Departments of Emergency Medicine (K.S.Z.), Massachusetts General Hospital, Boston.
Harvard Medical School (K.S.Z., L.H.S.), Boston, MA.
Circ Cardiovasc Qual Outcomes. 2022 May;15(5):e008269. doi: 10.1161/CIRCOUTCOMES.121.008269. Epub 2022 Apr 4.
Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke.
Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework.
Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher).
After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were important factor in determining transfer destination of patients with stroke. This study provides a starting point for future research exploring how relational coordination between hospitals may ensure optimized allocation of patients with stroke for maximal patient benefit.
脑卒中患者经常在医院之间转移。这可能会对患者接受的护理质量产生影响，但目前尚不清楚发送和接收医院的特征如何影响转移事件的可能性。我们的目的是确定与发送和接收脑卒中患者相关的医院特征。
使用包括马萨诸塞州所有 78 家医院在内的全面全州行政数据集，我们对 2007 年 10 月至 2015 年 9 月期间所有缺血性脑卒中患者的转移进行了这项观察性研究。医院变量包括声誉（《美国新闻与世界报道》排名）、能力（卒中中心地位、年度脑卒中量和创伤中心指定）和机构隶属关系。我们包括网络变量来控制医院间转移的结构。我们使用关系事件建模来解释与转移相关的复杂时间和关系依赖性。该方法将一系列患者转移分解为一系列以转移发起和目的地为特征的决策，使用离散选择框架对其进行建模。
在 73114 例缺血性脑卒中入院患者中，研究期间有 7189 例（9.8%）发生转移。在考虑到医院之间的旅行时间和结构网络特征后，与成为接收医院的可能性增加相关的因素（按相对效应大小降序排列）包括共享医院隶属关系（高 5.8 倍）、教学医院地位（高 4.2 倍）、卒中中心地位（同等或更高地位时高 4.3 倍和 3.8 倍）和同等或更高声誉排名的医院（高 1.5 倍）。
在考虑到距离和结构网络特征后，按重要性降序排列，共享医院隶属关系、医院能力和医院声誉是确定脑卒中患者转移目的地的重要因素。本研究为未来探索医院之间的关系协调如何确保优化脑卒中患者的分配以实现最大患者获益的研究提供了起点。
