Area under the curve of methotrexate and creatinine clearance are outcome-determining factors in primary CNS lymphomas.
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ABSTRACT:
SUBMITTER: Ferreri AJ
PROVIDER: S-EPMC2409565 | biostudies-other | 2004 Jan
REPOSITORIES: biostudies-other
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Project description:AimThe aim was to develop and validate limited sampling strategy (LSS) models to predict the area under the plasma concentration-time curve (AUC) for metformin.MethodsMetformin plasma concentrations (n = 627) at 0-24 h after a single 500 mg dose were used for LSS development, based on all subsets linear regression analysis. The LSS-derived AUC(0,24 h) was compared with the parameter 'best estimate' obtained by non-compartmental analysis using all plasma concentration data points. Correlation between the LSS-derived and the best estimated AUC(0,24 h) (r(2) ), bias and precision of the LSS estimates were quantified. The LSS models were validated in independent cohorts.ResultsA two-point (3 h and 10 h) regression equation with no intercept estimated accurately the individual AUC(0,24 h) in the development cohort: r(2) = 0.927, bias (mean, 95% CI) -0.5, -2.7-1.8% and precision 6.3, 4.9-7.7%. The accuracy of the two point LSS model was verified in study cohorts of individuals receiving single 500 or 1000 mg (r(2) = -0.933-0.934) or seven 1000 mg daily doses (r(2) = 0.918), as well as using data from 16 published studies covering a wide range of metformin doses, demographics, clinical and experimental conditions (r(2) = 0.976). The LSS model reproduced previously reported results for effects of polymorphisms in OCT2 and MATE1 genes on AUC(0,24 h) and renal clearance of metformin.ConclusionsThe two point LSS algorithm may be used to assess the systemic exposure to metformin under diverse conditions, with reduced costs of sampling and analysis, and saving time for both subjects and investigators.
| S-EPMC5137825 | biostudies-literature
Project description:BackgroundIncremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of disagreement is not uncommon, and can create confusion when assessing whether the added information improves the model prediction accuracy.MethodsIn this article, we examine the analytical connections and differences between the AUC IncV (ΔAUC) and AP IncV (ΔAP). We also compare the true values of these two IncV metrics in a numerical study. Additionally, as both are semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (ΔsBrS) in the numerical study.ResultsWe demonstrate that ΔAUC and ΔAP are both weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, ΔAP assigns heavier weights to the changes in higher-risk regions, whereas ΔAUC weights the changes equally. Due to this difference, the two IncV metrics can disagree, and the numerical study shows that their disagreement becomes more pronounced as the event rate decreases. In the numerical study, we also find that ΔAP has a wide range, from negative to positive, but the range of ΔAUC is much smaller. In addition, ΔAP and ΔsBrS are highly consistent, but ΔAUC is negatively correlated with ΔsBrS and ΔAP when the event rate is low.ConclusionsΔAUC treats the wins and losses of a new risk model equally across different risk regions. When neither the existing or new model is the true model, this equality could attenuate a superior performance of the new model for a sub-region. In contrast, ΔAP accentuates the change in the prediction accuracy for higher-risk regions.
