A Multicenter Double-Blind, Placebo-Controlled Trial of Escitalopram in Children and Adolescents with Generalized Anxiety Disorder

Moldauer, Leslie; Hahn, Rebekah D; Wise, Alexandria; Bertzos, Kristina; Eisenberg, Beth; Greenberg, Edward; Liu, Chengcheng; Gopalkrishnan, Mallika; McVoy, Molly; Knutson, James A

doi:10.1089/cap.2023.0004

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…Escitalopram and sertraline-related side effects were consistent with previous clinical studies. 3,4,7 The finding that activation-cluster side effects are associated with differences in sertraline clearance aligns with previous reports in fluoxetine, fluvoxamine, and escitalopram 2,28,33 ; however, we did not observe a difference in escitalopram clearance, which has been previously reported. 2 After accounting for age, we were unable to detect a relationship at a 5% significance threshold.…”

Section: Discussionsupporting

confidence: 92%

“…2 After accounting for age, we were unable to detect a relationship at a 5% significance threshold. Although sertraline is indicated for obsessive-compulsive disorder in patients as young as 6 years old, 8 escitalopram was until recently only approved in patients older than 11 years 3,7 ; this is reflected in this study as patients treated with sertraline were as young as 5 years old and the youngest escitalopram-treated patient was 10 years old. Given the differences in indicated populations between SSRIs, future studies could investigate the relationship of age with variation in tolerability and pharmacokinetics, especially as clearance and age may be correlated.…”

Section: Discussionmentioning

confidence: 90%

“…Psychiatric disorders are prevalent and associated with significant morbidity and mortality in children and adolescents. 1 Importantly, selective serotonin reuptake inhibitors (SSRIs), including escitalopram and sertraline, are effective in treating anxiety disorders, [2][3][4] depression, [5][6][7] and other mental health disorders 8 in youth; however, many experience side effects. [9][10][11] Current antidepressant dosing strategies in youth frequently follow a "one size fits all" approach, where an initial standard dose is selected and then slowly titrated until the patient responds or experiences side effects.…”

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confidence: 99%

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Machine Learning‐Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents

Poweleit,

Vaughn,

Desta

et al. 2024

Clin Pharma and Therapeutics

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Selective serotonin reuptake inhibitors (SSRI) are the first‐line pharmacologic treatment for anxiety and depressive disorders in children and adolescents. Many patients experience side effects that are difficult to predict, are associated with significant morbidity, and can lead to treatment discontinuation. Variation in SSRI pharmacokinetics could explain differences in treatment outcomes, but this is often overlooked as a contributing factor to SSRI tolerability. This study evaluated data from 288 escitalopram‐treated and 255 sertraline‐treated patients ≤ 18 years old to develop machine learning models to predict side effects using electronic health record data and Bayesian estimated pharmacokinetic parameters. Trained on a combined cohort of escitalopram‐ and sertraline‐treated patients, a penalized logistic regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval (CI): 0.66–0.88), with 0.69 sensitivity (95% CI: 0.54–0.86), and 0.82 specificity (95% CI: 0.72–0.87). Medication exposure, clearance, and time since the last dose increase were among the top features. Individual escitalopram and sertraline models yielded an AUROC of 0.73 (95% CI: 0.65–0.81) and 0.64 (95% CI: 0.55–0.73), respectively. Post hoc analysis showed sertraline‐treated patients with activation side effects had slower clearance (P = 0.01), which attenuated after accounting for age (P = 0.055). These findings raise the possibility that a machine learning approach leveraging pharmacokinetic data can predict escitalopram‐ and sertraline‐related side effects. Clinicians may consider differences in medication pharmacokinetics, especially during dose titration and as opposed to relying on dose, when managing side effects. With further validation, application of this model to predict side effects may enhance SSRI precision dosing strategies in youth.

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