Unlocking treatment success: predicting atypical antipsychotic continuation in youth with mania.
Atypical antipsychotics
Machine learning
Mania
Prediction model
SuperLearner
Journal
BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682
Informations de publication
Date de publication:
02 Aug 2024
02 Aug 2024
Historique:
received:
24
09
2023
accepted:
25
07
2024
medline:
3
8
2024
pubmed:
3
8
2024
entrez:
2
8
2024
Statut:
epublish
Résumé
This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for clinical treatment. The study population was collected from the national claims database in China. A total of 4,532 patients aged 4-18 who began risperidone therapy for mania between September 2013 and October 2019 were identified. The data were randomly divided into two datasets: training (80%) and testing (20%). Five regularly used machine learning methods were employed, in addition to the SuperLearner (SL) algorithm, to develop prediction models for the continuation of atypical antipsychotic therapy. The area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI) was utilized. In terms of discrimination and robustness in predicting risperidone treatment continuation, the generalized linear model (GLM) performed the best (AUC: 0.823, 95% CI: 0.792-0.854, intercept near 0, slope close to 1.0). The SL model (AUC: 0.823, 95% CI: 0.791-0.853, intercept near 0, slope close to 1.0) also exhibited significant performance. Furthermore, the present findings emphasize the significance of several unique clinical and socioeconomic variables, such as the frequency of emergency room visits for nonmental health disorders. The GLM and SL models provided accurate predictions regarding risperidone treatment continuation in children and adolescents with episodes of mania and hypomania. Consequently, applying prediction models in atypical antipsychotic medicine may aid in evidence-based decision-making.
Identifiants
pubmed: 39095826
doi: 10.1186/s12911-024-02622-z
pii: 10.1186/s12911-024-02622-z
doi:
Substances chimiques
Antipsychotic Agents
0
Risperidone
L6UH7ZF8HC
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
219Subventions
Organisme : Natural Science Foundation of Sichuan Province
ID : 2022NSFSC0725
Informations de copyright
© 2024. The Author(s).
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