Suppr超能文献

利用电子健康记录为患有重度抑郁症的退伍军人制定个体化治疗规则。

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

机构信息

Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA.

出版信息

Mol Psychiatry. 2024 Aug;29(8):2335-2345. doi: 10.1038/s41380-024-02500-0. Epub 2024 Mar 14.

Abstract

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

摘要

开发个体化治疗规则 (ITR) 以优化抗抑郁药物 (ADM)、心理治疗或 ADM-心理治疗联合治疗重度抑郁症 (MDD) 的努力受到小样本、小预测因子集和非最佳分析方法的阻碍。旨在模拟实验的大型行政数据库分析随后进行实用试验,有望解决这些问题。本报告介绍了一项概念验证研究,该研究使用了 Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) 诊所的 43,470 名接受 MDD 治疗的门诊患者的电子健康记录 (EHR),这些诊所不仅提供 ADM,还提供心理治疗和 ADM-心理治疗联合治疗。EHR 和地理空间数据库用于生成广泛的基线预测因子集(5,865 个变量)。结果是在接下来的 12 个月内发生至少一次严重不良事件(自杀未遂、精神科急诊就诊、精神科住院、自杀死亡)的复合指标。采用最佳实践方法调整非随机治疗分配,并在 70%的训练样本中估计初步 ITR,并在 30%的测试样本中评估 ITR。发现与基线预测因子相关的总体结果概率存在显著的总体差异(AU-ROC=0.68,S.E.=0.01),在预测风险最高的患者中,测试样本的结果发生率为 5%,而在其余测试样本中,结果发生率为 7.1%。发现 ITR 表明,仅接受心理治疗是 56.0%患者的最佳治疗方法(比接受其他治疗方法的风险低约 20%),而治疗类型与其他患者的结果风险无关。实施该 ITR 的总体治疗费用变化可以忽略不计,因为处方 ADM 的患者减少 16.1%,接受心理治疗的患者增加 2.9%。需要进行一项实用试验来确认 ITR 的准确性。

相似文献

1
Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.
Mol Psychiatry. 2024 Aug;29(8):2335-2345. doi: 10.1038/s41380-024-02500-0. Epub 2024 Mar 14.
4
The influence of patients' preference/attitude towards psychotherapy and antidepressant medication on the treatment of major depressive disorder.
J Behav Ther Exp Psychiatry. 2014 Mar;45(1):170-7. doi: 10.1016/j.jbtep.2013.10.003. Epub 2013 Oct 15.
6
Predictors of persistence of comorbid generalized anxiety disorder among veterans with major depressive disorder.
J Clin Psychiatry. 2011 Nov;72(11):1445-51. doi: 10.4088/JCP.10m05981blu. Epub 2010 Dec 14.
7
10
Treatment Differences in Primary and Specialty Settings in Veterans with Major Depression.
J Am Board Fam Med. 2021 Mar-Apr;34(2):268-290. doi: 10.3122/jabfm.2021.02.200475.

本文引用的文献

1
Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.
Psychiatr Res Clin Pract. 2023 Mar 26;5(4):118-125. doi: 10.1176/appi.prcp.20220015. eCollection 2023 Winter.
3
Algorithmic fairness in artificial intelligence for medicine and healthcare.
Nat Biomed Eng. 2023 Jun;7(6):719-742. doi: 10.1038/s41551-023-01056-8. Epub 2023 Jun 28.
4
Social Drivers of Mental Health: A U.S. Study Using Machine Learning.
Am J Prev Med. 2023 Nov;65(5):827-834. doi: 10.1016/j.amepre.2023.05.022. Epub 2023 Jun 5.
9
Trends in U.S. Depression Prevalence From 2015 to 2020: The Widening Treatment Gap.
Am J Prev Med. 2022 Nov;63(5):726-733. doi: 10.1016/j.amepre.2022.05.014. Epub 2022 Sep 19.
10
Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges.
Mol Psychiatry. 2022 Jun;27(6):2700-2708. doi: 10.1038/s41380-022-01528-4. Epub 2022 Apr 1.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验