Systematic review and meta-analysis: real-world data rates of deep remission with anti-TNFα in inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Deep remission (DR) is a treatment target in IBD associated with reduced hospitalization and improved outcome. Randomized control trial (RCT) data demonstrates efficacy of anti-TNFα agents in achieving DR; however, real-world data (RWD) can provide information complementary to RCTs, specifically regarding treatment duration. In this systematic review with meta-analysis, we use real-world data (RWD) to determine rates of DR in IBD treated with anti-TNFα. METHODS: We completed a systematic search of MEDLINE and EMBASE on July 8, 2019 with review of major gastrointestinal conference abstracts from 2012 to 2019. Studies utilizing RWD (data not from phase I-III RCTs) of adult IBD patients treated with anti-TNFα agents were included. DR was defined by clinical and endoscopic remission at minimum. DR was assessed at 8 weeks, 6 months, 1 year, and 2 years. Risk of bias was assessed with the Newcastle Ottawa Scale. RESULTS: 29,033 publications were identified. Fifteen publications, nine manuscripts and six conference abstracts, were included encompassing 1212 patients (769 Crohn's disease-CD, 443 ulcerative colitis-UC), and analyzed using Comprehensive Meta-Analysis. Rate of DR was 36.4% (95% CI 12.6-69.4%) at 8 weeks, 39.1% (95% CI 10.4-78%) at 6 months, 44.4% (95% CI 34.6-54.6%) at 1 year, and 36% (95% CI 18.7-58%) at 2 years. DR in CD at 1 year was 48.6% (95% CI 32.8-64.7%) and in UC was 43.6% (95% CI 32.8-55.1%). CONCLUSIONS: The rate of DR was highest after 1 year of therapy, in nearly 45% of IBD patients treated with anti-TNFα. Similar rates were achieved between patients with UC and CD. The findings highlight the efficacy of anti-TNFα in real-world setting. Future studies using RWD can determine efficacy of newer IBD therapeutics in routine clinical practice.
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 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 | 0.032 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".