European Journal of Clinical Pharmacology

, Volume 70, Issue 3, pp 265–273

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

  • Hussain A. Isma’eel
  • George E. Sakr
  • Robert H. Habib
  • Mohamad Musbah Almedawar
  • Nathalie K. Zgheib
  • Imad H. Elhajj



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.


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.


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 %.


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.


Artificial neural network Least-squares modeling Anticoagulation Pharmacogenetics INR 

Supplementary material

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  1. 1.
    Ellis MH (2004) Artificial neural networks for anticoagulant management–think again! Isr Med Assoc J: IMAJ 6(12):770–771PubMedGoogle Scholar
  2. 2.
    Biss TT, Avery PJ, Brandao LR, Chalmers EA, Williams MD, Grainger JD, Leathart JB, Hanley JP, Daly AK, Kamali F (2012) VKORC1 and CYP2C9 genotype and patient characteristics explain a large proportion of the variability in warfarin dose requirement among children. Blood 119(3):868–873. doi:10.1182/blood-2011-08-372722 PubMedCrossRefGoogle Scholar
  3. 3.
    Fuster V, Ryden LE, Asinger RW, Cannom DS, Crijns HJ, Frye RL, Halperin JL, Kay GN, Klein WW, Levy S, McNamara RL, Prystowsky EN, Wann LS, Wyse DG, Gibbons RJ, Antman EM, Alpert JS, Faxon DP, Gregoratos G, Hiratzka LF, Jacobs AK, Russell RO, Smith SC Jr, Alonso-Garcia A, Blomstrom-Lundqvist C, de Backer G, Flather M, Hradec J, Oto A, Parkhomenko A, Silber S, Torbicki A (2001) ACC/AHA/ESC Guidelines for the Management of Patients With Atrial Fibrillation: Executive Summary A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines and Policy Conferences (Committee to Develop Guidelines for the Management of Patients With Atrial Fibrillation) Developed in Collaboration With the North American Society of Pacing and Electrophysiology. Circulation 104(17):2118–2150PubMedGoogle Scholar
  4. 4.
    Burnett B (2013) Management of venous thromboembolism. Prim Care 40(1):73–90. doi:10.1016/j.pop.2012.11.004 PubMedCrossRefGoogle Scholar
  5. 5.
    Seiler C (2004) Management and follow up of prosthetic heart valves. Heart 90(7):818–824. doi:10.1136/hrt.2003.025049 PubMedCrossRefGoogle Scholar
  6. 6.
    Robert-Ebadi H, Le Gal G, Righini M (2009) Use of anticoagulants in elderly patients: practical recommendations. Clin Interv Aging 4:165–177PubMedCentralPubMedGoogle Scholar
  7. 7.
    Palareti G, Cosmi B (2009) Bleeding with anticoagulation therapy - who is at risk, and how best to identify such patients. Thromb Haemost 102(2):268–278. doi:10.1160/th08-11-0730 PubMedGoogle Scholar
  8. 8.
    Palareti G (2011) Bleeding with anticoagulant treatments. Hamostaseologie 31(4):237–242. doi:10.5482/ha-1151 PubMedCrossRefGoogle Scholar
  9. 9.
    Ansell J, Hirsh J, Hylek E, Jacobson A, Crowther M, Palareti G (2008) Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest 133(6 Suppl):160S–198S. doi:10.1378/chest.08-0670 PubMedCrossRefGoogle Scholar
  10. 10.
    Pirmohamed M (2006) Warfarin: almost 60 years old and still causing problems. Br J Clin Pharmacol 62(5):509–511. doi:10.1111/j.1365-2125.2006.02806.x PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Wittkowsky AK, Devine EB (2004) Frequency and causes of overanticoagulation and underanticoagulation in patients treated with warfarin. Pharmacotherapy 24(10):1311–1316PubMedCrossRefGoogle Scholar
  12. 12.
