Adverse effects of biologics: a network meta-analysis and Cochrane overview
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Bibliographic record
Abstract
BACKGROUND: Biologics are used for the treatment of rheumatoid arthritis and many other conditions. While the efficacy of biologics has been established, there is uncertainty regarding the adverse effects of this treatment. Since serious risks such as tuberculosis (TB) reactivation, serious infections, and lymphomas may be common to the biologics but occur in small numbers across the various indications, we planned to combine the results from biologics used in many conditions to obtain the much needed risk estimates. OBJECTIVES: To compare the adverse effects of tumor necrosis factor blocker (etanercept, adalimumab, infliximab, golimumab, certolizumab), interleukin (IL)-1 antagonist (anakinra), IL-6 antagonist (tocilizumab), anti-CD28 (abatacept), and anti-B cell (rituximab) therapy in patients with any disease condition except human immunodeficiency disease (HIV/AIDS). METHODS: Randomized controlled trials (RCTs), controlled clinical trials (CCTs) and open-label extension (OLE) studies that studied one of the nine biologics for use in any indication (with the exception of HIV/AIDS) and that reported our pre-specified adverse outcomes were considered for inclusion. We searched The Cochrane Library, MEDLINE, and EMBASE (to January 2010). Identifying search results and data extraction were performed independently and in duplicate. For the network meta-analysis, we performed mixed-effects logistic regression using an arm-based, random-effects model within an empirical Bayes framework. MAIN RESULTS: We included 163 RCTs with 50,010 participants and 46 extension studies with 11,954 participants. The median duration of RCTs was six months and 13 months for OLEs. Data were limited for tuberculosis (TB) reactivation, lymphoma, and congestive heart failure. Adjusted for dose, biologics as a group were associated with a statistically significant higher rate of total adverse events (odds ratio (OR) 1.19, 95% CI 1.09 to 1.30; number needed to treat to harm (NNTH) = 30, 95% CI 21 to 60) and withdrawals due to adverse events (OR 1.32, 95% CI 1.06 to 1.64; NNTH = 37, 95% CI 19 to 190) and an increased risk of TB reactivation (OR 4.68, 95% CI 1.18 to 18.60; NNTH = 681, 95% CI 143 to 14706) compared to control.The rate of serious adverse events, serious infections, lymphoma, and congestive heart failure were not statistically significantly different between biologics and control treatment. Certolizumab pegol was associated with significantly higher risk of serious infections compared to control treatment (OR 3.51, 95% CI 1.59 to 7.79; NNTH = 17, 95% CI 7 to 68). Infliximab was associated with significantly higher risk of withdrawals due to adverse events compared to control (OR 2.04, 95% CI 1.43 to 2.91; NNTH = 12, 95% CI 8 to 28). Indirect comparisons revealed that abatacept and anakinra were associated with a significantly lower risk of serious adverse events compared to most other biologics. Although the overall numbers are relatively small, certolizumab pegol was associated with significantly higher odds of serious infections compared to etanercept, adalimumab, abatacept, anakinra, golimumab, infliximab, and rituximab; abatacept was significantly less likely than infliximab and tocilizumab to be associated with serious infections. Abatacept, adalimumab, etanercept and golimumab were significantly less likely than infliximab to result in withdrawals due to adverse events. AUTHORS' CONCLUSIONS: Overall, in the short term biologics were associated with significantly higher rates of total adverse events, withdrawals due to adverse events and TB reactivation. Some biologics had a statistically higher association with certain adverse outcomes compared to control, but there was no consistency across the outcomes so caution is needed in interpreting these results.There is an urgent need for more research regarding the long-term safety of biologics and the comparative safety of different biologics. National and international registries and other types of large databases are relevant sources for providing complementary evidence regarding the short- and longer-term safety of biologics.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.035 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it