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使用真实世界数据对与住院患者谵妄诊断相关的合并症进行性别分层分析。

Sex-stratified analyses of comorbidities associated with an inpatient delirium diagnosis using real world data.

作者信息

Sirota Marina, Kodama Lay, Woldemariam Sarah, Tang Alice, Li Yaqiao, Kornak John, Allen Isabel E, Raphael Eva, Oskotsky Tomiko

机构信息

University of California, San Francisco.

University of California San Francisco.

出版信息

Res Sq. 2024 Jul 23:rs.3.rs-4765249. doi: 10.21203/rs.3.rs-4765249/v1.

Abstract

Delirium is a detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage electronic health records (EHR) to identify 7,492 UCSF patients and 19,417 UC health system patients with an inpatient delirium diagnosis and the same number of control patients without delirium. We found significant associations between comorbidities or laboratory values and an inpatient delirium diagnosis, including metabolic abnormalities and psychiatric diagnoses. Some associations were sex-specific, including dementia subtypes and infections. We further explored the associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis to development of delirium, demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to increased risk of mortality. These results demonstrate the powerful application of the EHR to shed insights into prior diagnoses and laboratory values that could help predict development of inpatient delirium and the importance of sex when making these assessments.

摘要

谵妄是一种有害的精神状态,常见于老年住院患者,目前难以预测。在本研究中,我们利用电子健康记录(EHR)识别出7492名加州大学旧金山分校的患者和19417名加州大学健康系统的患者,他们被诊断为住院期间谵妄,同时还识别出相同数量的未患谵妄的对照患者。我们发现合并症或实验室检查值与住院期间谵妄诊断之间存在显著关联,包括代谢异常和精神疾病诊断。一些关联存在性别差异,包括痴呆亚型和感染。我们通过从首次诊断到谵妄发生进行纵向分析,进一步探讨了与贫血和双相情感障碍的关联,结果显示随时间推移存在显著关系。最后,我们表明住院期间谵妄诊断会导致死亡风险增加。这些结果证明了EHR在深入了解既往诊断和实验室检查值方面的强大应用,这些信息有助于预测住院期间谵妄的发生,以及在进行这些评估时考虑性别的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f6bb/11302686/c270af66531b/nihpp-rs4765249v1-f0001.jpg

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