Racial Differences in Escitalopram/Citalopram-Related Weight Gain in Children and Adolescents: A Natural Language Processing-Based Electronic Medical Record Study

Ramsey, Laura B.; Aldrich, Stacey L.; Poweleit, Ethan A.; Prows, Cynthia A.; Martin, Lisa J.

doi:10.1089/cap.2018.0110

Cited by 5 publications

(4 citation statements)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two important treatment-related side effects of escitalopram are weight gain and activation. First, CYP2C19 activity may influence weight gain in this vulnerable population, a finding which may help explain variability in weight gain that has been prospectively observed in escitalopram-treated youth (Czaja et al, 2013; Calarge et al, 2017; Ramsey et al, 2018a). Second, activation, a constellation of side effects that often cause treatment discontinuation and decrease the likelihood of medication response (Bussing et al, 2013; Luft et al, 2018), was associated with CYP2C19 phenotype; more activation symptoms were reported in patients that metabolize es/citalopram more slowly, even after controlling for concomitant psychotropics.…”

Section: Discussionmentioning

confidence: 99%

Influence of CYP2C19 Metabolizer Status on Escitalopram/Citalopram Tolerability and Response in Youth With Anxiety and Depressive Disorders

et al. 2019

Self Cite

View full text Add to dashboard Cite

In pediatric patients, the selective serotonin reuptake inhibitors (SSRIs) escitalopram and citalopram (es/citalopram) are commonly prescribed for anxiety and depressive disorders. However, pharmacogenetic studies examining CYP2C19 metabolizer status and es/citalopram treatment outcomes have largely focused on adults. We report a retrospective study of electronic medical record data from 263 youth < 19 years of age with anxiety and/or depressive disorders prescribed escitalopram or citalopram who underwent routine clinical CYP2C19 genotyping. Slower CYP2C19 metabolizers experienced more untoward effects than faster metabolizers (p = 0.015), including activation symptoms (p = 0.029) and had more rapid weight gain (p = 0.018). A larger proportion of slower metabolizers discontinued treatment with es/citalopram than normal metabolizers (p = 0.007). Meanwhile, faster metabolizers responded more quickly to es/citalopram (p = 0.005) and trended toward less time spent in subsequent hospitalizations (p = 0.06). These results highlight a disparity in treatment outcomes with es/citalopram treatment in youth with anxiety and/or depressive disorders when standardized dosing strategies were used without consideration of CYP2C19 metabolizer status. Larger, prospective trials are warranted to assess whether tailored dosing of es/citalopram based on CYP2C19 metabolizer status improves treatment outcomes in this patient population.

show abstract

Section: Discussionmentioning

confidence: 99%

Influence of CYP2C19 Metabolizer Status on Escitalopram/Citalopram Tolerability and Response in Youth With Anxiety and Depressive Disorders

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The goal of NLP technology is to enable people to use natural language to interact with computer systems to achieve information and to ensure the efficient processing of information in the process of humancomputer interaction. The application of NLP technology is mainly focused on the automatic differentiation of phrases, corpus construction, and grammar research, which makes it widely used in data mining, information retrieval, and machine translation [5,6]. With the steady advancement of Chinese legal society, the open system of the judicial field has gradually improved.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing Technology Used in Artificial Intelligence Scene of Law for Human Behavior

Ning

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In order to study the application of natural language processing (NLP) technology in artificial intelligence (AI) scene of law, NLP technology is used to construct a legal AI retrieval system and further simulate the system. Then, by inputting the subject matter of the case into the system, the system’s accuracy, recall rate, and error rate and other related indicators are evaluated, to analyze the performance of the legal retrieval system. The results show that in the case analysis of a single theme, the accuracy rate of the case with the theme of “impeding police enforcement” is low, and the accuracy rate of the other theme cases is over 70%, and the highest accuracy rate even reaches 95%. In the case retrieval analysis of multitheme, the accuracy rate of case retrieval is improved, higher than 75%, and the zero-detection rate is significantly reduced with the increase in keywords. In the analysis of network case retrieval, the average correct rate of the overall case retrieval will be nearly 65%. Further tests on its reliability show that during the continuous week of the retrieval test, the system has no faults and passed the reliability test. Therefore, through this study, it is found that the application of NLP technology in the legal AI retrieval system has a reliable accuracy, which meets the expectation of this paper.

