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. 2022 May 5;17(5):e0267966.
doi: 10.1371/journal.pone.0267966. eCollection 2022.

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

Zhiyuan Ma  1 , Ping Wang  2 , Milan Mahesh  3 , Cyrus P Elmi  4 , Saeid Atashpanjeh  5 , Bahar Khalighi  6 , Gang Cheng  7 , Mahesh Krishnamurthy  1 , Koroush Khalighi  8
Affiliations

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

Zhiyuan Ma et al. PLoS One. .

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart illustration of the study cohorts.
AB: AdaBoost, ET: Extremely Randomized Tree, GNB: Gaussian Naïve Bayes, KNN: K Nearest Neighbors, RF: Random Forests, LGBT: Light Gradient Boosting Tree, LG: Logistic Regression, NN: Neural Network, SVC: Support Vector Machine.
Fig 2
Fig 2. ROC curve analysis of nine different machine learning algorithms using the validation cohort in the IWPC cohort.
AB: AdaBoost, ET: Extremely Randomized Tree, GNB: Gaussian Naïve Bayes, KNN: K Nearest Neighbors, RF: Random Forests, LGBT: Light Gradient Boosting Tree, LG: Logistic Regression, NN: Neural Network, SVC: Support Vector Machine.
Fig 3
Fig 3. The importance of features in the random forest and AdaBoost machine learning models.
Fig 4
Fig 4. Primary outcome analysis with three different models.
Warfarin sensitivity was significantly associated with increased in-hospital mortality. PSM, propensity score matching; IPTW, inverse probability of treatment weighting.
See this image and copyright information in PMC

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