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Poweleit EA, Vaughn SE, Desta Z, Dexheimer JW, Strawn JR, Ramsey LB. Machine Learning-Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents. Clin Pharmacol Ther 2024; 115:860-870. [PMID: 38297828 PMCID: PMC11046530 DOI: 10.1002/cpt.3184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024]
Abstract
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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Affiliation(s)
- Ethan A. Poweleit
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Biomedical Informatics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
- Division of Research in Patient Services, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Samuel E. Vaughn
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Zeruesenay Desta
- Division of Clinical Pharmacology, Indiana University, School of Medicine, Indianapolis, IN
| | - Judith W. Dexheimer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Jeffrey R. Strawn
- Division of Clinical Pharmacology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH
- Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - Laura B. Ramsey
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Kansas City, Kansas City, MO, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
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Zhang CY, Voort JLV, Yuruk D, Mills JA, Emslie GJ, Kennard BD, Mayes T, Trivedi M, Bobo WV, Strawn JR, Athreya AP, Croarkin PE. A Characterization of the Clinical Global Impression Scale Thresholds in the Treatment of Adolescent Depression Across Multiple Rating Scales. J Child Adolesc Psychopharmacol 2022; 32:278-287. [PMID: 35704877 PMCID: PMC9353998 DOI: 10.1089/cap.2021.0111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Introduction: The Clinical Global Impressions-Improvement (CGI-I) scale is widely used in clinical research to assess symptoms and functioning in the context of treatment. The correlates of the CGI-I with efficacy scales for adolescent major depressive disorder are poorly understood. This study focused on benchmarking CGI-I scores with changes in the Children's Depression Rating Scale-Revised (CDRS-R) and the Quick Inventory of Depressive Symptomatology-Adolescent (17-item) Self-Report (QIDS-A17-SR). Methods: We examined three datasets with the clinician-rated CDRS-R to ascertain equivalent percent changes in total scores and CGI-I ratings. Exploratory analyses examined corresponding percentage changes in the QIDS-A17-SR and the CGI-I ratings. The CGI-I was the reference scale for nonparametric equipercentile linking with the Equate package in R. Results: CGI-I scores of 1 mapped to ≥78%-95% change in CDRS-R scores at 4-6 weeks across three datasets. CGI-I scores of 2 mapped to 56%-94% change in CDRS-R scores at 4-6 weeks across three studies. CGI-I scores of 3 mapped to 30%-68% changes in CDRS-R scores at 4-6 weeks across three studies. CGI-I scores of 4 mapped to a range of 29%-44% at 4-6 weeks across three studies. There was no significant difference (p ≥ 0.6) between treatment groups in both the Treatment of Adolescents with Depression and Treatment of Resistant Depression in Adolescents studies, for each CGI-I score ( = 1, or = 2 or = 3, or ≥4), associated mapping of total depression severity score, or associated percent change from baseline for corresponding follow-up visits. There was no significant sex difference (p > 0.2) in CGI-I linkages to CDRS-R total or percentage changes. Conclusions: These findings establish clear relationships among CGI-I scores and the CDRS-R and the QIDS-A17-SR. These benchmarks have utility for clinical trial study design, inter-rater reliability training, and clinical implementation.
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Affiliation(s)
- Carl Y. Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Deniz Yuruk
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jeffrey A. Mills
- Department of Economics, University of Cincinnati, Cincinnati, Ohio, USA
| | - Graham J. Emslie
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Children's Health, Children's Medical Center, Dallas, Texas, USA
| | - Betsy D. Kennard
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Taryn Mayes
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Madhukar Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - William V. Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida, USA
| | - Jeffrey R. Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, Ohio, USA
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.,Address correspondence to: Paul E. Croarkin, DO, MS, Department of Psychiatry and Psychology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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