show abstract

“…Nonrepresentative samples in brain imaging studies, for example, introduce significant bias that can influence our basic understanding of brain structure and function (LeWinn, Sheridan, Keyes, Hamilton, & McLaughlin, 2017). Similarly, in treatment studies, the combination of both under‐representation in study samples (Pickard, Reyes, & Reaven, 2019), and differential patterns of outcomes that vary by racial and ethnic status (Ramsey et al, 2019) raise the possibility (or likelihood) that interpretation of the broader literature without attention to these important factors may be biased. The general lack of attention to racial and ethnic factors in child and mental health research is not unique – a broad survey of more than 26,000 articles in the psychological literature more broadly over the past 50 years found substantial racial inequities in the publication patterns across a number of journals (Roberts, Bareket‐Shavit, Dollins, Goldie, & Mortenson, 2020).…”

Section: Reporting Of Race and Ethnicity And Implicationsmentioning

confidence: 99%

Editorial: Is child mental health research structurally racist?

Kollins

2021

Child Psychology Psychiatry

View full text Add to dashboard Cite

For many people across the world, the past 12 months have been among the most challenging faced in their lifetimes. The COVID-19 pandemic has changed the way we experience and understand our lives and those of others. One striking feature of COVID-19 has been its disproportionate adverse effects on individuals from under-represented minority groups in terms of both morbidity and mortality. This has occurred amidst, and also contributed to, a societal backdropparticularly in the United Statesof intense discussion over whether racial and ethnic inequities across this and other domains of life have been systematically reinforced through political-economic, institutional and cultural structures, practices and presentations. Referred to as structural racism (https://www.aspe ninstitute.org/blog-posts/structural-racism-def inition/), it has been argued that this phenomenon is a social and cultural crisis as pressing and demanding of attention as the COVID-19 health crisis. The impact of these COVID-19 related events has most definitely impacted the scientific literature. A simple PubMed search of the terms 'child mental health' and 'COVID-19' yields 614 articles since January 2020. At the same time, although smaller in number, a similar search of 'child mental health' and 'racism' returns more articles in 2020 than in any previous year. It is interesting and informative, therefore, to think about how the JCPP, with its rigorous scientific approach and clinical focus, should respond to the intersection of these events. Given the recency of COVID-19, definitive evidence for how the virus and associated government-imposed disease-control measures will affect child mental health will likely require months, if not years to understand, in any meaningful way (Sonuga-Barke, 2021). However, by turning the spotlight on health disparities, the COVID-19 pandemic has prompted me to reflect broadly on how our field of child psychology and psychiatry handles race and ethnicity and whether its practices, including those of JCPP, are affected by, or even reinforce, the structural factors thought to perpetuate inequities between groups. I will focus on 3 main areas: how race and ethnicity are routinely handled in the literature; methodological challenges to the equitable handling of these factors; and where we go from here.

show abstract

Racial Differences in Escitalopram/Citalopram-Related Weight Gain in Children and Adolescents: A Natural Language Processing-Based Electronic Medical Record Study

Cited by 5 publications

References 4 publications

Influence of CYP2C19 Metabolizer Status on Escitalopram/Citalopram Tolerability and Response in Youth With Anxiety and Depressive Disorders

Influence of CYP2C19 Metabolizer Status on Escitalopram/Citalopram Tolerability and Response in Youth With Anxiety and Depressive Disorders

Natural Language Processing Technology Used in Artificial Intelligence Scene of Law for Human Behavior

Editorial: Is child mental health research structurally racist?

Contact Info

Product

Resources

About