Pharmacodynamic Modeling of Warfarin Dosing Algorithm for Cardiovascular Patients in Indonesia: A Tailored Method to Anticoagulation Therapy.

Norisca Aliza Putriana, Irma Rahayu Latarissa, Taofik Rusdiana, Tina Rostinawati, Mohammad Rizki Akbar
Author Information
  1. Norisca Aliza Putriana: Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia.
  2. Irma Rahayu Latarissa: Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia. ORCID
  3. Taofik Rusdiana: Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia. ORCID
  4. Tina Rostinawati: Department of Biological Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia. ORCID
  5. Mohammad Rizki Akbar: Department of Cardiovascular, Faculty of Medicine, Universitas Padjadjaran, Sumedang, Indonesia. ORCID

Abstract

Purpose: Warfarin is an anticoagulant drug widely used for treating thromboembolism-related conditions. The main challenge with this drug is the high variability in patients response, which is influenced by both clinical, non-clinical, and genetic factors, such as , and . Therefore, this research aimed to evaluate the impact of clinical and genetic factors on warfarin dose adjustment and to develop a dosing algorithm for patients with cardiovascular disease.
Patients and Methods: A total of 77 research subjects were selected using consecutive sampling based on the inclusion criteria of cardiac outpatients on warfarin for ≥3 months with PT-INR data, complete medical records, and willingness to participate. Exclusion criteria included vitamin K use and inability to follow up. Patients demographic data and clinical characteristics were collected from medical records. Blood samples were obtained for genetic testing of (sequencing). Statistical analyses included both bivariate and multivariate analyses (logistic regression) with a significance level set at <0.05.
Results: Statistical analysis using the Kruskal-Wallis test showed that the CC, CT, and TT genotypes were significantly associated with warfarin dose (p = 0.02). Furthermore, the Mann-Whitney test results showed that gender did not have a significant relationship with warfarin dose (p = 0.16). The Spearman Rank correlation test showed that age (p = 0.02) and BMI (p = 0.03) had significant relationships with warfarin dose (p < 0.05). However, gender (p = 0.89) had no effect, while age (p = 0.01), BMI (p = 0.01), and genotype (p = 0.01) significantly influenced warfarin dose determination.
Conclusion: In conclusion, the combined contribution of age (8.76%), BMI (7.95%), and genotype (8.29%) to warfarin dose adjustment was 25%. The linear regression model for predicting warfarin dose was determined to be y = 12.736-0.16*age + 0.55*BMI + 3.55*genotype, where 1 = CC, 2 = CT, and 3 = TT.

Keywords

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MeSH Term

Humans
Warfarin
Anticoagulants
Male
Female
Middle Aged
Indonesia
Algorithms
Aged
Dose-Response Relationship, Drug
Cytochrome P450 Family 4
Cardiovascular Diseases
Adult
Genotype

Chemicals

Warfarin
Anticoagulants
Cytochrome P450 Family 4
CYP4F2 protein, human

Word Cloud

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