Fluorescence polarization immunoassay cross-reactivity for digoxin.
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Aged↗
Biomedical subjects
J C Garrelts
Publications and source records attributed to J C Garrelts.
Accuracy of Bayesian, Sawchuk-Zaske, and nomogram dosing methods for vancomycin.
The predictive performance of a Bayesian method for vancomycin dosing was compared with that of two nomogram-based methods and the Sawchuk-Zaske method. Prospectively collected serum concentration data were evaluated retrospectively in patients who had at least two steady-state peak and trough serum vancomycin concentrations obtained during two different dosage regimens. The methods evaluated were a Bayesian program that uses a one-compartment weighted-sum-of-squares expression; the nomogram methods of Moellering and Matzke, which derive vancomycin clearance from urinary creatinine clearance; and the Sawchuk-Zaske method, which uses equations for one-compartment, first-order elimination. The ability of each method to predict the second set of serum concentrations when given the first set of concentrations was evaluated using mean prediction error (ME), mean absolute error (MAE), and root mean squared error (RMSE). To compare the predictions made by each of the four methods, the differences in mean error and the differences in the natural logarithm of mean absolute error and their 95% confidence intervals were compared. No significant difference in ME (bias) or MAE (precision) was found between the Moellering and Matzke methods. The Sawchuk-Zaske method was significantly more precise than the Matzke method in predicting peak serum concentrations and more precise than the Moellering or Matzke method in predicting trough concentrations. The Bayesian program was significantly more precise and less biased than the Moellering and Matzke methods and less biased than the Sawchuk-Zaske method in predicting both peak and trough concentrations.(ABSTRACT TRUNCATED AT 250 WORDS)
Adolescent↗
Vancomycin and the "red man's syndrome".
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Drug Eruptions↗
Alternative drug therapies for mania: a literature review.
This article reviews the agents used to treat manic patients. It has been suggested that up to 20 percent of all manic patients are refractory to lithium. We reviewed studies suggesting that drugs affecting the serotoninergic, adrenergic, dopaminergic, and opioid neurotransmission systems, as well as anticonvulsants and others, are possible alternative treatments. Despite the large number of agents used, few large controlled studies carefully examine these interventions. Clinical assessments using specific manic rating scales are lacking in most studies. Among the successful alternative treatments reported, carbamazepine has received the most intensive research. Other suggested therapeutic agents, such as levothyroxine and clorgyline, require additional research to verify their effectiveness. From these pharmacological approaches and an understanding of the pathophysiology of mania, future therapeutic interventions can be formulated.
Anticonvulsants↗
Chronic acetazolamide intoxication.
Severe acidosis associated with acetazolamide therapy is rare. We report the first case in which plasma and whole blood acetazolamide concentrations were measured. A 61 year-old patient receiving oral acetazolamide for treatment of glaucoma presented with a 7 day history of declining mental status. The patient was lethargic and oriented only to name. The respiratory rate was 36 per minute in a Kussmaul pattern with arterial blood gases revealing a pH of 7.23, pO2 68 mmHg, paCO2 14 mmHg and bicarbonate 6 mEq/L. Serum creatinine was 3.1 mg%, Cl 126 mEq/L, and anion gap 15. Urine pH was 6.0. Infection and other causes of acidosis and bicarbonate loss were excluded, and he was discharged with normal mental status and improving acid-base balance 18 days after admission. Acetazolamide concentrations four days after the last dose were 26.38 mcg/ml and 38.84 mcg/ml in serum and whole blood, respectively. The serum half-life was 34 hours, compared to a range of 1.5 to 6 hours in subjects with normal renal function. Monitoring acetazolamide concentrations may be useful in adjusting dosage and preventing toxicity in patients with decreased renal function.
Acetazolamide↗
A pharmacoeconomic model to evaluate antibiotic costs.
STUDY OBJECTIVES: To characterize patient sociodemographics and health, describe vancomycin treatment parameters and clinician-rated outcomes, and determine costs associated with treatment including preparation and administration, adverse events, and toxicity. DESIGN: A prospective study to develop a model for costs associated with antibiotic treatment (vancomycin). SETTING: A community hospital. PATIENTS: One hundred adults with active infections. INTERVENTIONS: Mean duration of therapy was 10 days, and most patients received 2000 mg/day. Serum concentrations were monitored in two of three patients. Detailed cost analyses were completed on a subset of 26 patients selected at random from the overall sample. MEASUREMENTS AND MAIN RESULTS: Sepsis and skin and skin structure infections were the most common indications for vancomycin therapy. Treatment was effective in 81 patients, failed in 9, and was not evaluable in 10. Thirty-eight percent of patients experienced adverse events attributable to the drug. Phlebitis was common, and red man syndrome, nephrotoxicity, and ototoxicity were infrequent. CONCLUSIONS: Total cost of vancomycin treatment for 100 patients was $30,251: $23,855 for preparation and administration, $1710 for monitoring serum concentrations, and $4686 for treating adverse reactions. Drug costs accounted for only 55% of the total cost. Vancomycin is safe and effective, but phlebitis is underreported and significantly affects cost.
Adult↗
Exploration of once-daily dosing of aminoglycosides through Bayesian simulation.
STUDY OBJECTIVES: To simulate peak and trough concentrations, using Bayesian forecasting, achieved with a variety of once-daily dosing (ODD) regimens; to evaluate dosing regimens required to produce target peak and trough concentrations; to compare the peak-to-MIC (minimum inhibitory concentration) ratios and time above MIC for various dosing regimens and MICs; to stratify the information based on renal function estimates; and to use the results from these simulations to make recommendations regarding optimum ODD of aminoglycosides. DESIGN: Simulation of ODD using a Bayesian technique and existing patient data. SETTING: A tertiary referral, community teaching hospital. PATIENTS: One hundred consecutive adults from the author's data base, with a wide variety of infections and underlying illnesses, who met strict inclusion criteria. INTERVENTIONS: Two methods of dosing, weight-based (4-7 mg/kg) and target concentration-based (peak 10, 15, or 20 mu g/ml, trough < or = 0.3 mu g/ml), were evaluated. Each patient had a known dosing-sampling history, stable renal function, and at least two measured serum concentrations, and were being treated with either gentamicin or tobramycin. MEASUREMENTS AND MAIN RESULTS: A wide range of peak and trough serum concentrations are achieved when dosages are chosen based on patient weight. Even with large dosages, some patients had very low peak-to-MIC ratios and time above the MIC, and vice versa. Variations in MIC had a much greater effect on dosing target values than did variations in dosage. A large degree of variability was also noted in doses and dosing intervals when using a target serum concentration approach. For both methods, an inverse relationship existed between calculated creatinine clearance and time above MIC, although there was little change over the range of 60-119 ml/minute. CONCLUSIONS: Bayesian simulation showed that weight-based ODD of aminoglycosides did not produce clinically acceptable serum concentrations or target values in many patients. Young and elderly patients, and any patient with a creatinine clearance below 60 or above 119 ml/minute, are especially likely not to achieve an optimum serum concentration profile. Aminoglycoside ODD should be individualized by evaluating the peak-to-MIC ratio, time above MIC, and patient response.
Adult↗