Evaluation of 16 genotype-guided Warfarin Dosing Algorithms in 310 Korean Patients Receiving Warfarin Treatment: Poor Prediction Performance in VKORC1 1173C Carriers

Developing Chinese race-specific warfarin dose prediction algorithms

BACKGROUND

Numerous genotype-guided warfarin dosing algorithms have been developed to individualize warfarin doses, but they can only explain 47-52% of the variability.

AIM

This study aimed to develop new warfarin algorithms suitable to predict the stable warfarin dose for the Chinese population and to compare their prediction performance with those of the most commonly used algorithms.

METHOD

Multiple linear regression analysis with the warfarin optimal dose (WOD), logarithm (log) WOD, 1/WOD, and [Formula: see text], respectively, as the dependent variables were performed to deduce a new warfarin algorithm (NEW-Warfarin). WOD was the stable dose that maintained the international normalized ratio (INR) within the target range (2.0-3.0). Three major genotype-guided warfarin dosing algorithms were selected and compared against NEW-Warfarin predictive performance using the mean absolute error (MAE). Furthermore, patients were divided into five groups according to warfarin indications [atrial fibrillation (AF), pulmonary embolism (PE), cardiac-related disease (CRD), deep vein thrombosis (DVT), and other diseases (OD)]. Multiple linear regression analyses were also performed for each group.

RESULTS

The regression equation with [Formula: see text] as the dependent variable had the highest coefficient of determination (R² = 0.489). The NEW-Warfarin had the best predictive accuracy compared to the three algorithms selected. Group analysis, according to indications, showed that the R² of the five groups were PE (0.902) > DVT (0.608) > CRD (0.569) > OD (0.436) > AF (0.424).

CONCLUSION

Dosing algorithms based on warfarin indications are more suitable for predicting warfarin doses. Our research provides a novel strategy to develop indication-specific warfarin dosing algorithms to improve the efficacy and safety of warfarin prescribing.

Comparison of Maintenance Dose Predictions by Warfarin Dosing Algorithms Based on Chinese and Western Patients

Warfarin has a long record of safe and effective clinical use, and it remains one of the most commonly prescribed drugs for the prevention and treatment of thromboembolic conditions even in the era of direct oral anticoagulants. To address its large interindividual variability and narrow therapeutic window, the Clinical Pharmacogenetics Implementation Consortium has recommended using pharmacogenetic dosing algorithms, such as the ones developed by the International Warfarin Pharmacogenetics Consortium (IWPC) and by Gage et al, to dose warfarin when genotype information is available. In China, dosing algorithms based on local patient populations have been developed and evaluated for predictive accuracy of warfarin maintenance doses. In this study, percentage deviations of doses predicted by 15 Chinese dosing algorithms from that by IWPC and Gage algorithms were systematically evaluated to understand the differences between Chinese and Western algorithms. In general, dose predictions by Chinese dosing algorithms tended to be lower than those predicted by IWPC or Gage algorithms for the most prevalent VKORC1 and CYP2C9 genotypes in the Chinese population. The extent of negative prediction deviation appeared to be largest in the younger age group with smaller body weight. Our findings are consistent with previous reports that Asians have a higher sensitivity to warfarin and require lower doses than Western populations.

Impact of VKORC1, CYP2C9, and CYP4F2 Polymorphisms on Optimal Warfarin Dose: Does Ethnicity Matters?

BACKGROUND

Conventional anticoagulation with warfarin remains the cornerstone strategy for numerous preventive strategies. It is established that Asian patients require lower warfarin doses than Caucasians potentially attributing to the genetic polymorphism (GP) differences.

AREAS OF UNCERTAINTY

The impact of GP on optimal warfarin dose (OWD) in Koreans is unclear when compared with other ethnicities. It is also not well established whether GP linked to OWD in Korean patients to the similar extend as in Chinese, Japanese, and Caucasians.

DATA SOURCES

Single-center prospective observational study in Koreans, matched with historic cohorts of other ethnicities.

THERAPEUTIC ADVANCES

Clinical characteristics, concomitant medications, OWD, international normalized ratio, and VKORC1, CYP2C9, and CYP4F2 GPs were assessed in consecutive Korean patients. The OWD was defined when patient's international normalized ratio was within target range for at least 3 consecutive times separated by 1 week. We included 133 (mean age 62.6 ± 12.1 years, 49% males) warfarin-treated patients of Korean descend. The mean OWD was 3.30 ± 1.34 (range: 1-9) mg/d. Homozygous wild-type patients required lower OWD (3.1 ± 1.1 mg/d vs. 4.7 ± 1.8 mg/d, P < 0.001) for VKORC1 and higher OWD for both CYP2C9 (3.4 ± 1.3 mg/d vs. 2.3 ± 1.1 mg/d, P = 0.002) and CYP4F2 (3.0 ± 1.2 mg/d vs. 3.4 ± 1.3 mg/d vs. 4.0 ± 1.7 mg/d, P = 0.033) than those carrying heterozygote genes.

CONCLUSIONS

Korean patients exhibit different VKORC1, CYP2C9, and CYP4F2 profiles impacting lower OWD in Eastern Asians than required in Caucasians. Universal international OWD guidelines may consider patient ethnicity as a confounder; however, this hypothesis needs further clarification.

