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Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients
Journal article   Open access   Peer reviewed

Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients

Zhiyuan Ma, Ping Wang, Milan Mahesh, Cyrus P Elmi, Saeid Atashpanjeh, Bahar Khalighi, Gang Cheng, Mahesh Krishnamurthy and Koroush Khalighi
PloS one, v 17(5), pp e0267966-e0267966
2022
DOI: https://doi.org/10.1371/journal.pone.0267966
PMID: 35511891
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1371/journal.pone.0267966View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Algorithms Anticoagulants - adverse effects Critical Illness Drug Resistance Hospital Mortality Humans International Normalized Ratio Metabolism, Inborn Errors Warfarin - adverse effects
Warfarin 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. Nine 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. We 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). Our 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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6 citations in Web of Science
4 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Pharmacology & Pharmacy
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