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This systematic review and meta-analysis aimed to scrutinize the safety and effectiveness of Escitalopram (Selective Serotonin Reuptake Inhibitor [SSRI]) in pediatric generalized anxiety disorder (GAD) and discern the potential indicators of therapeutic response.
Escitalopram effectively lowers PARS scores in pediatric generalized anxiety disorder patients. Neuroimaging serves as a key predictor of treatment response.
This systematic review and meta-analysis aimed to scrutinize the safety and effectiveness of Escitalopram (Selective Serotonin Reuptake Inhibitor [SSRI]) in pediatric generalized anxiety disorder (GAD) and discern the potential indicators of therapeutic response.
Across six databases, a search for randomized controlled trials (RCTs) examining Escitalopram's efficacy in pediatric GAD was executed. Two reviewers independently selected trials, collected data, and examined trial quality. Any kind of differences were solved by a third reviewer. The outcomes of the study were presented as mean differences (MDs) along with 95% confidence intervals (CIs). For computing the quality of evidence, the Cochrane risk of bias tool was utilized.
Analysis of 5 RCTs involving 401 patients revealed that Escitalopram triggered a higher drop in the Pediatric Anxiety Rating Scale (PARS) score as opposed to placebo (MD -6.1, 95% CI [-8.75 to -3.44] (P = 0.09, I2 = 65%)). Various methods, such as alterations in reaction time, executive functions, and Amygdala Functional Connectivity, including the CYP2C19 metabolizer phenotype, were utilized to predict Escitalopram therapy responses. Notably, neuroimaging emerged as the most useful predictor of therapeutic response.
Escitalopram demonstrated noteworthy reductions in PARS scores among pediatric patients battling GAD. Neuroimaging, serving as a biomarker, proved beneficial in forecasting treatment response and offering insights into the neurological aspects of anxiety disorders.
F1000Research
The effectiveness of using escitalopram in pediatric generalized anxiety disorder and the methods to predict the treatment response: A systematic review and meta-analysis
Mohammad J. J. Taha et al.
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