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Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method

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Abstract

Background

The unpredictability of acenocoumarol dose needed to achieve target blood thinning level remains a challenge. We aimed to apply and compare a pharmacogenetic least-squares model (LSM) and artificial neural network (ANN) models for predictions of acenocoumarol dosing.

Methods

LSM and ANN models were used to analyze previously collected data on 174 participants (mean age: 67.45 SD 13.49 years) on acenocoumarol maintenance therapy. The models were based on demographics, lifestyle habits, concomitant diseases, medication intake, target INR, and genotyping results for CYP2C9 and VKORC1. LSM versus ANN performance comparisons were done by two methods: by randomly splitting the data as 50 % derivation and 50 % validation cohort followed by a bootstrap of 200 iterations, and by a 10-fold leave-one-out cross-validation technique.

Results

The ANN-based pharmacogenetic model provided higher accuracy and larger R value than all other LSM-based models. The accuracy percentage improvement ranged between 5 % and 24 % for the derivation cohort and between 12 % and 25 % for the validation cohort. The increase in R value ranged between 6 % and 31 % for the derivation cohort and between 2 % and 31 % for the validation cohort. ANN increased the percentage of accurately dosed subjects (mean absolute error ≤1 mg/week) by 14.1 %, reduced the percentage of mis-dosed subjects (mean absolute error 2-3 mg/week) by 7.04 %, and reduced the percentage of grossly mis-dosed subjects (mean absolute error ≥4 mg/week) by 24 %.

Conclusions

ANN-based pharmacogenetic guidance of acenocoumarol dosing reduces the error in dosing to achieve target INR. These results need to be ascertained in a prospective study.

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Acknowledgments

We wish to acknowledge Dr. Habib Dakik and Dr. Mohammad El Obeidy for reviewing this manuscript.

Author Contributions

Dr. Isma’eel A Hussain: conception and design, analysis and interpretation of data, drafting and revising the article, and final approval of the manuscript.

Dr. George E Sakr: Conception and Design, Analysis and interpretation of data, drafting and revising the article, and final approval of the manuscript.

Dr. Robert H Habib: Analysis and interpretation of data, drafting and revising the article, and final approval

Mr. Mohamad M Almedawar: Analysis and interpretation of data, drafting and revising the article, and final approval of the manuscript.

Dr. Nathalie K Zgheib: Acquisition of data, analysis and interpretation of data, critically revising the manuscript for important intellectual content, and final approval of the manuscript.

Dr. Imad H Elhajj: Conception and design, analysis and interpretation of data; drafting the article or revising it critically for important intellectual content; and final approval of the manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.

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Correspondence to Nathalie K. Zgheib or Imad H. Elhajj.

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Dr. Isma’eel and Dr. Sakr are co-authors of this manuscript with equal contribution

All authors takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation

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Isma’eel, H.A., Sakr, G.E., Habib, R.H. et al. Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method. Eur J Clin Pharmacol 70, 265–273 (2014). https://doi.org/10.1007/s00228-013-1617-2

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