DSM-5 和 ICD-11 人格维度特征在一般医疗队列中的相关性及其与再入院和死亡率的关系。
Association between DSM-5 and ICD-11 personality dimensional traits in a general medical cohort and readmission and mortality.
机构信息
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA; University Psychiatric Clinic, University of Chile Clinical Hospital, Santiago, Chile.
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
出版信息
Gen Hosp Psychiatry. 2020 May-Jun;64:63-67. doi: 10.1016/j.genhosppsych.2020.01.003. Epub 2020 Jan 27.
BACKGROUND
Personality has long been studied as a factor associated with health outcomes. Investigations of large, generalizable clinical cohorts are limited by variations in personality diagnostic methodologies and difficulties with long-term follow-up.
METHODS
Electronic health records of a cohort of patients admitted to a general hospital were characterized using a previously developed natural language processing tool for extracting DSM-5 and ICD-11 personality domains. We used Cox regression and Fine-Gray competing risk survival to analyze the relationships between these personality estimates, sociodemographic features, and risk of readmission and mortality.
RESULTS
Among 12,274 patients, 2379 deaths occurred in the course of 61,761 patient-years at risk, with 19,985 admissions during follow-up. Detachment was the most common personality feature. Presence of disinhibition was independently associated with a higher mortality risk, while anankastic traits were associated with a lower mortality risk. Increased likelihood of readmission was predicted by detachment, while decreased likelihood of readmission was associated with disinhibition and psychoticism traits.
CONCLUSIONS
Personality features can be identified from electronic health records and are associated with readmission and mortality risk. Developing treatment strategies that target patients with higher personality symptom burden in specific dimensions could enable more efficient and focused interventions.
背景
人格一直以来都被研究为与健康结果相关的因素。对大规模、可推广的临床队列的研究受到人格诊断方法学的变化和长期随访困难的限制。
方法
使用先前开发的用于提取 DSM-5 和 ICD-11 人格域的自然语言处理工具，对综合医院住院患者队列的电子健康记录进行了特征描述。我们使用 Cox 回归和 Fine-Gray 竞争风险生存分析来分析这些人格估计值、社会人口统计学特征与再入院和死亡率风险之间的关系。
结果
在 12274 名患者中，在 61761 人年的风险期内发生了 2379 例死亡，随访期间发生了 19985 例入院。疏离是最常见的人格特征。存在去抑制与更高的死亡率风险相关，而强迫型特质与更低的死亡率风险相关。分离与更高的再入院可能性相关，而去抑制和精神病特质与更低的再入院可能性相关。
结论
可以从电子健康记录中识别人格特征，并与再入院和死亡率风险相关。针对特定维度具有更高人格症状负担的患者制定治疗策略，可能会实现更有效和有针对性的干预。
Zotero 插件