine the response to GLP-2 analogues of which the three most common were >20 % decrease in PS (95 %), at least 1 day of PS reduction per week (84 %) and increased urinary output (68 %). In non-responders GLP-2 therapy was stopped within the first year by 67 % of the centers. Finally, strategies in case of significant adverse events include stopping the GLP-2 analogue (used by 79 % of experts), dose reduction (67 %) and temporary treatment interruption (62 %). CONCLUSION: The results of this survey completed by expert IF centers show the real-life use of GLP-2 analogues in clinical practice. Key learning points identified include the accounting for a period of intestinal adaptation before starting GLP-2 analogues and not stopping the treatment too early in case of non-response. The best strategy in case of adverse effects should be studied further.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.351
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations5
Published2024
Admission routes2
Has abstractno

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