A Case of an Increase in Prothrombin Time-International Normalized Ratio by Interaction Between Warfarin and Baloxavir Marboxil in a Patient on Implantable Ventricular Assist Device
Bibliographic record
Abstract
BACKGROUND: Baloxavir marboxil (BM) is a novel drug with a cap-dependent endonuclease inhibitory action for influenza A or B; it is highly safe and requires just a single oral dose. Patients with severe heart failure use implantable ventricular assist device (iVAD) until transplantation, but they have an increased risk of thrombosis development. Their warfarin is administered based on point-of-care testing (POCT) with a strict control of prothrombin time-international normalized ratio (PT-INR). CASE REPORT: Here, we report a case of a patient with iVAD whose PT-INR was significantly increased from the target range after BM administration. The patient was a 45-year-old man and transplanted with iVAD; warfarin treatment was started when his PT-INR target range was 3.0-3.5. At home, he frequently self-measured PT-INR by POCT and precisely controlled the warfarin dose. He had a fever, was diagnosed with influenza A and was administered BM 40 mg. Thereafter, his PT-INR continued to increase, reaching 4.8 on day 12 of BM administration, exceeding his target range; warfarin was skipped for 1 day. In this case, based on the history of BM administration and clinical course, the increase in PT-INR could be due to BM. Considering the interaction between warfarin and BM, we suspected a possibility of competition for protein-binding sites. Increased PT-INR in the patient was detected early by POCT and thus severe bleeding was avoided. CONCLUSION: Strict monitoring of PT-INR when using BM in patients taking warfarin is of clinical importance.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".