Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong‐Hao

doi:10.1371/journal.pone.0135784

Cited by 49 publications

(64 citation statements)

References 29 publications

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“…The consideration of admixture will be particularly important for U.S. populations, and some progress has been made in developing these models . Additional computational approaches to warfarin dosing, such as machine learning and artificial neural networks, have also been investigated . Although this review focused on U.S. populations, many quality studies have been published evaluating other race/ethnic groups across the globe .…”

Section: Future Directionsmentioning

confidence: 99%

“…43 Additional computational approaches to warfarin dosing, such as machine learning and artificial neural networks, have also been investigated. 67,68 Although this review focused on U.S. populations, many quality studies have been published evaluating other race/ethnic groups across the globe. 51,61 These studies can inform research in U.S. populations and can ultimately provide guidance on the best process for accurately predicting warfarin dose.…”

Section: Future Directionsmentioning

confidence: 99%

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Warfarin Pharmacogenomics in Diverse Populations

et al. 2017

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Genotype-guided warfarin dosing algorithms are a rational approach to optimize warfarin dosing and potentially reduce adverse drug events. Diverse populations, such as African Americans and Latinos, have greater variability in warfarin dose requirements and are at greater risk for experiencing warfarinrelated adverse events compared with individuals of European ancestry. Although these data suggest that patients of diverse populations may benefit from improved warfarin dose estimation, the vast majority of literature on genotype-guided warfarin dosing, including data from prospective randomized trials, is in populations of European ancestry. Despite differing frequencies of variants by race/ethnicity, most evidence in diverse populations evaluates variants that are most common in populations of European ancestry. Algorithms that do not include variants important across race/ethnic groups are unlikely to benefit diverse populations. In some race/ethnic groups, development of race-specific or admixture-based algorithms may facilitate improved genotype-guided warfarin dosing algorithms above and beyond that seen in individuals of European ancestry. These observations should be considered in the interpretation of literature evaluating the clinical utility of genotype-guided warfarin dosing. Careful consideration of race/ethnicity and additional evidence focused on improving warfarin dosing algorithms across race/ethnic groups will be necessary for successful clinical implementation of warfarin pharmacogenomics. The evidence for warfarin pharmacogenomics has a broad significance for pharmacogenomic testing, emphasizing the consideration of race/ethnicity in discovery of gene-drug pairs and development of clinical recommendations for pharmacogenetic testing.

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Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Warfarin Pharmacogenomics in Diverse Populations

et al. 2017

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“…9,16 Other packages within R were used for different specific tasks (eg, nnet for construction of the neural network and random forest [randomForest] for constructing random forests). 7,11,[15][16][17][18][19][20][21][22][23][24] All numerical data were centered and scaled prior to analysis with all of the above methods. The R code used for these analyses is shown in Supplementary material 2.…”

Section: Model Comparisonsmentioning

confidence: 99%

<p>Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study</p>

Chen¹,

Brewster²,

Dworkin³

et al. 2019

IJNRD

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Background: Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. Methods: We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R. Results: The best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models. Conclusion: Prediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome.

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“…Warfarin is one of the most used anticoagulants worldwide. However, its use tends to be challenging, due to its narrow therapeutic window and dose variability requirements among patients (Liu et al, 2015;Ma et al, 2018). Side effects may result in bleeding for patients with an overdosing or thrombosis in case of under-dosing, both related with an inadequate dosage.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, patients who are under treatment need to be continuously monitored to avoid further damage. Studies have been developed in order to improve the recommended dose for warfarin patients that present side effects related to bleeding or thrombosis (Liu et al, 2015;Ma et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data

et al. 2020

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Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.

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Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

Cited by 49 publications

References 29 publications

Warfarin Pharmacogenomics in Diverse Populations

Warfarin Pharmacogenomics in Diverse Populations

<p>Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study</p>

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data

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