Acta psychiatrica Scandinavica · 2019
Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mas S, Gassó P, Rodríguez N, Cabrera B, Mezquida G, Lobo A, González-Pinto A, Parellada M, Corripio I, Vieta E, Castro-Fornieles J, Bobes J, Usall J, Saiz-Ruiz J, Contreras F, Parellada E, Bernardo M, PEPs group
Аннотация
AIMS: Here, we present a clustering strategy to identify phenotypes of antipsychotic (AP) response by using longitudinal data from patients presenting first-episode psychosis (FEP).
METHOD: One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data.
RESULTS: We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness).
CONCLUSIONS: The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.
METHOD: One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data.
RESULTS: We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness).
CONCLUSIONS: The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.