Retrospective Analysis of the Effect of Argatroban and Coumadin on INR Values in Heparin Induced Thrombocytopenia.
Bibliographic record
Abstract
Abstract Background: Patients who develop Heparin-induced thrombocytopenia(HIT) are at an increased risk for thrombosis. They may require long term anticoagulant therapy and are often stepped down from either argatroba, lepirudinn or danaparoid to oral warfarin therapy. Step down of anticoagulation therapy is complicated by falsely elevated PT/INR which can cause difficultly in assessing the appropriate time to discontinue Argatroban therapy. Patients may be increased risk of bleeding due to receiving combined therapy. Previous studies analyzing this effect of combined therapy were observed in healthy individuals. The experience at this institution is complicated utilization of abnormally low rates of argatroban to achieve therapeutic aPTT levels. Method: This is a retrospective chart review study analyzing the trends of INRs with argatroban and warfarin concomitant therapy. All patients during the period of January 2002 to December 2005 receiving argatroban were identified from pharmacy records. Only patients with argatroban and warfarin stepdown therapy were considered for this analysis. The only exclusion criteria was documented end stage liver failure. ANOVA analysis was deemed appropriate and SPSS program was utilized. Direct thrombin Inhibitor (DTI) levels was utilized for argatroban monitoring starting in 2005. The primary endpoint was to determine the effect Argatroban has on INR by analyzing the INR with concomitant argatroban and coumadin therapy, argatroban alone and evaluating the true INR with coumadin alone. The secondary endpoint was to observe any adverse effects and complications from concomitant therapy mainly bleeding and mortality. Results: The primary endpoints are displayed in table 1. Ninety-three percent of patients required a maintenance argartroban rate of less than 1.0 mcg/kg/min with 68 % requiring less than 0.5 mcg/kg/min. There was a stronger correlation when argatroban rates were less than 0.25 mcg/kg/min and the associated twofold increase previously noted in other trials with combined therapy was not observed. The secondary endpoints are shown in table 2. Less than 50% of the patients in both major and minor bleeding episodes and 33% of mortality had documented INRs > 4.0. Conclusions: The argatroban rates documented in this study are 50–75% less than 2–4 mcg/kg/min usually reported in literature. In addition, the conversion from aPTT to DTI monitoring resulted in a greater than 60% reduction in argatroban rates. However the correlation was also stronger at these lower rates. Although there was not a high percentage of bleeds with INR’s greater than 4.0, the relationship between DTI and INR suggest that a lower combined INR could be targeted for therapeutic efficacy. A warfarin dosing nomogram based on these finding will be developed and tested. Information regarding ISI was not available at the time of this abstract. Primary Endpoints Argatroban Rate(mcg/kg/min) N INR on warfarin alone INR on warfarin and argatroban ANOVA < 0.05 13 3.17 ± 1.37 4.01 ± 2.90 0.734 0.06–0.25 32 3.68 ± 1.06 4.19 ± 1.17 0.704 0.26–0.5 14 2.71 ± 1.12 3.38 ± 1.69 0.22 0.5–1.0 13 3.10 ±0.79 5.00 ± 0.54 0.54 >1.0 6 2.74 ± 1.47 5.13 ± 3.69 0.162 Secondary Endpoints -Complications n (%) outcomes with INR > 4.0 on both agents * all INRs >5.5 Total Bleeding 11 (13%) 5 (45%) Major 7 (8%) 3 (43%) Minor 4 (5%) 2 (50%) High requiring PRBC with no bleeding* 5 (6%) 5 (100%) Mortality 15 (17%) 5 (33%)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".