Influence of Clinical Risk Factors on International Normalized Ratio Control in Patients on Warfarin Therapy: A Systematic Review
Bibliographic record
Abstract
Warfarin remains a widely prescribed oral anticoagulant despite its narrow therapeutic index and its association with life-threatening bleeding events.This systematic review examined the impact of clinical risk factors on international normalized ratio (INR) control in patients receiving Warfarin therapy.Following the PRISMA-P guidelines, we searched four electronic databases (PubMed, SCOPUS, Embase, and Web of Science) using Medical Subject Headings to identify eligible cohort studies published between 2016 and 2020.A total of 15 original research articles met the inclusion criteria.The risk of bias was assessed using the Newcastle-Ottawa scale.This review identified several clinical risk factors influencing INR control, including drug interactions with Warfarin, herbal supplements, dietary intake of vitamin K, and multiple comorbidities (anemia, heart failure, chronic kidney disease, psychiatric disorders, prosthetic valve), kidney transplant recipient, hemodialysis, and hypoalbuminemia.These clinical risk factors adversely affect the time in therapeutic range, thereby increasing the risk of adverse outcomes such as thromboembolism, major bleeding, and mortality.Overall, these clinical risk factors compromise Warfarin efficacy by contributing to subtherapeutic or supratherapeutic INR levels.Identifying and addressing these clinical risk factors prior to initiating Warfarin therapy is essential to achieving optimal INR control.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".