Integrating pharmacokinetics into point-of-care information systems.

Computer-based patient care information systems (PCIS) have emerged as an integral component of healthcare organisations. Currently, 4 models of PCIS exist: the centralised model, the hub-and-spoke model, the network model, and the distributed model. The centralised model has the advantage of a central patient database; however, a major disadvantage of this model is the inability to easily interface with other software packages. The hub-and-spoke model links satellite or feeder systems into a mainframe computer; thus, each satellite has the ability to work independently. This system is limited by the ability to interface satellite systems with the mainframe computer. The network model works via a local area network (LAN) using client server technology which allows for high speed data access and transfer. The network model does not provide an integrated view of patient information and can access only 1 host system at a time. The distributed model is similar to the network model in design but provides for data and system integration via relational databases. This allows for the creation of a central data repository and support for decision-support tools. Computer-assisted decision support has the potential to significantly improve clinical decision-making. Six types of computer-assisted decision-support have been defined: alerting, interpreting, assisting, critiquing, diagnosing and managing. Software representing each type of decision-support software has been incorporated into clinical practice; however, with the exception of drug interaction programs, widespread incorporation of decision-support software into PCIS is uncommon. Clinical pharmacokinetic programs are a category of pharmacy-related decision-support software, and current clinical pharmacokinetic software systems can be categorised as interpreting, assisting or critiquing decision-support. Despite the potential for significant clinical contributions, the integration of clinical pharmacokinetic software into PCIS is uncommon. Most packages are available only as stand alone programs or as a module of a pharmacy information system. These packages usually maintain their own centralised database and require special file transfer protocols for integration. Although PCIS are becoming more commonplace, the integration of commercial clinical pharmacokinetic packages into PCIS is limited. New technology using standardised and relational databases should allow for easier integration in the future.

Pharmacokinetic optimisation of vancomycin therapy.

Renewed interest in vancomycin over the past decade has led to an abundance of data concerning the pharmacokinetics of vancomycin, and its dosage selection and concentration-response relationships. No definitive data exist that correlate vancomycin serum concentrations with clinical outcomes. However, inconsistencies in sampling times for peak serum concentrations and differences in infusion times make interpreting vancomycin serum concentrations difficult. Furthermore, the evidence implicating vancomycin as a cause of oto- or nephrotoxicity is circumstantial, and these adverse effects may occur only in high-risk populations. Owing to the variability in its dose-serum concentration relationship and multicompartmental pharmacokinetics, several methodologies have been developed for instituting and adjusting vancomycin dosages. Nomograms rely on a fixed volume of distribution and the relationship between vancomycin clearance and creatinine clearance. Since both of these factors may be altered in certain populations, dosage methodologies (both traditional and Bayesian) that use population- or patient-specific pharmacokinetic data perform better than standard nomograms for initiating vancomycin therapy. Controversy still exists as to whether a 1- or a 2-compartment model is more appropriate for making dosage adjustments; however, steady-state rather than non-steady-state vancomycin serum concentrations should be used for dosage adjustments. Certain pathophysiological states such as age, bodyweight and renal function contribute to altered pharmacokinetics and may alter the design of the dosage regimen. Since no definitive relationship exists between vancomycin serum concentrations and either clinical outcome or adverse effects, considerable controversy surrounds the utility of monitoring serum vancomycin concentrations. Therefore, routine vancomycin serum concentration monitoring may be warranted only in specific populations, such as patients receiving concurrent aminoglycoside therapy or those receiving higher than usual dosages of vancomycin, patients undergoing haemodialysis and patients with rapidly changing renal function.

Creatinine-clearance estimates for predicting gentamicin pharmacokinetic values in obese patients.

