Objective There is little evidence for finding optimal antipsychotic treatment for schizophrenia, especially in paediatrics. To evaluate the performance and clinical benefit of several prediction methods for 1-year treatment continuation of antipsychotics. Design and Settings Population-based prognostic study conducting using the nationwide claims database in Korea. Participants 5109 patients aged 2–18 years who initiated antipsychotic treatment with risperidone/aripiprazole for schizophrenia between 2010 and 2017 were identified. Main outcome measures We used the conventional logistic regression (LR) and common six machine-learning methods (least absolute shrinkage and selection operator, ridge, elstic net, randomforest, gradient boosting machine, and superlearner) to derive predictive models for treatment continuation of antipsychotics. The performance of models was assessed using the Brier score (BS), area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The clinical benefit of applying these models was also evaluated by comparing the treatment continuation rate between patients who received the recommended medication by models and patients who did not. Results The gradient boosting machine showed the best performance in predicting treatment continuation for risperidone (BS, 0.121; AUROC, 0.686; AUPRC, 0.269). Among aripiprazole models, GBM for BS (0.114), SuperLearner for AUROC (0.688) and random forest for AUPRC (0.317) showed the best performance. Although LR showed lower performance than machine learnings, the difference was negligible. Patients who received recommended medication by these models showed a 1.2–1.5 times higher treatment continuation rate than those who did not. Conclusions All prediction models showed similar performance in predicting the treatment continuation of antipsychotics. Application of prediction models might be helpful for evidence-based decision-making in antipsychotic treatment. Data are available upon reasonable request. This study used Health Insurance Review and Assessment Service database (M20190117533). Requests to access these datasets should be directed to HIRA; Official website of HIRA: ; Contact information of data access committee: +82-33-739-1083.

中文翻译：

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儿童青少年精神分裂症抗精神病药继续治疗预测方法比较

目的 几乎没有证据表明可以找到精神分裂症的最佳抗精神病药物治疗方法，尤其是在儿科方面。评估抗精神病药 1 年持续治疗的几种预测方法的性能和临床益处。设计和设置使用韩国全国索赔数据库进行的基于人群的预后研究。参与者确定了 5109 名 2-18 岁的患者，这些患者在 2010 年至 2017 年间开始使用利培酮/阿立哌唑抗精神病药物治疗精神分裂症。主要结果指标 我们使用常规逻辑回归 (LR) 和常见的六种机器学习方法（最小绝对收缩和选择算子、岭、弹性网、随机森林、梯度提升机和超级学习器）来推导抗精神病药物治疗延续的预测模型. 使用 Brier 评分 (BS)、接受者操作特征曲线下面积 (AUROC) 和精确回忆曲线下面积 (AUPRC) 评估模型的性能。还通过比较接受模型推荐药物治疗的患者与未接受模型推荐药物的患者之间的治疗持续率来评估应用这些模型的临床益处。结果梯度提升机在预测利培酮治疗持续性方面表现最佳（BS，0.121；AUROC，0.686；AUPRC，0.269）。在阿立哌唑模型中，GBM for BS (0.114)、SuperLearner for AUROC (0.688) 和 random forest for AUPRC (0.317) 表现最好。尽管 LR 的性能低于机器学习，但差异可以忽略不计。接受这些模型推荐药物治疗的患者显示为 1.2–1。治疗持续率比未接受者高 5 倍。结论 所有预测模型在预测抗精神病药物治疗的持续性方面表现相似。预测模型的应用可能有助于抗精神病药物治疗中的循证决策。可根据合理要求提供数据。本研究使用健康保险审查和评估服务数据库 (M20190117533)。访问这些数据集的请求应提交给 HIRA；HIRA官网：可根据合理要求提供数据。本研究使用健康保险审查和评估服务数据库 (M20190117533)。访问这些数据集的请求应提交给 HIRA；HIRA官网：可根据合理要求提供数据。本研究使用健康保险审查和评估服务数据库 (M20190117533)。访问这些数据集的请求应提交给 HIRA；HIRA官网：; 数据访问委员会的联系信息：+82-33-739-1083。