Intensive Care Medicine

, Volume 40, Issue 9, pp 1332–1339 | Cite as

A data-driven approach to optimized medication dosing: a focus on heparin

  • Mohammad M. Ghassemi
  • Stefan E. Richter
  • Ifeoma M. Eche
  • Tszyi W. Chen
  • John Danziger
  • Leo A. Celi
Original

Abstract

Purpose

To demonstrate a novel method that utilizes retrospective data to develop statistically optimal dosing strategies for medications with sensitive therapeutic windows. We illustrate our approach on intravenous unfractionated heparin, a medication which typically considers only patient weight and is frequently misdosed.

Methods

We identified available clinical features which impact patient response to heparin and extracted 1,511 patients from the multi-parameter intelligent monitoring in intensive care II database which met our inclusion criteria. These were used to develop two multivariate logistic regressions, modeling sub- and supra-therapeutic activated partial thromboplastin time (aPTT) as a function of clinical features. We combined information from these models to estimate an initial heparin dose that would, on a per-patient basis, maximize the probability of a therapeutic aPTT within 4–8 h of the initial infusion. We tested our model’s ability to classifying therapeutic outcomes on a withheld dataset and compared performance to a weight-alone alternative using volume under surface (VUS) (a multiclass version of AUC).

Results

We observed statistically significant associations between sub- and supra-therapeutic aPTT, race, ICU type, gender, heparin dose, age and Sequential Organ Failure Assessment scores with mean validation AUC of 0.78 and 0.79 respectively. Our final model improved outcome classification over the weight-alone alternative, with VUS values of 0.48 vs. 0.42.

Conclusions

This work represents an important step in the secondary use of health data in developing models to optimize drug dosing. The next step would be evaluating whether this approach indeed achieves target aPTT more reliably than the current weight-based heparin dosing in a randomized controlled trial.

Keywords

Observational Heparin Clinical informatics Dosing Optimization 

Supplementary material

134_2014_3406_MOESM1_ESM.doc (648 kb)
Supplementary material 1 (DOC 647 kb)

References

  1. 1.
    Celi LA, Mark RG, Stone DJ, Montgomerey RA (2013) Big Data” in the Intensive Care Unit. Closing the Data Loop. Am J Resp Crit Care Med 187(11):1157–1160PubMedCrossRefGoogle Scholar
  2. 2.
    Alban S (2012) Adverse effects of heparin. In: Lever R, Mulloy B, Page CP (eds) Heparin—A Century of Progress. Springer, Berlin, pp 211–263CrossRefGoogle Scholar
  3. 3.
    Raschke RA, Reilly BM, Guidry JR, Fontana JR, Srinivas S (1993) The weight-based heparin dosing nomogram compared with a standard care nomogram: a randomized controlled trial. Ann Int Med 119(9):874–881PubMedCrossRefGoogle Scholar
  4. 4.
    Hirsh J, Raschke R (2004) Heparin and low-molecular-weight heparin - the seventh ACCP conference on antithrombotic and thrombolytic therapy. Chest 126(3 suppl):188S–203SPubMedCrossRefGoogle Scholar
  5. 5.
    Cruickshank MK, Levine MN, Hirsh J, Roberts R, Siguenza M (1991) A standard heparin nomogram for the management of heparin therapy. Arch Intern Med 151(2):333–337PubMedCrossRefGoogle Scholar
  6. 6.
    Hirsh Jack et al (2001) Guide to anticoagulant therapy: heparin a statement for healthcare professionals from the American Heart Association. Circulation 103(24):2994–3018PubMedCrossRefGoogle Scholar
  7. 7.
    Krishnaswamy Amar, Michael Lincoff A, Cannon CP (2010) The use and limitations of unfractionated heparin. Crit Pathw Cardiol 9(1):35–40PubMedCrossRefGoogle Scholar
  8. 8.
    Lee MS, Wali AU, Menon V et al (2001) The determinants of activated partial thromboplastin time, relation of activated partial thromboplastin time to clinical outcomes, and optimal dosing regimens for heparin treated patients with acute coronary syndromes: a review of gusto-IIb. J Thromb Thrombolysis 14(2):91–101CrossRefGoogle Scholar
  9. 9.
    Raschke RA, Gollihare B, Peirce JC (1996) The effectiveness of implementing the weight-based heparin nomogram as a practice guideline. Arch Intern Med 156(15):1645–1649PubMedCrossRefGoogle Scholar
  10. 10.
    Melloni C, Alexander KP, Chen AY et al (2008) Unfractionated heparin dosing and risk of major bleeding in non-ST-segment elevation acute coronary syndromes. Am Heart J 156(2):209–215PubMedCrossRefGoogle Scholar
  11. 11.
    Grand’Maison A, Charest AF, Geerts WH (2005) Anticoagulant use in patients with chronic renal impairment. Am J Cardiovasc Drugs 5(5):291–305PubMedCrossRefGoogle Scholar
  12. 12.
    Saeed M, Villarroel M, Reisner AT et al (2011) Multiparameter intelligent monitoring in intensive care II (MIMIC-II): a public-access intensive care unit database. Crit Care Med 39(5):952–960PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Elixhauser A, Steiner C, Harris DR, Cofey RM (1998) Comorbidity measures for use with administrative data. Med Care 36(1):8–27PubMedCrossRefGoogle Scholar
  14. 14.
    van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ (2009) A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care 47(6):626–633PubMedCrossRefGoogle Scholar
  15. 15.
    César F, Hernández-Orallo J, Salido MA (2003) Volume under the ROC Surface for Multi-class Problems. Machine learning: ECML 2003. Springer, Berlin, 108–120Google Scholar
  16. 16.
    Schaden E, Metnitz PG, Pfanner G et al (2012) Coagulation Day 2010: an Austrian survey on the routine of thromboprophylaxis in intensive care. Intens Care Med 38:984–990CrossRefGoogle Scholar
  17. 17.
    Guervil David J et al (2011) Activated partial thromboplastin time versus antifactor Xa heparin assay in monitoring unfractionated heparin by continuous intravenous infusion. Ann Pharmacother 45(7-8):861–868PubMedCrossRefGoogle Scholar
  18. 18.
    Imhoff M, Webb A, Goldschmidt A (2001) Health Informatics. Intens Care Med 27:179–186CrossRefGoogle Scholar
  19. 19.
    Squara P, Foruquet E, Jacquet L et al (2003) A computer program for interpreting pulmonary artery catheterization data: results of the European HEMODYN Resident Study. Intens Care Med 29:735–741Google Scholar
  20. 20.
    Meyfroidt G, Wouters P, De Becker W, Cottem D, Van den Berghe G (2011) Impact of a computer-generated alert system on the quality of tight glycemic control. Intens Care Med 37:1151–1157CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2014

Authors and Affiliations

  • Mohammad M. Ghassemi
    • 1
  • Stefan E. Richter
    • 2
  • Ifeoma M. Eche
    • 3
  • Tszyi W. Chen
    • 3
  • John Danziger
    • 3
  • Leo A. Celi
    • 1
    • 3
  1. 1.Laboratory for Computational PhysiologyMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.University of California Los AngelesLos AngelesUSA
  3. 3.Beth Israel Deaconess Medical CenterBostonUSA

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