Kang Jihoon, Song Hyunjoo, Kim Seong Eun, Kim Jun Yup, Park Hong-Kyun, Cho Yong-Jin, Lee Kyung Bok, Lee Juneyoung, Lee Ji Sung, Choi Ah Rum, Kang Mi Yeon, Gorelick Philip B, Bae Hee-Joon
Neurology, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of).
School of Computer Science and Engineering, Soongsil University, Seoul, Korea (the Republic of).
BMJ Neurol Open. 2024 Apr 9;6(1):e000578. doi: 10.1136/bmjno-2023-000578. eCollection 2024.
The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.
We extracted patient-level data on acute stroke care and hospital connectivity by integrating national stroke audit data with reimbursement claims data. We then used this information to transform interhospital transfers into a network framework, where hospitals were designated as nodes and transfers as edges. Using the Louvain algorithm, we grouped densely connected hospitals into distinct stroke care communities. The quality and characteristics in given stroke communities were analysed, and their distinct types were derived using network parameters. The clinical implications of this network model were also explored.
Over 6 months, 19 113 patients with acute ischaemic stroke initially presented to 1009 hospitals, with 3114 (16.3%) transferred to 246 stroke care hospitals. These connected hospitals formed 93 communities, with a median of 9 hospitals treating a median of 201 patients. Derived communities demonstrated a modularity of , indicating a strong community structure, highly centralised around one or two hubs. Three distinct types of structures were identified: single-hub (n=60), double-hub (n=22) and hubless systems (n=11). The endovascular treatment rate was highest in double-hub systems, followed by single-hub systems, and was almost zero in hubless systems. The hubless communities were characterised by lower patient volumes, fewer hospitals, no hub hospital and no stroke unit.
This network analysis could quantify the national stroke care system and point out areas where the organisation and functionality of acute stroke care could be improved.
卒中护理格局已从独立医院转向医院间的合作网络。尽管这些网络很重要,但关于其特征和功能属性的信息有限。
我们通过整合国家卒中审计数据和报销申请数据,提取了急性卒中护理和医院连通性的患者层面数据。然后,我们利用这些信息将医院间转诊转化为网络框架,其中医院被指定为节点,转诊为边。使用鲁汶算法,我们将紧密相连的医院分组为不同的卒中护理社区。分析了给定卒中社区的质量和特征,并使用网络参数得出其不同类型。还探讨了这种网络模型的临床意义。
在6个多月的时间里,19113例急性缺血性卒中患者最初就诊于1009家医院,其中3114例(16.3%)被转诊至246家卒中护理医院。这些相连的医院形成了93个社区,中位数为9家医院,治疗患者中位数为201例。得出的社区模块化程度为 ,表明社区结构强大,高度集中在一两个枢纽周围。识别出三种不同类型的结构:单枢纽(n = 60)、双枢纽(n = 22)和无枢纽系统(n = 11)。双枢纽系统的血管内治疗率最高,其次是单枢纽系统,无枢纽系统几乎为零。无枢纽社区的特点是患者数量较少、医院较少、没有枢纽医院且没有卒中单元。
这种网络分析可以量化国家卒中护理系统,并指出急性卒中护理的组织和功能可以改进的领域。