Iwanaga Mai, Yamaguchi Sosei, Hashimoto Satoshi, Hanaoka Shimpei, Kaneyuki Hiroshi, Fujita Kiyoshi, Kishi Yoshiki, Hirata Toyoaki, Fujii Chiyo, Sugiyama Naoya
Department of Community Mental Health and Law, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
Department of Psychiatry, National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan.
Front Psychiatry. 2024 Feb 8;15:1303189. doi: 10.3389/fpsyt.2024.1303189. eCollection 2024.
In order to uphold and enhance the emergency psychiatric care system, a thorough comprehension of the characteristics of patients who require a high-acuity psychiatry unit is indispensable. We aimed to clarify the most important predictors of the need for a high-acuity psychiatry unit using a random forest model.
This cross-sectional study encompassed patients admitted to psychiatric emergency hospitals at 161 medical institutions across Japan between December 8, 2022, and January 31, 2023. Questionnaires were completed by psychiatrists, with a maximum of 30 patients assessed per medical institution. The questionnaires included psychiatrists' assessment of the patient's condition (exposure variables) and the need for a high-acuity psychiatry unit (outcome variables). The exposure variables consisted of 32 binary variables, including age, diagnoses, and clinical condition (i.e., factors on the clinical profile, emergency treatment requirements, and purpose of hospitalization). The outcome variable was the need for a high-acuity psychiatry unit, scored from 0 to 10. To identify the most important predictors of the need for a high-acuity psychiatry unit, we used a random forest model. As a sensitivity analysis, multivariate linear regression analysis was performed.
Data on 2,164 patients from 81 medical institutions were obtained (response rate, 50.3%). After excluding participants with missing values, this analysis included 2,064 patients. Of the 32 items, the top-5 predictors of the need for a high-acuity psychiatry unit were the essentiality of inpatient treatment (otherwise, symptoms will worsen or linger), need for 24-hour professional care, symptom severity, safety ensured by specialized equipment, and medication management. These items were each significantly and positively associated with the need for a high-acuity psychiatry unit in linear regression analyses (p < 0.001 for all). Conversely, items on age and diagnosis were lower in the ranking and were not statistically significant in linear regression models.
Items related to the patient's clinical profile might hold greater importance in predicting the need for a high-acuity psychiatry unit than do items associated with age and diagnosis.
为了维护和加强急诊精神科护理系统，全面了解需要高急症精神科病房的患者特征是必不可少的。我们旨在使用随机森林模型明确需要高急症精神科病房的最重要预测因素。
这项横断面研究涵盖了2022年12月8日至2023年1月31日期间日本161家医疗机构收治到精神科急诊医院的患者。问卷由精神科医生填写，每家医疗机构最多评估30名患者。问卷包括精神科医生对患者病情的评估（暴露变量）以及对高急症精神科病房的需求（结果变量）。暴露变量由32个二元变量组成，包括年龄、诊断和临床状况（即临床特征、急诊治疗需求和住院目的方面的因素）。结果变量是对高急症精神科病房的需求，评分从0到10。为了确定需要高急症精神科病房的最重要预测因素，我们使用了随机森林模型。作为敏感性分析，进行了多元线性回归分析。
获得了来自81家医疗机构的2164名患者的数据（回复率为50.3%）。在排除有缺失值的参与者后，该分析纳入了2064名患者。在这32项中，需要高急症精神科病房的前5个预测因素是住院治疗的必要性（否则症状会恶化或持续）、24小时专业护理的需求、症状严重程度、专用设备确保的安全性以及药物管理。在线性回归分析中，这些项目中的每一项都与需要高急症精神科病房显著正相关（所有p值均<0.001）。相反，年龄和诊断方面的项目排名较低，在线性回归模型中无统计学意义。
与患者临床特征相关的项目在预测对高急症精神科病房的需求方面可能比与年龄和诊断相关的项目更为重要。
