预测从单极抑郁症转化为双极障碍和精神分裂症:对12,182名住院患者进行了为期10年的回顾性队列研究
Ting Zhu1,2, Ran Kou3, Di Mu1,2,4
1West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Depression and anxiety
|April 14, 2025
概括
这项研究确定了单极性抑郁症转化为双极性障碍 (BD) 和精神分裂症 (SCZ) 的预测因素,强调了对这些严重精神疾病的早期风险评估的重要性.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算精神病学是一种计算精神病学.
- 医疗保健中的机器学习
背景情况:
- 双极性障碍 (BD) 和精神分裂症 (SCZ) 的初始抑郁症状可能导致误诊为单极性抑郁症 (UD).
- 关于从UD转换为BD和SCZ的个性化,时间变化的预测指标缺乏共识.
- 需要预测模型来分层对UD患者BD/SCZ转换的风险.
研究的目的:
- 检查 UD 到 BD 和 SCZ 的转换率.
- 开发BD/SCZ转换短期,中期和长期风险分层的预测模型.
- 使用可解释的人工智能识别导致疾病进展的关键特征.
主要方法:
- 从2009年到2020年,对12182名抑郁症住院患者的电子病历 (EMR) 进行了回顾性分析.
- 应用四种机器学习算法,利用社会人口,临床,实验室,生命体征,症状和治疗数据.
- 使用了SHapley添加式扩展 (SHAP) 和分解来进行特征贡献分析.
主要成果:
- 转换率:2.82%转换为BD和0.53%转换为SCZ.
- 患有BD的危险因素:女性性别,严重抑郁症,情绪稳定剂,β-阻断剂,抗精神病药物.
- 对SCZ的危险因素:男性性别,精神病症状,抗精神病药物,抗副作用药物,心理治疗.
- 家庭精神疾病病史显著增加了对BD和SCZ转换的敏感性.
- 耐药性和精神病性UD患者显示过渡风险较高.
结论:
- 随着时间的推移,预测模型的性能下降 (BD的AUC:0.771在1年到0.733在7年;SCZ:0.866在1年到0.752在7年).
- 社会人口因素,生活方式,生命体征和血液标志物成为随访的重要风险因素.
- 经过验证的模型可以为临床医生提供疾病转换的动态风险信息.
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