ArticlePloS one2022
Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Warfarin-associated Spontaneous Hemorrhage from the Lateral Pectoral Artery after Reverse Total Shoulder Arthroplasty: A Case Report.Journal of orthopaedic case reports · 2026Article
- Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.European journal of clinical pharmacology · 2026Review
- Study of target INR achievement, incidence of hemorrhagic complications and affecting factors after during warfarin treatment in western area of China.Scientific reports · 2025Article
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Authors and funding
9 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWarfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients.
methodsNine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting.
resultsWe found that logistic regression (AUC = 0.879, 95% CI: 0.834-0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01-1.77).
conclusionsOur data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.
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