Dataset Information

A Network Approach to Stroke Systems of Care.

ABSTRACT: The landscape of stroke systems of care is evolving as patients are increasingly transferred between hospitals for access to higher levels of care. This is driven by time-sensitive disability-reducing interventions such as mechanical thrombectomy. However, coordination and triage of patients for such treatment remain a challenge worldwide, particularly given complex eligibility criteria and varying time windows for treatment. Network analysis is an approach that may be applied to this problem. Hospital networks interlinked by patients moved from facility to facility can be studied using network modeling that respects the interdependent nature of the system. This allows understanding of the central hubs, the change of network structure over time, and the diffusion of innovations. This topical review introduces the basic principles of network science and provides an overview on the applications and potential interventions in stroke systems of care.

SUBMITTER: Zachrison KS

PROVIDER: S-EPMC6822608 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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A Network Approach to Stroke Systems of Care.

Zachrison Kori S KS Dhand Amar A Schwamm Lee H LH Onnela Jukka-Pekka JP

Circulation. Cardiovascular quality and outcomes 20190813 8

The landscape of stroke systems of care is evolving as patients are increasingly transferred between hospitals for access to higher levels of care. This is driven by time-sensitive disability-reducing interventions such as mechanical thrombectomy. However, coordination and triage of patients for such treatment remain a challenge worldwide, particularly given complex eligibility criteria and varying time windows for treatment. Network analysis is an approach that may be applied to this problem. H ...[more]

PMID: 31405293

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Project description:Background and Purpose—Systemic thrombolysis represents the main proven therapy for acute ischemic stroke, but safe treatment is reported only in well-established stroke units. To extend the use of tissue plasminogen activator (tPA) treatment in primary care hospitals on isolated areas through telemedic was the purpose of specific initiatives in southern Umbria, Italy. Methods—The stroke center of Foligno established a telestroke network to provide consultations for three local hospitals in southern Umbria. The telemedic system consists of a digital network that includes a two-way video conference system and imaging sharing. The main network hospital established specialized stroke wards/teams in which qualified teams treat acute stroke patients. Physicians in these hospitals are able to contact the stroke centers 24 h per day. Quality data are available to support the safe implementation of the stroke procedures. Those available from governmental authorities and local datasets are volume of hospitalization, in-hospital mortality, 30-days mortality, and discharge setting. Objective of the study was to assess the annual hospitalization volume in both the hub and spoke hospitals for ischemic stroke and appraise the performance of the network after the introduction of the telestroke system. Results—A total of 225 systemic thrombolyses were performed in time period indicated above all hospitals. In the main spoke hospital, 41 procedures were performed after teleconsultations were made available. The thrombolysis rate in the hub hospital ranged between 10% in 2016 and 20% in 2019, while in the spoke hospital was below 5% in 2016 and raised to 15% in 2019. The statistically significant difference, in the number of procedures, between hub and spoke in the beginning of the observation time disappeared after introduction of the telestroke network. No increase of the mortality was found. Conclusions—The present data suggest that systemic thrombolysis indicated via stroke experts in the setting of teleconsultation shows similar complication rates to those reported in the National Institute of Neurological Disorders and Stroke trial. Therefore, tPA treatment is also safe in this context and can be extended to primary hospitals.

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Project description:Due to the ubiquitous nature of disruptive extreme events, functionality of the critical infrastructure systems (CIS) is constantly at risk. In case of a disruption, in order to minimize the negative impact to the society, service networks operating on the CIS should be restored as quickly as possible. In this paper, we introduce a novel network science inspired measure to quantify the criticality of components within a disrupted service network and develop a restoration heuristic (Cent-Restore) that prioritizes restoration efforts based on this measure. As an illustrative case study, we consider a road network blocked by debris in the aftermath of a natural disaster. The debris obstructs the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites, debilitating the emergency service network. In this context, the problem is defined as finding a schedule to clear the roads with the limited resources. First, we develop a mixed-integer programming model for the problem. Then we validate the efficiency and accuracy of the Cent-Restore heuristic on randomly generated instances by comparing it to the model. Furthermore, we use Cent-Restore to recommend real-time restoration plans for disrupted road networks of Boston and Manhattan and analyze the performance of the plans over time through resilience curves. We compare Cent-Restore to the current restoration guidelines proposed by FEMA and other strategies that prioritize the restoration efforts based on different measures. As a result we confirm the importance of including specific post-disruption attributes of the networks to create effective restoration strategies. Moreover, we explore the relationship between a service network's resilience and its topological and operational characteristics under different disruption scenarios. The methods and insights provided in this work can be extended to other disrupted large-scale critical infrastructure systems in which the ultimate goal is to enable the functions of the overlaying service networks.

Dataset Information

A Network Approach to Stroke Systems of Care.

Publications

A Network Approach to Stroke Systems of Care.

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