Changing from mandatory to optional genotyping results in higher acceptance of pharmacist-guided warfarin dosing

Aim: We evaluated the clinical acceptance and feasibility of a pharmacist-guided personalized consult service following its transition from a mandatory (mPGx) to optional (oPGx) CYP2C9/ VKORC1/ CYP4F2 genotyping for warfarin. Methods: A total of 1105 patients were included. Clinical acceptance and feasibility outcomes were analyzed using bivariate and multivariable analyses. Results: After transitioning to optional genotyping, genotype testing was still ordered in a large segment of the eligible population (52.1%). Physician acceptance of pharmacist-recommended doses improved from 83.9% (mPGx) to 86.6% (oPGx; OR: 1.3; 95% CI: 1.1–1.5; p = 0.01) with a shorter median genotype result turnaround time (oPGX: 23.6 hr vs mPGX: 25.1 hr ; p < 0.01). Conclusion: Ordering of genotype testing and provider acceptance of dosing recommendations remained high after transitioning to optional genotyping.

Development of a system to support warfarin dose decisions using deep neural networks

The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1–4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospitals (n = 12,673). The performance of 5th-day PT INR prediction was compared with 2000 predictions made by 10 expert physicians. A generator of individualized warfarin dose-PT INR tables which simulated the repeated administration of varying doses of warfarin was developed based on the prediction model. The algorithm outperformed humans with accuracy terms of within ± 0.3 of the actual value (machine learning algorithm: 10,650/12,673 cases (84.0%), expert physicians: 1647/2000 cases (81.9%), P = 0.014). In the individualized warfarin dose-PT INR tables generated by the algorithm, the 8th-day PT INR predictions were within 0.3 of actual value in 450/842 cases (53.4%). An artificial intelligence-based warfarin dosing algorithm using a recurrent neural network outperformed expert physicians in predicting future PT INRs. An individualized warfarin dose-PT INR table generator which was constructed based on this algorithm was acceptable.

Identification of ancestry proportions in admixed groups across the Americas using clinical pharmacogenomic SNP panels

We evaluated the performance of three PGx panels to estimate biogeographical ancestry: the DMET panel, and the VIP and Preemptive PGx panels described in the literature. Our analysis indicate that the three panels capture quite well the individual variation in admixture proportions observed in recently admixed populations throughout the Americas, with the Preemptive PGx and DMET panels performing better than the VIP panel. We show that these panels provide reliable information about biogeographic ancestry and can be used to guide the implementation of PGx clinical decision-support (CDS) tools. We also report that using these panels it is possible to control for the effects of population stratification in association studies in recently admixed populations, as exemplified with a warfarin dosing GWA study in a sample from Brazil.

The Utilization of Warfarin Dosing Algorithms in China: An Electronic Survey Study

Background: Warfarin, a key anticoagulant medication, has a narrow therapeutic window and individual difference. Many warfarin dosing algorithms have been developed and been proved to have clinical benefits. However, the utilization of algorithms by medical professionals in China was unknown. We conducted an online survey to investigate the use and requirements of warfarin dosing algorithms among Chinese medical professionals. Method: A questionnaire survey was conducted via WeChat to investigate the utilization of warfarin dosing algorithms by medical professionals in seven regions of China. The content of questionnaire was included general characteristics of participants, condition of anticoagulant therapy, utilization of warfarin dosing algorithms and demand for function of an assistant warfarin dosing system we proposed.Results: A total of 399 participants completed the survey. Although most medical professionals use warfarin for anticoagulant therapy, some of them (15.04%) were not familiar with warfarin’s individual difference. As high as 32.97% of clinicians ruled out genotypes when using warfarin. The vast majority of anticoagulant medical professionals believed warfarin dosing algorithms can have clinical benefits, but only 20.80% of them usually use algorithms for anticoagulant therapy. Conclusion: Warfarin dosing algorithms have high evaluation while not good utilization among Chinese anticoagulant medical professionals. If warfarin dosing algorithms and assistant tools aimed to Chinese population were developed, to some extent can improve anticoagulant therapy in China.