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

BACKGROUND

Personalized warfarin dosing is influenced by various factors including genetic and non-genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non-linear association between covariates and stable warfarin dose.

OBJECTIVE

To extend the multiple linear regression algorithm for personalized warfarin dosing in a Korean population and compare with a machine learning--based algorithm.

METHOD

From this cohort study, we collected information on 650 patients taking warfarin who achieved steady state including demographic information, indications, comorbidities, comedications, habits, and genetic factors. The dataset was randomly split into training set (90%) and test set (10%). The LR and machine learning (gradient boosting machine [GBM]) models were developed on the training set and were evaluated on the test set.

RESULT

LR and GBM models were comparable in terms of accuracy of ideal dose (75.38% and 73.85%), correlation (0.77 and 0.73), mean absolute error (0.58 mg/day and 0.64 mg/day), and root mean square error (0.82 mg/day and 0.9 mg/day), respectively. VKORC1 genotype, CYP2C9 genotype, age, and weight were the highest contributors and could obtain 80% of maximum performance in both models.

CONCLUSION

This study shows that our LR and GMB models are satisfactory to predict warfarin dose in our dataset. Both models showed similar performance and feature contribution characteristics. LR may be the appropriate model due to its simplicity and interpretability.

Comparison of the prediction performance of different warfarin dosing algorithms based on Chinese patients

Aim: To compare the prediction performance of different warfarin dosing algorithms based on Chinese patients. Materials & methods: A total of 18 algorithms were tested in 325 patients. The predictive efficacy of selected algorithms was evaluated by calculating the percentage of patients whose predicted dose fell within ±20% of their actual stable warfarin dose and the mean absolute error. Results: The percentage within ± 20% and the mean absolute error of the algorithms ranged from 11.9 to 41.2% and -0.20 (-0.29 to -0.11) mg/d to -1.63 (-1.75 to -1.50) mg/d. The algorithms established by Miao et al. and Wei et al. had optimal predictive performance. Conclusion: Algorithms based on geographical populations might be more suitable for the prediction of stable warfarin doses in local patients.

Development and Validation of a Novel Warfarin Dosing Algorithm for Korean Patients With VKORC1 1173C

BACKGROUND

Differences in the performance of suggested warfarin dosing algorithms among different ethnicities and genotypes have been reported; this necessitates the development of an algorithm with enhanced performance for specific population groups. Previous warfarin dosing algorithms underestimated warfarin doses in VKORC1 1173C carriers. We aimed to develop and validate a new warfarin dosing algorithm for Korean patients with VKORC1 1173C.

METHODS

A total of 109 patients carrying VKORC1 1173CT (N=105) or 1173CC (N=4) were included in this study. Multiple regression analysis was performed to deduce a new dosing algorithm. Following literature searches for genotype-guided warfarin dosing algorithms, 21 algorithms were selected and evaluated using the correlation coefficient (ρ) of actual dose and estimated dose, mean error, and root mean square error.

RESULTS

The developed algorithm is as follows: maintenance dose (mg/week)=exp [3.223-0.009×(age)+0.577×(body surface area [BSA])+0.178×(sex)-0.481×(CYP2C9 genotype)+0.227×(VKORC1 genotype)]. Integrated variables explained 44% of the variance in the maintenance dose. The predicted and actual doses showed moderate correlation (ρ=0.641) with the best performance with a mean error of -1.30 mg/week. The proportion of underestimated groups was 17%, which was lower than with the other algorithms.

CONCLUSIONS

This is the first study to develop and validate a warfarin dosing algorithm based on data from VKORC1 1173C carriers; it showed superior predictive performance compared with previously published algorithms.

Precision medicine: does ethnicity information complement genotype-based prescribing decisions?

Inter-ethnic differences in drug response are all too well known. These are underpinned by a number of factors, including pharmacogenetic differences across various ethnic populations. Precision medicine relies on genotype-based prescribing decisions with the aim of maximizing efficacy and mitigating the risks. When there is no access to genotyping tests, ethnicity is frequently regarded as a proxy of the patient's probable genotype on the basis of overall population-based frequency of genetic variations in the ethnic group the patient belongs to, with some variations being ethnicity-specific. However, ever-increasing transcontinental migration of populations and the resulting admixing of populations have undermined the utility of self-identified ethnicity in predicting the genetic ancestry, and therefore the genotype, of the patient. An example of the relevance of genetic ancestry of a patient is the inadequate performance of European-derived pharmacogenetic dosing algorithms of warfarin in African Americans, Brazilians and Caribbean Hispanics. Consequently, genotyping a patient potentially requires testing for all known clinically actionable variants that the patient may harbour, and new variants that are likely to be identified using state-of the art next-generation sequencing-based methods. Furthermore, self-identified ethnicity is associated with a number of ethnicity-related attributes and non-genetic factors that potentially influence the risk of phenoconversion (genotype-phenotype discordance), which may adversely impact the success of genotype-based prescribing decisions. Therefore, while genotype-based prescribing decisions are important in implementing precision medicine, ethnicity should not be disregarded.