    Esmerian MO, Mitri Z, Habbal MZ, Geryess E, Zaatari G, Alam S, Skouri HN, Mahfouz RA, Taher A, Zgheib NK (2011) Influence of CYP2C9 and VKORC1 polymorphisms on warfarin and acenocoumarol in a sample of Lebanese people. J Clin Pharmacol 51(10):1418–1428. doi:10.1177/0091270010382910 PubMedCrossRefGoogle Scholar
  13. 13.
    Sconce EA, Khan TI, Wynne HA, Avery P, Monkhouse L, King BP, Wood P, Kesteven P, Daly AK, Kamali F (2005) The impact of CYP2C9 and VKORC1 genetic polymorphism and patient characteristics upon warfarin dose requirements: proposal for a new dosing regimen. Blood 106(7):2329–2333. doi:10.1182/blood-2005-03-1108 PubMedCrossRefGoogle Scholar
  14. 14.
    Carlquist JF, Horne BD, Muhlestein JB, Lappe DL, Whiting BM, Kolek MJ, Clarke JL, James BC, Anderson JL (2006) Genotypes of the cytochrome p450 isoform, CYP2C9, and the vitamin K epoxide reductase complex subunit 1 conjointly determine stable warfarin dose: a prospective study. J Thromb Thrombolysis 22(3):191–197. doi:10.1007/s11239-006-9030-7 PubMedCrossRefGoogle Scholar
  15. 15.
    Wu AH, Wang P, Smith A, Haller C, Drake K, Linder M, Valdes R Jr (2008) Dosing algorithm for warfarin using CYP2C9 and VKORC1 genotyping from a multi-ethnic population: comparison with other equations. Pharmacogenomics 9(2):169–178. doi:10.2217/14622416.9.2.169 PubMedCrossRefGoogle Scholar
  16. 16.
    Nunnelee JD (2009) Review of an Article: The international Warfarin Pharmacogenetics Consortium (2009). Estimation of the warfarin dose with clinical and pharmacogenetic data. NEJM 360 (8): 753–64. J Vasc Nurs: Off Publ Soc Peripher Vasc Nurs 27(4):109CrossRefGoogle Scholar
  17. 17.
    Limdi NA, Wadelius M, Cavallari L, Eriksson N, Crawford DC, Lee MT, Chen CH, Motsinger-Reif A, Sagreiya H, Liu N, Wu AH, Gage BF, Jorgensen A, Pirmohamed M, Shin JG, Suarez-Kurtz G, Kimmel SE, Johnson JA, Klein TE, Wagner MJ (2010) Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood 115(18):3827–3834. doi:10.1182/blood-2009-12-255992 PubMedCrossRefGoogle Scholar
  18. 18.
    Caraco Y, Blotnick S, Muszkat M (2008) CYP2C9 genotype-guided warfarin prescribing enhances the efficacy and safety of anticoagulation: a prospective randomized controlled study. Clin Pharmacol Ther 83(3):460–470. doi:10.1038/sj.clpt.6100316 PubMedCrossRefGoogle Scholar
  19. 19.
    Wadelius M, Chen LY, Lindh JD, Eriksson N, Ghori MJ, Bumpstead S, Holm L, McGinnis R, Rane A, Deloukas P (2009) The largest prospective warfarin-treated cohort supports genetic forecasting. Blood 113(4):784–792. doi:10.1182/blood-2008-04-149070 PubMedCrossRefGoogle Scholar
  20. 20.
    Lenzini P, Wadelius M, Kimmel S, Anderson JL, Jorgensen AL, Pirmohamed M, Caldwell MD, Limdi N, Burmester JK, Dowd MB, Angchaisuksiri P, Bass AR, Chen J, Eriksson N, Rane A, Lindh JD, Carlquist JF, Horne BD, Grice G, Milligan PE, Eby C, Shin J, Kim H, Kurnik D, Stein CM, McMillin G, Pendleton RC, Berg RL, Deloukas P, Gage BF (2010) Integration of genetic, clinical, and INR data to refine warfarin dosing. Clinical Pharmacol Ther 87(5):572–578. doi:10.1038/clpt.2010.13 CrossRefGoogle Scholar
  21. 21.
    Epstein RS, Moyer TP, Aubert RE, Kane DJ O, Xia F, Verbrugge RR, Gage BF, Teagarden JR (2010) Warfarin genotyping reduces hospitalization rates results from the MM-WES (Medco-Mayo Warfarin Effectiveness study). J Am Coll Cardiol 55(25):2804–2812. doi:10.1016/j.jacc.2010.03.009 PubMedCrossRefGoogle Scholar
  22. 22.
    Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE, Lee MT, Limdi NA, Page D, Roden DM, Wagner MJ, Caldwell MD, Johnson JA (2009) Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med 360(8):753–764. doi:10.1056/NEJMoa0809329 PubMedCrossRefGoogle Scholar
  23. 23.
