Efficacité et tolérance de la lacidipine dans le traitement de l’hypertension artérielle en onco-hématologie pédiatrique

Bernard, E.; Mialou, Valérie; Dony, Arthur; Garnier, Nathalie; Rénard, C.; Bleyzac, Nathalie

doi:10.1016/j.arcped.2014.06.028

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Cholestasis was defined by a bilirubin plasma concentration above 50 µmol/L [ 25 ]. The interacting drugs considered were those that are most used in HSCT: all proton pump inhibitors (PPIs), macrolide antibiotics (including pristinamycine and quinupristine, but excluding spiramycine), all azole antifungals, and calcium channel blockers (nicardipine and amlodipine, but not lacidipine) [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation

2018

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BackgroundPediatric hematopoietic stem cell transplantation (HSCT) allows the treatment of numerous diseases, both malignant and non-malignant. Cyclosporine, a narrow therapeutic index drug, is the major immunosuppressant used to prevent graft-versus-host disease (GVHD), but may also cause severe adverse effects in case of overdosing.ObjectiveThe objective of this study is to predict the initial cyclosporine residual blood concentration value after pediatric HSCT, and consequently the dose necessary to reach the therapeutic range, using a mathematical individual predictive model.MethodsClinical and biological data collected from the graft infusion for 2 months after transplantation in 155 pediatric patients undergoing HSCT between 2008 and 2016 were used to generate synthetic data for 1000 subjects which were used to build a Bayesian network model. We compared the characteristics and sensitivity to clinical or biological missing data of this model with four other methods.ResultsThe tree-augmented Naïve Bayesian network showed the best characteristics, with no missing data (area under the curve of the receiving operator characteristics curve [AUC-ROC] of 0.89 ± 0.02), 18.9 ± 2.6% of patients misclassified, and positive and negative predictive values of 85.9 ± 3.4% and 74.2 ± 5.1%, respectively, and this trend is found in the synthetic dataset from no to 10% missing data. The most relevant variables that could influence whether the initial residual cyclosporine concentration is in the therapeutic range are the last dose before measurement and the mean dose before measurement.ConclusionsWe developed and cross-validated an online Bayesian network to predict the first cyclosporine concentration after pediatric HSCT. This model allows simulation of different dosing regimens, and enables the best dosing regimen to reach the therapeutic range immediately after transplantation to be found, minimizing the risk of adverse effects and GVHD occurrence.

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Section: Methodsmentioning

confidence: 99%

Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation

2018

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A Clinical Decision Support Tool to Find the Best Initial Intravenous Cyclosporine Regimen in Pediatric Hematopoietic Stem Cell Transplantation

Leclerc

Ducher

Ceraulo

et al. 2021

The Journal of Clinical Pharma

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To optimize cyclosporine A (CsA) dosing regimen in pediatric patients undergoing hematopoietic stem cell transplantation (HSCT), we aimed to provide clinicians with a validated decision support tool for determining the most suitable first dose of intravenous CsA. We used a 10‐year monocentric data set of pediatric patients undergoing HSCT. Discretization of all variables was performed according to literature or thanks to algorithms using Shannon entropy (from information theory) or equal width intervals. The first 8 years were used to build the Bayesian network model. This model underwent a 10‐fold cross‐validation, and then a prospective validation with data of the last 2 years. There were 3.3% and 4.1% of missing values in the training and the validation data set, respectively. After prospective validation, the Tree‐Augmented Naïve Bayesian network shows interesting prediction performances with an average area under the receiver operating characteristic curve of 0.804, 32.8% of misclassified patients, a true‐positive rate of 0.672, and a false‐positive rate of 0.285. This validated model allows good predictions to propose an optimized and personalized initial CsA dose for pediatric patients undergoing HSCT. The clinical impact of its use should be further evaluated.

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A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients

Leclerc

Bleyzac

Ceraulo

et al. 2020

Eur J Clin Pharmacol

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Efficacité et tolérance de la lacidipine dans le traitement de l’hypertension artérielle en onco-hématologie pédiatrique

Cited by 5 publications

References 26 publications

Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation

Bayesian Networks: A New Approach to Predict Therapeutic Range Achievement of Initial Cyclosporine Blood Concentration After Pediatric Hematopoietic Stem Cell Transplantation

A Clinical Decision Support Tool to Find the Best Initial Intravenous Cyclosporine Regimen in Pediatric Hematopoietic Stem Cell Transplantation

A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients

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