Expanding resources of endovascular thrombectomy: An optimization model

Chun Han Wang, Ting Yu Liu, Wen Chu Chiang, Sung Chun Tang, Li Kai Tsai, Chung Wei Lee, Yen Heng Lin, Jiann Shing Jeng, Matthew Huei Ming Ma, Ming Ju Hsieh, Yu Ching Lee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background/Purpose: Recently optimized models for selecting the locations of hospitals capable of providing endovascular thrombectomy (EVT) did not consider the accuracy of the prehospital stroke scale assessment and possibility of secondary transport. Our study aimed to propose a new model for selecting existing hospitals with intravenous thrombolysis capability to become EVT-capable hospitals. Methods: A sequential order was provided to upgrade hospitals providing intravenous thrombolysis, using a mixed integer programming model based on current medical resource allocation. In addition, we drafted a centralized plan to redistribute existing EVT resources by redetermining locations of EVT-capable hospitals. Using historical data of 7679 on-scene patients with suspected stroke, the model was implemented to determine the hospital that maximizes the number of patients receiving EVT treatment within call-to-definitive-treatment time. Results: All suspected stroke patients were sent to EVT-capable hospitals directly under the current medical resource allocation model. After upgrading one additional hospital to become an EVT-capable hospital, the percentage of patients receiving definitive treatment within the standard call-to-definitive-treatment time was elevated from 68.82% to 72.97%. In the model, assuming that there is no hospital providing EVT, all patients suspected of stroke will be sent to EVT-capable hospitals directly after upgrading three or more hospitals to be able to provide treatment. Conclusion: All patients eligible for acute stroke treatment are sent to EVT-capable hospitals in the simulation under the current medical resource allocation model. This model can be utilized to provide insights for capacity redistribution in other regions.

Original languageEnglish
Pages (from-to)978-985
Number of pages8
JournalJournal of the Formosan Medical Association
Volume121
Issue number5
DOIs
Publication statusPublished - 2022 May

All Science Journal Classification (ASJC) codes

  • General Medicine

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