Abstract
The prescription of antithrombotic agents in the elderly depends, to certain degree, on the identity of the unique individual elderly patient. This dependence cannot be captured by viewing the patient as belonging to a group, but rather by viewing age in the context of unique individual biology. This context is historical, physiological, psychological, and time- and location-dependent. The group-based approach to patient therapy is found in evidence-based medicine, typified by the large double-blind randomised clinical trial from which clinical recommendations are defined. An alternative approach to capturing the unique context of each patient is based using fuzzy logic and mathematics. In particular, the fuzzy subsethood theorem of Kosko has direct application here.
A new measure of clinical efficiency, K, derived from the fuzzy subsethood theorem has redefined the clinical significance of age. In particular, the causal role of age in any one patient’s response to therapy is to unique degree for that patient. This is because the causal measure K accounts for the role of known and unknown contextual factors of the patient, most of which are unknown, in defining clinical effect in any specific patient. Thus, we have shown mathematically why ‘age’, or being ‘elderly’, is only one factor to be taken into consideration when therapeutic decisions regarding antithrombotic therapy are made in any given patient. This is contrary to the group-based approach of evidence-based medicine, where age has the same therapeutic significance for all patients. This is because any hypothesis of evidence-based medicine is group-based and cannot be extrapolated to the individual patient. Such extrapolation has to be personalised through the expertise of the physician. The physician takes into account all the factors not considered in any therapeutic group-based trial which apply to his specific patient. These considerations take into account past experience with that patient. An example of the effect of age on the patient and his/her therapeutic context is provided which shows how age affects different patients to different degree using the fuzzy causal measure K. The purpose of this exercise is to show that there is mathematical support for the argument that individualisation of patient therapy by expert decision has measurable clinical significance. What is different here is that measure stick is not probabilistic but fuzzy-mathematic based.
References
Helgason CM, Malik DS, Cheng S-C, et al. Statistical versus fuzzy measures of interaction in patients with stroke. Neuro-epidemiology 2001; 20: 77–84
Zadeh LA. Fuzzy sets. Information Control 1965; 8: 338–53
Kosko B. Fuzziness versus probability: the subsethood theorem. In: Kosko B, editor. Neural networks and fuzzy systems: a dynamical approach to machine intelligence. Englewood Cliffs (NJ): Prentice Hall Inc., 1992: 269–74
Helgason CM, Jobe TH. The fuzzy hypercube and causal efficacy: representation of concomitant mechanisms in stroke. Neural Netw 1998; 11: 549–55
Helgason CM, Watkins FA, Jobe TH. Measurable differences between sequential and parallel diagnostic decision processes for determining stroke subtype: a representation of interacting pathologies. Thromb Haemost 2002; 88: 210–2
Helgason CM, Jobe TH. Perception-based reasoning and fuzzy cardinality provide direct measures of causality sensitive to initial conditions in the individual patient. Int J Computational Cognition 2003; 1: 79–104
Helgason CM, Jobe TH. How the number of dimensions affects fuzzy causal measures of vitamin therapy for hyperhomocysteinemia in stroke patients. Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society; 2003 Jul 24–26: Chicago, 218–21
Sackett DL. Rules of evidence and clinical recommendations on the use of antithrombotic agents. Chest 1986; 89 (2 Suppl.): 2–3S
The European Stroke Initiative Executive Committee and the EUSI Writing Committee. European stroke initiative recommendations for stroke management: update 2003. Cerebrovasc Dis 2003; 16: 311–37
Hirsh J, Fuster V, Ansell J, et al. American Heart Association/American College of Cardiology Foundation guide to warfarin therapy. Circulation 2003; 107: 1692–711
Weitz JI. Direct thrombin inhibitors. Thromb Res 2002; 106: V275–84
Reiffel JA. Will direct thrombin inhibitors replace warfarin for preventing embolic events in atrial fibrillation? Curr Opin Cardiol 2004; 19: 58–63
Ximelagatran is as effective as standard enoxaparin sodium plus warfarin therapy for venous thromboembolism (VTE). Inpharma Weekly 2003 Jul; 1(1397): 9
Johansson LC, Frison L, Logren U, et al. Influence of age on the pharmacokinetics and pharmacodynamics of ximelagatran, an oral direct thrombin inhibitor. Clin Pharmacokinet 2003; 42: 381–92
Barro S, Marin R, editors. Fuzzy logic in medicine. In: Kacprzyk J, series editor. Studies in fuzziness and soft computing series. Vol. 83. Heidelberg: Physica-Verlag, 2002: 83
Teodorescu H-N, Kandel A, Jain L. Fuzzy and neuro-fuzzy systems in medicine. Boca Raton (FL): CRC Press LLC, 1999
Mordeson JN, Malik DS, Cheng S-C. Fuzzy mathematics in medicine. Heidelberg: Physica Verlag, 2000
Szczepaniak PS, Lisboa Paolo JG. Fuzzy systems in medicine. Vol. 41. Heidelberg: Physica Verlag, 2000
Helgason CM, Jobe TH. The measure of causality in perception-based dosing of the individual patient and responsiveness to therapeutic intervention [online]. Available from URL: http://www.Yangsky.com/ijcc31.htm [Accessed 2004 May 3]
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No sources of funding were used to assist in the preparation of this review. The author has no conflicts of interest that are directly relevant to the content of this review.
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Helgason, C.M. The Application of Fuzzy Logic to the Prescription of Antithrombotic Agents in the Elderly. Drugs Aging 21, 731–736 (2004). https://doi.org/10.2165/00002512-200421110-00003
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DOI: https://doi.org/10.2165/00002512-200421110-00003