预测诊断转换从严重抑郁症到双相情感障碍:来自哥伦比亚的基于电子健康记录的研究
Susan K Service1, Juan De La Hoz1, Ana M Diaz-Zuluaga1
1Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA.
medRxiv : the preprint server for health sciences
|October 24, 2023
概括
预测双极性障碍 (BD) 从重大抑郁障碍 (MDD) 转化至关重要. 电子病历数据分析确定了关键风险因素,如初始发作的严重程度和自杀念头,使得早期BD诊断和改善患者的治疗结果.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
背景情况:
- 双极性障碍 (BD) 是一种慢性精神健康状况,通常最初表现为严重抑郁症 (MDD),导致诊断延迟.
- 早期识别患有MDD的个体,他们面临转化为BD的高风险,对于及时干预和改善预后至关重要.
研究的目的:
- 从电子健康记录 (EHR) 中识别预测重大抑郁症 (MDD) 转化为双极性障碍 (BD) 的临床和人口特征.
- 探索自然语言处理 (NLP) 在临床笔记上的实用性,以发现 BD转换的新风险因素.
主要方法:
- 从15年来对13,607名被诊断患有MDD的患者的EHR数据进行了回顾性分析.
- 多变量考克斯回归建模,以确定BD转换的预测特征.
- 应用NLP技术从非结构化的临床笔记中提取临床特征.
主要成果:
- 关于BD转换的关键预测因素包括初始MDD发作严重程度,精神病,住院,家族病史和女性性别.
- NLP将自杀念头和自杀企图确定为BD转换的新风险因素.
- 鉴定出的风险因素预测了BD在五年内转换,回忆率为72%,准确度为38%.
结论:
- 包括NLP在内的EHR数据分析是预测MDD的BD转换的有效和强大的工具.
- 这项研究证实了已知的风险因素,并确定了新的风险因素,支持早期诊断和针对双极性障碍的有针对性的干预措施.
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