Pharmacokinetics and dynamics of heparin in patients with proximal vein thrombosis

Dosing of unfractionated heparin in acute coronary syndromes

Although the use of low-molecular-weight heparins for treatment of acute coronary syndromes (ACS) has increased in recent years, unfractionated heparin (UFH) remains the drug of choice for many patients and institutions. One reason is that this agent is safe for patients with renal dysfunction as well as those who undergo percutaneous coronary intervention or coronary artery bypass graft. The use of UFH is complicated by the increased risk of bleeding due to concurrent administration of numerous antiplatelet drugs in most patients with ACS, the limited data regarding ideal therapeutic range, and the wide variability of patient response. Knowledge regarding the optimal therapeutic range and how to achieve it efficiently may enable clinicians to improve clinical outcomes in patients with ACS. We reviewed and analyzed the available evidence to clarify how to best manage UFH therapy in patients with ACS. Current data support the use of a lower and narrower therapeutic range for patients with ACS than the range that is used for venous thromboembolism. Many factors in addition to weight affect patient response to UFH, including age, sex, diabetes mellitus, smoking status, and obesity.

Developing and testing a system to improve the quality of heparin anticoagulation in patients with acute cardiac syndromes

We have taken a stepwise approach to improving the dosing of continuous intravenous heparin in patients with acute coronary syndromes. Our primary objective was to use computer modeling to develop a nomogram for managing heparin therapy and to put in place a continuous quality monitoring system to evaluate the nomogram's effectiveness. We prospectively collected data on 41 patients with unstable angina or myocardial infarction who were treated with heparin. Their response to heparin was computer modeled and the dose to achieve an activated partial thromboplastin time (aPTT) ratio of 2.0 was established. This dose was regressed against all demographic characteristics to establish predictors of heparin dose (phase I). The regression formula was used prospectively in 110 patients to initiate the infusion rate of heparin and a bolus dose to achieve an aPTT ratio of 2.5. Subsequent dosage adjustments were achieved by computer modeling the patient's aPTT response (phase II). A nomogram was developed that simulated the decisions achieved using computer-assisted methods. This was retrospectively tested and then prospectively tested in 50 patients using nursing staff (phase IV). The nomogram was then made generally available (phase IV) and has been tested in an additional 310 patients. Phase I: Of the original 41 patients, 32% of the aPTT ratios were in the therapeutic range, 36% were supratherapeutic, and 32% were subtherapeutic after the first 24 hours. Phases II and III resulted in 85% of the aPTT ratios between 1.5 and 2.5 at 24 hours. Phase 4 had similar results in 310 patients. The use of computer-assisted or a computer-generated nomogram to adjust heparin therapy results in better control of heparin therapy than using standard methods.

Drugs acting on blood and/or blood forming organs

Pharmacologic therapy for anemia is oriented toward (1) providing components needed for red blood cell production (vitamin B12 and folic acid), including hemoglobin synthesis (iron and other minerals), and (2) stimulating bone marrow formation of red blood cells. Drugs used to stimulate bone marrow activity will be the focus of this article.

A Bayesian formulation of the Kalman filter applied to the estimation of individual pharmacokinetic parameters

A general method of Bayesian forecasting employing the dynamic linear model has been adapted to the problem of estimating individual pharmacokinetic parameters. The Bayesian forecasting method incorporates an efficient Kalman filter algorithm for updating pharmacokinetic parameter estimates when further observations are made. The Kalman filter is more general and flexible than other Bayesian methods currently used and simulation studies have demonstrated its practicality for three different pharmacokinetic models. The method serves as the basis for a computer program for general clinical use.