Suppr超能文献

使用电子健康记录对个体进行自动检测以识别处于精神病风险中的个体的个体化跨诊断预测模型的第三次外部复制。

Third external replication of an individualised transdiagnostic prediction model for the automatic detection of individuals at risk of psychosis using electronic health records.

机构信息

Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom.

Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

出版信息

Schizophr Res. 2021 Feb;228:403-409. doi: 10.1016/j.schres.2021.01.005. Epub 2021 Feb 5.

Abstract

BACKGROUND

Primary indicated prevention is a key target for reducing the incidence and burden of schizophrenia and related psychotic disorders. An individualised, clinically-based transdiagnostic model for the detection of individuals at risk of psychosis has been developed and validated in two large, urban healthcare providers. We tested its external validity in a geographically and demographically different non-urban population.

METHOD

Retrospective EHR cohort study. All individuals accessing secondary healthcare provided by Oxford Health NHS Foundation Trust between 1st January 2011 and 30th November 2019 and receiving a primary index diagnosis of a non-psychotic or non-organic mental disorder were considered eligible. The previously developed model was applied to this database and its external prognostic accuracy was measured with Harrell's C.

FINDINGS

The study included n = 33,710 eligible individuals, with an average age of 27.7 years (SD = 19.8), mostly white (92.0%) and female (57.3%). The mean follow-up was 1863.9 days (SD = 948.9), with 868 transitions to psychosis and a cumulative incidence of psychosis at 6 years of 2.9% (95%CI: 2.7-3.1). Compared to the urban development database, Oxford Health was characterised by a relevant case mix, lower incidence of psychosis, different distribution of baseline predictors, higher proportion of white females, and a lack of specialised clinical services for at risk individuals. Despite these differences the model retained an adequate prognostic performance (Harrell's C = 0.79, 95%CI: 0.78-0.81), with no major miscalibration.

INTERPRETATION

The transdiagnostic, individualised, clinically-based risk calculator is transportable outside urban healthcare providers. Further research should test transportability of this risk prediction model in an international setting.

摘要

背景

初级目标预防是降低精神分裂症和相关精神病障碍发生率和负担的关键目标。已经针对个体开发并验证了一种个体化、基于临床的精神障碍风险检测的跨诊断模型,该模型来自两个大型城市医疗保健提供者。我们在地理和人口统计学上不同的非城市人群中测试了该模型的外部有效性。

方法

回顾性电子健康记录队列研究。考虑符合条件的所有个体,这些个体在 2011 年 1 月 1 日至 2019 年 11 月 30 日期间访问牛津健康 NHS 基金会信托的二级医疗保健,并接受非精神病或非器质性精神障碍的主要索引诊断。将之前开发的模型应用于该数据库,并使用哈雷尔 C 测量其外部预后准确性。

发现

该研究包括 n = 33710 名符合条件的个体,平均年龄为 27.7 岁(标准差 = 19.8),大多数为白人(92.0%)和女性(57.3%)。平均随访时间为 1863.9 天(标准差 = 948.9),有 868 人进展为精神病,6 年的精神病累积发生率为 2.9%(95%CI:2.7-3.1)。与城市开发数据库相比,牛津健康的特点是存在相关的病例组合,精神病发生率较低,基线预测因素分布不同,白人女性比例较高,以及缺乏针对高危人群的专门临床服务。尽管存在这些差异,但该模型仍然具有足够的预后性能(哈雷尔 C = 0.79,95%CI:0.78-0.81),没有明显的校准不足。

解释

个体化、基于临床的跨诊断风险计算器可在城市医疗保健提供者之外进行转移。进一步的研究应该在国际环境中测试这种风险预测模型的可转移性。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验