Objectives: This study aimed to examine the association between abnormal readings of metabolic parameters detected during second-generation antipsychotic (SGA) treatment and the likelihood of receiving subsequent adverse drug event interventions. Methods: This was a nested case-control study conducted on patients 1-17 years of age with at least two prescriptions of SGAs between January 2010 and January 2019 using TriNetX EMR data. Following an incident density sampling procedure, patients who received the SGA metabolic adverse event intervention (mAEI) (case) were matched with three nonrecipients (controls). The abnormal readings of metabolic parameters within 30 days before the initiation of mAIEs were further identified. These metabolic parameters include body mass index (BMI) and laboratory parameters such as cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, blood glucose, HbA1c, thyroid hormones, liver enzymes, and prolactin. The association of abnormal metabolic parameters with subsequent mAEIs was assessed using a conditional logistic regression model, after adjusting for demographic and other clinical risk factors. Results: One thousand eight hundred eighty-four children and adolescents met the inclusion criteria and were prescribed SGA mAEIs. The most common types of mAEIs prescribed were weight management pharmacotherapy (40.6%), switching from a high or medium metabolic risk profile SGA to a low-risk one (30.9%), nonpharmacological treatment (25.4%), and switching from SGA polytherapy to monotherapy (11.7%). The conditional logistic regression analysis on matched mAEI recipients and nonrecipients showed that patients with an abnormal BMI had 43% higher odds of receiving mAEI (odds ratio [95% confidence interval]: 1.43 [1.13-1.79]). However, the presence of an abnormal laboratory reading was not associated with the initiation of mAEIs. Conclusions: The prescribing of mAEIs were associated with the presence of obesity, but not with abnormal readings of other metabolic parameters, suggesting that additional data are needed to clarify the long-term implication of SGA metabolic adverse events other than weight gain and to inform the appropriate timing for interventions.
Keywords: SGA-associated adverse drug events; conditional logistic regression; events; metabolic adverse event intervention; metabolic monitoring; nested case–control study.