| S-EPMC8278775 | biostudies-literature
Project description:BackgroundMeasuring glomerular filtration rate (GFR) using iohexol plasma clearance has been proposed as the preferred way for GFR determination. The extended multiple-sample protocol is based on fitting the full concentration-time decay-curve, and from the obtained fit-parameters, the area under the curve (AUC) and GFR (the injected dose divided by the AUC) were calculated. The goal of the current study is to evaluate the impact of different fitting procedures on the precision of GFR-results obtained from the full concentration-time curve, and compare these results with those obtained with simplified multiple-samples and single-sample protocols.MethodsThe concentration-time curves of 8 samples at times 30, 60, 90, 120, 150, 180, 240 and 300 min after bolus injection of iohexol of 570 adults, aged 70+, from the Berlin Initiative Study (BIS), were analysed. The fit-parameters for the two-compartment model (double-exponential decay curve), and from these, the AUC and GFR were obtained with 8 different fitting procedures.ResultsThe two-compartmental non-linear least squares fitting procedure showed the best accuracy (541 out of 570 reported GFR-results were within 5% of the majority of the 8 fitting methods). The two-compartmental slope-intercept fitting procedure was not always applicable and the non-compartmental fitting procedures did not always allow to calculate the GFR. All correction formulas for the simplified late multiple-samples methods showed acceptable accuracy and precision with a preference for Ng's correction formula (Lin's CCC = 0.992, bias = 0.5 ± 2.5). Jacobsson's iterative method was the best one-sample method, with Lin's CCC = 0.983 and bias = - 0.6 ± 3.4.ConclusionThe fitting procedure has an important impact on the precision of the calculated AUC and GFR. The simplified late-sample protocols and one-sample methods did not suffer from fitting problems and showed acceptable equivalence when compared to the full compartment GFR-results.Trial registrationThe "Berlin Initiative Study" is officially registered with the German Register for Clinical Studies ("Deutschen Register Klinischer Studien"(DRKS)) under registration number DRKS00017058 , since April 12, 2019, and it is also visible on the WHO clinical trials registry platform (within the next 4 weeks after the registration date).
| S-EPMC8101203 | biostudies-literature
Project description:The Area Under the Receiving Operating Characteristic Curve (AUC) is frequently used for assessing the overall accuracy of a diagnostic marker. However, estimation of AUC relies on knowledge of the true outcomes of subjects: diseased or non-diseased. Because disease verification based on a gold standard is often expensive and/or invasive, only a limited number of patients are sent to verification at doctors' discretion. Estimation of AUC is generally biased if only small verified samples are used and it is thus necessary to make corrections for such lack of information. However, correction based on the ignorable missingness assumption (or missing at random) is also biased if the missing mechanism indeed depends on the unknown disease outcome, which is called nonignorable missing. In this paper, we propose a propensity-score-adjustment method for estimating AUC based on the instrumental variable assumption when the missingness of disease status is nonignorable. The new method makes parametric assumptions on the verification probability, and the probability of being diseased for verified samples rather than for the whole sample. The proposed parametric assumption on the observed sample is easier to be verified than the parametric assumption on the full sample. We establish the asymptotic properties of the proposed estimators. A simulation study is performed to compare the proposed method with existing methods. The proposed method is also applied to an Alzheimer's disease data collected by National Alzheimer's Coordinating Center.
| S-EPMC6668995 | biostudies-literature
Project description:BackgroundRisk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients.MethodsWe propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest.ResultsWe compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method.ConclusionsThe proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.
| S-EPMC4949771 | biostudies-literature
Project description:Rationale: Interpretation of spirometry is influenced by inherent limitations and by the normal or predicted reference values used. For example, traditional spirometric parameters such as "distal" airflows do not provide sufficient differentiating capacity, especially for mixed patterns or small airway disease.Objectives: We assessed the utility of an alternative spirometric parameter (area under the expiratory flow-volume curve [AEX]) in differentiating between normal, obstruction, restriction, and mixed patterns, as well as in severity stratification of traditional functional impairments.Methods: We analyzed 15,308 spirometry tests in subjects who had same-day lung volume assessments in a pulmonary function laboratory. Using Global Lung Initiative predicted values and standard criteria for pulmonary function impairment, we assessed the diagnostic performance of AEX in best-split partition and artificial neural network models.Results: The average square root AEX values were 3.32, 1.81, 2.30, and 1.64 L?s-0.5 in normal, obstruction, restriction, and mixed patterns, respectively. As such, in combination with traditional spirometric measurements, the square root of AEX differentiated well between normal, obstruction, restriction, and mixed defects. Using forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1/FVC z-scores plus the square root of AEX in a machine learning algorithm, diagnostic categorization of ventilatory impairments was accomplished with very low rates of misclassification (<9%). Especially for mixed ventilatory patterns, the neural network model performed best in improving the rates of diagnostic misclassification.Conclusions: Using a novel approach to lung function assessment in combination with traditional spirometric measurements, AEX differentiates well between normal, obstruction, restriction and mixed impairments, potentially obviating the need for more complex lung volume-based determinations.
