Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States of America.
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, United States of America.
PLoS One. 2019 Jul 10;14(7):e0219514. doi: 10.1371/journal.pone.0219514. eCollection 2019.
The main purpose of this paper was to model the process by which patients enter the ED, are seen by physicians, and discharged from the Emergency Department at Nationwide Children's Hospital, as well as identify modifiable factors that are associated with ED lengths of stay through use of multistate modeling.
In this study, 75,591 patients admitted to the ED from March 1st, 2016 to February 28th, 2017 were analyzed using a multistate model of the ED process. Cox proportional hazards models with transition-specific covariates were used to model each transition in the multistate model and the Aalen-Johansen estimator was used to obtain transition probabilities and state occupation probabilities in the ED process.
Acuity level, season, time of day and number of ED physicians had significant and varying associations with the six transitions in the multistate model. Race and ethnicity were significantly associated with transition to left without being seen, but not with the other transitions. Conversely, age and gender were significantly associated with registration to room and subsequent transitions in the model, though the magnitude of association was not strong.
The multistate model presented in this paper decomposes the overall ED length of stay into constituent transitions for modeling covariate-specific effects on each transition. This allows physicians to understand the ED process and identify which potentially modifiable covariates would have the greatest impact on reducing the waiting times in each state in the model.
本文的主要目的是通过多状态建模来模拟患者进入急诊部、接受医生诊治以及从全国儿童医院急诊部出院的过程，并确定与急诊部停留时间相关的可改变因素。
本研究分析了 2016 年 3 月 1 日至 2017 年 2 月 28 日期间 75591 名入急诊部的患者，使用急诊部流程的多状态模型。使用具有特定转移协变量的 Cox 比例风险模型来对多状态模型中的每个转移进行建模，并使用 Aalen-Johansen 估计器来获得急诊部流程中的转移概率和状态占用概率。
严重程度级别、季节、一天中的时间和急诊部医生人数与多状态模型中的六个转移具有显著且不同的关联。种族和民族与未接受诊治而离开的转移显著相关，但与其他转移无关。相反，年龄和性别与登记到病房以及模型中的后续转移显著相关，尽管关联的程度不强。
本文提出的多状态模型将整体急诊部停留时间分解为构成转移，以对每个转移的特定协变量效应进行建模。这使医生能够了解急诊部的流程，并确定哪些潜在的可改变的协变量将对模型中每个状态的等待时间产生最大影响。