    Ugrinowitsch C, Fellingham GW, Ricard MD (2004) Limitations of ordinary least squares models in analyzing repeated measures data. Med Sci Sports Exerc 36(12):2144–2148PubMedCrossRefGoogle Scholar
  24. 24.
    Zou J, Han Y, So SS (2008) Overview of artificial neural networks. Methods Mol Biol 458:15–23PubMedGoogle Scholar
  25. 25.
    Freeman RV, Eagle KA, Bates ER, Werns SW, Kline-Rogers E, Karavite D, Moscucci M (2000) Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty. Am Heart J 140(3):511–520. doi:10.1067/mhj.2000.109223 PubMedCrossRefGoogle Scholar
  26. 26.
    Purwanto EC, Logeswaran R, Abdul Rahman AR (2012) Prediction models for early risk detection of cardiovascular event. J Med Syst 36(2):521–531PubMedCrossRefGoogle Scholar
  27. 27.
    Byrne SCP, Barry A, Graham I, Delaney T, Corrigan OI (2000) Using Neural Nets for Decision Support in Prescription and Outcome Prediction in Anticoagulation Drug Therapy The Fifth Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2000) Workshop Notes of the 14th European Conference on Artificial Intelligence (ECAI-2000)Google Scholar
  28. 28.
    Narayanan MN, Lucas SB (1993) A genetic algorithm to improve a neural network to predict a patient’s response to warfarin. Methods Inf Med 32(1):55–58PubMedGoogle Scholar
  29. 29.
    Smith BP, Ward RA, Brier ME (1998) Prediction of anticoagulation during hemodialysis by population kinetics and an artificial neural network. Artif Organs 22(9):731–739PubMedCrossRefGoogle Scholar
  30. 30.
    Reingold E (1999) Artificial Neural Networks. In: Artificial Intelligence Tutorial Reviewed. University of Toronto MississaugaGoogle Scholar
  31. 31.
    Dybowski R, Weller P, Chang R, Gant V (1996) Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 347(9009):1146–1150PubMedCrossRefGoogle Scholar
  32. 32.
    Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, Soranzo N, Whittaker P, Ranganath V, Kumanduri V, McLaren W, Holm L, Lindh J, Rane A, Wadelius M, Deloukas P (2009) A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 5(3):e1000433. doi:10.1371/journal.pgen.1000433 PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Hagan MT (1996) Neural network design. PWS Publishing Co., BostonGoogle Scholar
  34. 34.
    L. S (1985) Introduction to non-linear optimization. City & Guilds, MacMillanGoogle Scholar
  35. 35.
    Vanyrur R, Heberger K, Kovesdi I, Jakus J (2002) Prediction of tumoricidal activity and accumulation of photosensitizers in photodynamic therapy using multiple linear regression and artificial neural networks. Photochem Photobiol 75(5):471–478PubMedCrossRefGoogle Scholar
  36. 36.
    Gage BF, Eby C, Johnson JA, Deych E, Rieder MJ, Ridker PM, Milligan PE, Grice G, Lenzini P, Rettie AE, Aquilante CL, Grosso L, Marsh S, Langaee T, Farnett LE, Voora D, Veenstra DL, Glynn RJ, Barrett A, McLeod HL (2008) Use of pharmacogenetic and clinical factors to predict the therapeutic dose of warfarin. Clin Pharmacol Ther 84(3):326–331. doi:10.1038/clpt.2008.10 PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Fragkaki AG, Farmaki E, Thomaidis N, Tsantili-Kakoulidou A, Angelis YS, Koupparis M, Georgakopoulos C (2012) Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. J Chromatogr A 1256:232–239. doi:10.1016/j.chroma.2012.07.064 PubMedCrossRefGoogle Scholar
  38. 38.
    Price RK, Spitznagel EL, Downey TJ, Meyer DJ, Risk NK, el-Ghazzawy OG (2000) Applying artificial neural network models to clinical decision making. Psychol Assess 12(1):40–51PubMedCrossRefGoogle Scholar
  39. 39.
    Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346(8983):1135–1138PubMedCrossRefGoogle Scholar
  40. 40.