| S-EPMC7193817 | biostudies-literature
Project description:PurposeThe area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements.MethodsAn algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses.ResultsThis algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance.ConclusionsThe variable nature of the baseline is an important aspect to consider when calculating the AUC.
| S-EPMC3152796 | biostudies-literature
Project description:ObjectivesTo assess differences in vancomycin AUC estimates from two common, clinically applied first-order pharmacokinetic equation methods compared with Bayesian estimates.MethodsA cohort of patients who received vancomycin and therapeutic drug monitoring was studied. First-order population pharmacokinetic equations were used to guide initial empirical dosing. After receipt of the first dose, patients had peak and trough serum levels drawn and steady-state AUC was estimated using first-order pharmacokinetic equations as standard care. We subsequently created a Bayesian model and used individual Empirical Bayes Estimates to precisely calculate vancomycin AUC24-48, AUC48-72 and AUC72-96 in this cohort. AUC at steady state (AUCSS) differences from the first-order methods were compared numerically and categorically (i.e. below, within or above 400-600 mg·h/L) to Bayesian AUCs, which served as the gold standard.ResultsA total of 65 adult inpatients with 409 plasma samples were included in this analysis. A two-compartment intravenous infusion model with first-order elimination fit the data well. The mean of Bayesian AUC24-48 was not significantly different from AUC estimates from the two first-order pharmacokinetic equation methods (P = 0.68); however, Bayesian AUC48-72 and Bayesian AUC72-96 were both significantly different when compared with both first-order pharmacokinetic equation methods (P < 0.01 for each). At the patient level, categorical classifications of AUC estimates from the two first-order pharmacokinetic equation methods differed from categorizations derived from the Bayesian calculations. Categorical agreement was ∼50% between first-order and Bayesian calculations, with declining categorical agreement observed with longer treatment courses. Differences in categorical agreement between calculation methods could potentially result in different dose recommendations for the patient.ConclusionsBayesian-calculated AUCs between 48-72 and 72-96 h intervals were significantly different from first-order pharmacokinetic method-estimated AUCs at steady state. The various calculation methods resulted in different categorical classification, which could potentially lead to erroneous dosing adjustments in approximately half of the patients.
| S-EPMC9890897 | biostudies-literature
Project description:The combination of high doses of methotrexate (MTX) and cytarabine (araC) is the standard chemotherapy for patients with primary CNS lymphoma (PCNSL). The addition of an alkylating agent could improve MTX-araC efficacy because it is active against quiescent G0 cells and increases antimetabolites cytotoxicity. A pilot experience with high doses of MTX, araC, and thiotepa (MAT regimen) was performed to investigate feasibility and efficacy of adding an alkylating agent. With respect to MTX-araC combination, araC dose was halved to minimize toxicity. Herein, we report tolerability, activity, and efficacy of MAT regimen and compare these results to those previously reported with MTX/ara-C combination.Twenty HIV-negative patients with PCNSL treated with MAT regimen and whole-brain irradiation and selected according to eligibility criteria of the International Extranodal Lymphoma Study Group (IELSG) #20 trial were analyzed.Patient characteristics of MAT and MTX-araC series were similar. G4 hematologic toxicity was common after MAT chemotherapy, with dose reductions in 60% of patients, infections in 20%, G4 non-hematologic toxicity in 15%, and one (5%) toxic death. Response after chemotherapy was complete in four patients (clinical response rate, 20%; 95% confidence interval, 3%-37%) and partial in three (overall response rate, 35%; 95% confidence interval, 15%-55%). Fifteen patients experienced failure and 16 died (median follow-up, 26 months), with a 2-year overall survival of 24% ± 9%.MAT and MTX-araC combinations showed similar tolerability, whereas araC dose reduction was associated with a remarkably lower efficacy, hiding any potential benefit of thiotepa. Four doses of araC 2 g/m(2) per course are recommended in patients with PCNSL.
| S-EPMC3228098 | biostudies-literature