Abstract 11938: Long-term Population-based Outcomes of Novel Oral Anticoagulants Compared to Warfarin Among Long-term Anticoagulated Patients: A Propensity Matched Analysis
Bibliographic record
Abstract
Introduction: Novel oral anticoagulants (NOACs) have been used in clinical practice in the US for the last 4 years. While NOACs may be an attractive alternative to warfarin among many patients, long-term outcomes of use of these medications are unknown. We performed a propensity matched analysis of long-term outcomes among patients taking a NOAC or warfarin. Hypothesis: NOACs will be superior to warfarin for the reduction of long-term outcomes. Methods: Patients receiving long-term anticoagulation between June 2010 and December 2014 for thromboembolism prevention with either warfarin or a single NOAC were matched 1:1 by index date (± 6 months) and propensity score (±0.01). Multivariable Cox hazard regression was performed to determine the risk of death, stroke/TIA, major bleed, and bleed by the anticoagulant therapy received. Results: Patients studied included: apixaban: 590, dabigatran: 583, rivaroxaban: 1, 454. A total of 5,254 patients were studied (2,627 per group). Average age was 72.4±10.9 and 59.0% were male. The majority of patients were receiving long-term anticoagulation for AF management (warfarin: 96.5% vs. NOAC: 92.7%, p<0.0001). Rivaroxaban (n=1,454 [55.3%]) was the most commonly used NOAC, (apixaban=590 [22.5%], dabigatran=583 [22.2%]). History of stroke/TIA and prior bleed were similar between the groups, yet more NOAC patients formally had major bleeding (9.2% vs. 6.8%, p=0.002). The use of NOACs compared to warfarin were associated with a reduced risk of long-term adverse outcomes [Figure, death (p=0.09), stroke/TIA (p<0.0001), major bleed (p<0.0001), and bleed (p=0.14)]. No significant outcome variance was noted in NOAC type comparison. Conclusions: Our community-based results are support better long-term efficacy and safety of NOACs compared to warfarin, and are in agreement with the shorter-term results of previous clinical trials.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".