Defining optimal activated clotting time for percutaneous coronary intervention: A systematic review and Bayesian meta‐regression
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend routine monitoring of unfractionated heparin (UFH) with activated clotting time (ACT) during percutaneous coronary intervention (PCI). However, the optimal ACT for patients undergoing PCI is unclear. METHODS: We sought to determine the association of peak ACT during PCI with 30-day major adverse cardiac events (MACE; all-cause mortality, myocardial infarction, and revascularization) and bleeding events. We searched the Cochrane Central Register of Controlled Trials, EMBASE, and Medline for randomized controlled trials (RCTs) evaluating UFH through May 2015. Only patients randomized to UFH alone or to UFH with a glycoprotein IIb/IIIa inhibitor (GPI) were analyzed using Bayesian meta-regression. RESULTS: Among 13 included RCTs (n = 17455), eight (n = 5521) included study arms of UFH alone and 12 (n = 11934) included arms of UFH with a GPI. Peak ACT ranged from 201 to 460 sec for UFH alone and 248-317 sec for UFH with a GPI. With UFH alone, the probability of MACE was 7.0% (95% credible interval [CrI] 1.5, 31.5) for a peak ACT of 200 sec and 5.8% (95% CrI 2.6, 12.0) for 300 sec. Among UFH with a GPI, the probability of MACE was 2.8% (95% CrI 0.8, 6.8) for a peak ACT of 200 sec and 7.2% (95% CrI 5.4, 9.7) for 300 sec. CONCLUSION: Among individual RCTs, the probability of MACE and major bleeding events associated with low versus high values of peak ACT is inconsistent. Our meta-regression results are inconclusive, emphasizing the need for RCTs comparing low versus high doses of UFH. © 2016 Wiley Periodicals, Inc.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".