    Baxt WG (1990) Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion. Neural Comput 2(4):480–489CrossRefGoogle Scholar
  41. 41.
    Papadourakis GM, Gaga E, Vareltzis G, Bebis G. Use of artificial neural networks for clinical decision-making (Maldescensus testis)Google Scholar
  42. 42.
    Pengo V, Crippa L, Falanga A, Finazzi G, Marongiu F, Moia M, Palareti G, Poli D, Testa S, Tiraferri E, Tosetto A, Tripodi A, Siragusa S, Manotti C (2012) Phase III studies on novel oral anticoagulants for stroke prevention in atrial fibrillation: a look beyond the excellent results. J Thromb Haemost: JTH 10(10):1979–1987. doi:10.1111/j.1538-7836.2012.04866.x PubMedCrossRefGoogle Scholar
  43. 43.
    Connolly SJ, Ezekowitz MD, Yusuf S, Eikelboom J, Oldgren J, Parekh A, Pogue J, Reilly PA, Themeles E, Varrone J, Wang S, Alings M, Xavier D, Zhu J, Diaz R, Lewis BS, Darius H, Diener HC, Joyner CD, Wallentin L (2009) Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med 361(12):1139–1151. doi:10.1056/NEJMoa0905561 PubMedCrossRefGoogle Scholar
  44. 44.
    Avorn J (2011) The relative cost-effectiveness of anticoagulants: obvious, except for the cost and the effectiveness. Circulation 123(22):2519–2521. doi:10.1161/circulationaha.111.030148 PubMedCrossRefGoogle Scholar
  45. 45.
    Gong IY, Tirona RG, Schwarz UI, Crown N, Dresser GK, Larue S, Langlois N, Lazo-Langner A, Zou G, Roden DM, Stein CM, Rodger M, Carrier M, Forgie M, Wells PS, Kim RB (2011) Prospective evaluation of a pharmacogenetics-guided warfarin loading and maintenance dose regimen for initiation of therapy. Blood 118(11):3163–3171. doi:10.1182/blood-2011-03-345173 PubMedCrossRefGoogle Scholar
  46. 46.
    Anderson JL, Horne BD, Stevens SM, Grove AS, Barton S, Nicholas ZP, Kahn SF, May HT, Samuelson KM, Muhlestein JB, Carlquist JF (2007) Randomized trial of genotype-guided versus standard warfarin dosing in patients initiating oral anticoagulation. Circulation 116(22):2563–2570. doi:10.1161/circulationaha.107.737312 PubMedCrossRefGoogle Scholar
  47. 47.
    Horne BD, Lenzini PA, Wadelius M, Jorgensen AL, Kimmel SE, Ridker PM, Eriksson N, Anderson JL, Pirmohamed M, Limdi NA, Pendleton RC, McMillin GA, Burmester JK, Kurnik D, Stein CM, Caldwell MD, Eby CS, Rane A, Lindh JD, Shin JG, Kim HS, Angchaisuksiri P, Glynn RJ, Kronquist KE, Carlquist JF, Grice GR, Barrack RL, Li J, Gage BF (2012) Pharmacogenetic warfarin dose refinements remain significantly influenced by genetic factors after one week of therapy. Thromb Haemost 107(2):232–240. doi:10.1160/th11-06-0388 PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Wieloch M, Sjalander A, Frykman V, Rosenqvist M, Eriksson N, Svensson PJ (2011) Anticoagulation control in Sweden: reports of time in therapeutic range, major bleeding, and thrombo-embolic complications from the national quality registry AuriculA. Eur Heart J 32(18):2282–2289. doi:10.1093/eurheartj/ehr134 PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hussain A. Isma’eel
    • 1
    • 4
  • George E. Sakr
    • 2
    • 4
  • Robert H. Habib
    • 1
    • 4
  • Mohamad Musbah Almedawar
    • 1
    • 4
  • Nathalie K. Zgheib
    • 3
    • 4
  • Imad H. Elhajj
    • 2
    • 4
  1. 1.Division of Cardiology, Department of Internal MedicineAmerican University of BeirutBeirutLebanon
  2. 2.Department of Electrical & Computer EngineeringAmerican University of BeirutBeirutLebanon
  3. 3.Department of Pharmacology and ToxicologyAmerican University of Beirut Medical CenterBeirutLebanon
  4. 4.Vascular Medicine ProgramAmerican University of Beirut Medical CenterBeirutLebanon

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