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Record W4404096849 · doi:10.1016/j.jaut.2024.103331

Efficacy and safety of Advanced Combination Treatment in immune-mediated inflammatory disease: A systematic review and meta-analysis of randomized controlled trials

2024· review· en· W4404096849 on OpenAlexaff
Virginia Solitano, Yuhong Yuan, Siddharth Singh, Christopher Ma, Olga Maria Nardone, Gionata Fiorino, María Laura Acosta Felquer, Lillian Barra, Maria Antonietta D’Agostino, Janet Pope, Laurent Peyrin‐Biroulet, Silvio Danese, Vipul Jairath

Bibliographic record

VenueJournal of Autoimmunity · 2024
Typereview
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of CalgaryLawson Health Research InstituteWestern University
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineMeta-analysisRandomized controlled trialImmune systemIntensive care medicineImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Advanced combination treatment (ACT), defined as a combination of at least 2 biologic agents, a biologic agent and an oral small molecule, 2 oral small molecules drug with different mechanisms of action is a proposed strategy to improve outcomes in patients with immune-mediated inflammatory disease (IMID). We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing ACT with monotherapy in patients with select IMIDs. Through a systematic literature search, we identified 10 RCTs (n = 1154) comparing ACT with single agent therapy (monotherapy). The primary outcome was induction of clinical remission. Secondary outcomes were adverse events, serious adverse events, infections, and serious infections. We performed random-effects meta-analysis and used GRADE to appraise certainty of evidence. Eight out of 10 trials investigated an anti-TNF-α drug (e.g., etanercept, infliximab, golimumab, certolizumab) combined with another biologic (e.g anti-IL-23, anti-integrin, anti-IL-1) or an oral small molecule. There was no significant difference in the likelihood of achieving clinical remission with ACT vs. monotherapy in patients with rheumatoid arthritis (n = 7 RCTs) (RR, 1.75 [95 % CI 0.60–5.13]; moderate heterogeneity (I 2 = 33 %)] and systemic lupus erythematosus (n = 1) (RR, 1.20 [0.53–2.72]) (GRADE; low certainty evidence). Patients with rheumatoid arthritis in the ACT arm were more likely to experience adverse events (RR, 1.07 [1.01–1.12]) compared to monotherapy. ACT led to higher rates of induction of clinical remission in patients with IBD (n = 2 RCTs) (RR, 1.68 [1.15–2.46]) with minimal heterogeneity (I 2 = 15 %) (GRADE; low certainty evidence), and no differences in the likelihood of adverse events (RR 0.92 [0.80–1.05]). There were no differences in the risk of infections or serious infections in patients treated with ACT or monotherapy with rheumatological disease or IBD. ACT did not offer clinical benefit in patients with rheumatological IMIDs and resulted in higher rate adverse events in rheumatoid arthritis. ACT may offer clinical benefit without a clear safety signal in patients with IBD, but further trials are warranted. The variability in ACT regimens across studies limits the generalizability of these findings. • ACT combining biologics or a biologic with a small molecule, emerged as a promising strategy in other medical fields. • This meta-analysis provides a comprehensive evaluation of the clinical efficacy and safety profiles in IMID RCTs. • In rheumatological conditions, ACT did not demonstrate a clear benefit over monotherapy. • In IBD, ACT significantly induced clinical remission without a corresponding increase in adverse events or infections. • Future research should focus on optimizing ACT, understanding disease-specific mechanisms, and long-term outcomes.

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)0.021
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.043
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0270.044
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.385
Teacher spread0.330 · 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 designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2024
Admission routes1
Has abstractyes

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