The Cockcroft-Gault and Salazar-Corcoran equations were compared with respect to prediction of gentamicin pharmacokinetic values in obese and nonobese patients, and the results were used to formulate guidelines for calculating initial gentamicin dosages in obese patients. Creatinine clearance (CLcr) was estimated by applying the Cockcroft-Gault equation using total body weight (TBW), ideal body weight (IBW), and dosage weight (DW) and with Salazar-Corcoran equations using fat-free body mass (FBM) in 100 obese and 100 nonobese patients. Gentamicin pharmacokinetic values (k, CL, and t1/2) were estimated by using CLcr estimated by each method and standardized to a body surface area of 1.73 sq m. Actual pharmacokinetic values were determined by using steady-state gentamicin concentrations and a modified Sawchuk-Zaske equation; these values were compared with the predicted values. In the obese patients, pharmacokinetic values predicted from standardized CLcr by the Cockcroft-Gault equation using estimated DW were not significantly biased, compared with actual values; most predictions produced by the other methods were significantly biased. Predictions produced by the DW method were generally more precise than those resulting from the other methods. In nonobese patients, k values estimated by the Cockcroft-Gault equation using IBW were not significantly biased, while values obtained with all other methods were biased. All methods were biased when predicting CL and t1/2 in nonobese patients. Significant correlations existed between standardized estimates of CLcr (by all methods) and pharmacokinetic values in both groups. Predictions of gentamicin k, CL, and t1/2 were best overall when CLcr was estimated by the Cockcroft-Gault equation using DW, rather than by other methods.(ABSTRACT TRUNCATED AT 250 WORDS)

Symptomatology, pulmonary function and response, and T lymphocyte beta 2-receptors during smoking cessation in patients with chronic obstructive pulmonary disease.

STUDY OBJECTIVES: To characterize the effect of smoking cessation and nicotine replacement on pulmonary symptomatology, baseline pulmonary function and response to terbutaline, and purified T lymphocyte beta 2-receptor regulation; and the relationship between T lymphocyte beta 2-receptor density and pulmonary function. DESIGN: Open-label, longitudinal, 28-week study. SETTING: A university clinical research center. PATIENTS: Eighteen long-term smokers with mild to moderate chronic obstructive pulmonary disease (COPD) were enrolled and seven completed the study. INTERVENTIONS: Subjects stopped smoking with the aid of nicotine substitution and behavioral counseling. Pulmonary response (FEV1) to subcutaneous terbutaline and T lymphocyte beta 2-receptor density (Bmax) and function (cAMP) were measured prior to smoking cessation (week 0), during nicotine replacement (week 8), and after nicotine cessation (week 28). MEASUREMENTS AND MAIN RESULTS: Serum cotinine concentrations, plasma epinephrine concentrations, and day and night cough decreased significantly after smoking cessation, whereas basal cAMP concentrations increased (p < 0.05). No significant change was seen in baseline FEV1, pulmonary response to terbutaline, or Bmax over the 28 weeks; however, intrasubject changes in Bmax between visits correlated significantly (p < 0.05) with intrasubject changes in pulmonary response between visits. CONCLUSIONS: Our data indicate that smoking cessation is associated with a significant decrease in the symptomatology of COPD, and that change in T lymphocyte beta 2-receptor density is a good marker of change in pulmonary response to beta 2-agonists.

Influence of serum separator tubes on total and free phenytoin concentrations and dosages.

The influence of serum separator tubes (SSTs) on total and free phenytoin concentrations and phenytoin dosages was evaluated in patients treated with phenytoin. Thirty blood samples were obtained from 18 patients. Equal volumes of blood were placed into SSTs and plain tubes. Samples were centrifuged, and serum analyzed for total and free phenytoin by fluorescence polarization immunoassay (FPIA) at 2, 24, and 48 h. Total phenytoin concentrations collected in SSTs were significantly lower than those collected in plain tubes at 2 (12.3 vs 12.8 mg/L, p < 0.01), 24 (11.4 vs 12.9 mg/L, p < 0.01), and 48 h (10.9 vs 12.9 mg/L, p < 0.01). These differences resulted in significantly higher calculated maximum rates of metabolism (Vmax) and daily phenytoin dosages (R0) for SSTs at 24 (Vmax: 10.4 vs 10.1 mg/kg/day; R0: 8.2 vs 8.0 mg/kg/day, p < 0.01) and 48 h (Vmax: 10.8 vs 10.3 mg/kg/day; R0: 8.5 vs 8.1 mg/kg/day, p < 0.01). Free phenytoin concentrations from SSTs were significantly lower at 48 h (1.56 vs 1.61 mg/L, p < 0.05). However, there were no significant differences in calculated dosages. Observed statistical differences in total phenytoin concentrations can be clinically important for making dosage adjustments, especially in patients undergoing nonlinear elimination. Thus, SSTs should not be used to collect blood for total serum phenytoin determination.