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

Pharmacological Treatment of Arterial Hypertension in Children and Adolescents in Lithuania

The global prevalence of arterial hypertension (AH) in the pediatric population is increasing, but therapeutic approaches and the choice of the most suitable antihypertensive medications remains challenging. The study aimed to estimate the prevalence, treatment rates, and pharmacological treatment patterns of children and adolescents with AH in Lithuania during 2019 using real-world data. The study population consisted of citizens of Lithuania aged 0 to 17 years, who were diagnosed with AH according to the International Classification of Diseases (ICD). The analysis of reimbursed antihypertensive medication prescriptions was performed according to AH etiology and age. The overall prevalence of AH by diagnostic ICD codes in 2019 was 0.29%:0.24% for primary and 0.05% for secondary. Treatment rates were 39.8% for primary AH and 66.3% for secondary AH. Angiotensin-converting enzyme inhibitors (ACEi) were the most popular medications irrespective of the etiology of AH or age. Beta-blockers were in the second place and used more often in older children. Calcium channel blockers were the third and angiotensin receptor blockers were the fourth most frequent choices. Enalapril was the most popular agent in the ACEi group and metoprolol in the beta-blocker group. Nearly forty percent of Lithuanian children with primary AH receive pharmacological therapy compared to two-thirds with secondary AH. Although ACEi are the predominant class of antihypertensive medications, discordances with available guidelines are evident, particularly in the overuse of beta-blockers and underuse of diuretics.

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

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.

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

PURPOSE

Managing the pharmacokinetic variability of immunosuppressive drugs after pediatric hematopoietic stem cell transplantation (HSCT) is a clinical challenge. Thus, the aim of our study was to design and validate a decision support tool predicting the best first cyclosporine oral dose to give when switching from intravenous route.

METHODS

We used 10-years pediatric HSCT patients' dataset from 2008 to 2018. A tree-augmented naïve Bayesian network model (method belonging to artificial intelligence) was built with data from the first eight-years, and validated with data from the last two.

RESULTS

The Bayesian network model obtained showed good prediction performances, both after a 10-fold cross-validation and external validation, with respectively an AUC-ROC of 0.89 and 0.86, a percentage of misclassified patients of 28.7% and 35.2%, a true positive rate of 0.71 and 0.65, and a false positive rate of 0.12 and 0.14 respectively.

CONCLUSION

The final model allows the prediction of the most likely cyclosporine oral dose to reach the therapeutic target specified by the clinician. The clinical impact of using this model needs to be prospectively warranted. Respecting the decision support tool terms of use is necessary as well as remaining critical about the prediction by confronting it with the clinical context.

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

BACKGROUND

Pediatric 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.

OBJECTIVE

The 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.

METHODS

Clinical 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.

RESULTS

The 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.

CONCLUSIONS

We 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.