TY - JOUR
T1 - Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients
AU - Lopez Pineda, Arturo
AU - Pourshafeie, Armin
AU - Ioannidis, Alexander
AU - Leibold, Collin Mc Closkey
AU - Chan, Avis L.
AU - Bustamante, Carlos D.
AU - Frankovich, Jennifer
AU - Wojcik, Genevieve L.
N1 - Funding Information:
Contributions, ALP designed the study. JF designed the patient inclusion/exclusion criteria, designed the medication categories and medication search strategy, designed the prospective clinical questionnaires and outcome scores and supervised the clinical database and medication abstraction and entry. CMCL, AC, and JF reviewed medical notes and drafted part of the manuscript. GLW, AP, AI, and ALP performed analysis of data. CDB, GLW, and JF provided interpretation of the results. ALP drafted the manuscript, and all authors contributed critically, read, revised and approved the final version. Research reported in this publication was partially supported via institutional funds from Stanford University. CDB is a Chan Zuckerberg Biohub investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was approved by Stanford University's Institutional Review Board (IRB). Written informed consents were obtained from parents of minors, and assents were obtained from subjects aged 7 to 17 (all patients in this cohort) who were able to understand it (IRB protocol #26922). We did not have the opportunity to re-consent two patients at the time of data abstraction and analysis. The inclusion of their data is covered by a retrospective chart review protocol (IRB protocol #28533). Authors CMCL, AC, and JF served as honest brokers securing identifiable information. The remaining authors only had access to de-identified information. Non-human subject determination was provided (IRB protocol #46979). The data that supports the findings of this study is available for research purposes upon written request, which will be reviewed on a case-by-case basis by Dr. Jennifer Frankovich, director of the Stanford PANS clinic (http://med.stanford.edu/pans), and Stanford's Institutional Review Board (IRB). The MedAl and PyMedAl implementation can be accessed through the public repository: https://github.com/bustamante-lab/medal.
Funding Information:
Research reported in this publication was partially supported via institutional funds from Stanford University. CDB is a Chan Zuckerberg Biohub investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020
PY - 2021/1
Y1 - 2021/1
N2 - Objective: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort. Materials and methods: In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy. Results: This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician's practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years. Discussion and conclusion: Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.
AB - Objective: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort. Materials and methods: In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy. Results: This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician's practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years. Discussion and conclusion: Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs.
KW - Cluster analysis
KW - Longitudinal studies
KW - Patient similarity
KW - Pediatric acute-onset neuropsychiatric syndrome
KW - Polypharmacy
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U2 - 10.1016/j.jbi.2020.103664
DO - 10.1016/j.jbi.2020.103664
M3 - Article
C2 - 33359113
AN - SCOPUS:85098694821
VL - 113
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
SN - 1532-0464
M1 - 103664
ER -