中風是導致死亡和殘疾的主要原因之一，在治療時間窗內接受及時且正確的治療對中風患者的健康恢復至關重要。相較於其他急症，潛在中風患者的輸送面臨更多挑戰，如需針對不同類型病患提供不同治療、並非所有醫院都能提供所需治療、以及不同治療的治療時間窗限制等，使得運送潛在中風患者之醫院選擇更加複雜，這些不確定性的因素影響了患者是否得以在治療時間窗內獲得正確治療的可能性，包括院內處理時間、患者可能需接受不同治療或必須進行轉院等因素。因此，為了處理這樣的問題，本研究受金融模型中風險趨避理論的啟發，採用了一種穩健的最佳化模型，目的是最大化患者在治療時間窗內接受正確治療的機率。我們將透過最佳化模型所得到的送醫決策與目前廣泛使用的四種送醫策略進行比較，利用台北市中風登記資料庫中2019年7月1日至2020年12月31日的全市中風登記實際數據進行回溯性研究，討論執行該策略的利益及潛在損失。這些數據包括每位患者的病發地點、辛辛那提預醫院中風量表（Cincinnati Prehospital Stroke Scale, CPSS）分數、院前時間，以及在每間醫院的院內處理時間，並使用Google Maps計算交通時間以及AMPL自動化決策流程，同時計算病患從病症發生至接受正確治療的時間。隨後，透過蒙特卡羅（Monte Carlo）模擬生成726名患者的隨機院內時間各十次，並對每種策略進行拔靴法（Bootstrapping），以比較不同院前分流策略。

結果表明，我們所提出的風險趨避策略對於有急性缺血性（Acute Ischemic Stroke, AIS）且大血管閉塞（Large Vessel Occlusion, LVO）的中風患者始終優於其他策略。在6小時，即血管內血栓切除術（Endovascular Thrombectomy, EVT）目標時間窗口內，81.983%（95% CI：[79.762, 84.127]）的LVO中風患者接受了EVT治療，這比Mothership策略高出4.755%，甚至比Nearest策略高出13.665%。我們需要強調風險趨避策略加速了在2.5至5.75小時內接受到正確性治療的病患，而平均接受到EVT治療的時間可縮短至268.045分鐘（95% CI：[258.276, 278.516]）。對於無LVO的中風患者，在4.5小時，即靜脈溶栓治療（Intravenous Thrombolysis, IVT）目標時間窗口內，80.576%（95% CI：[79.583, 81.567]）接受了IVT，此比例相較於所有策略而言為次優的結果，在較小的時間窗內表現也常為最佳。然而，這一策略可能對於發病時間超過3小時的患者帶來IVT延遲治療的風險。我們發現在執行我們提出策略下，在被診斷為AIS但沒有LVO的患者中，相對於處理該類型病患有最短的平均時間之Pessimistic策略相比，需要接受4.995分鐘（95% CI：[-0.425, 10.414]）延遲施打IVT的潛在風險。然而，該信賴區間包含零，這意味著這種延遲對於此類患者在長期內並無顯著差異。另一個重要考量是當AIS患者的症狀發作到接受IVT的確定治療時間超過4.5小時，這些患者通常會被排除在此類治療之外。因此，我們認為此送一醫策略是值得被嘗試執行。最後，我們基於此模型開發了一個送醫決策網站，提供急救醫療技術人員現場使用，並制定相關協議應對各種可能情況，同時，我們將與指導醫師持續討論，探討真實實施此策略之可行性。

Timely treatment within the therapeutic time window is of critical importance for stroke, a major cause of death and disability. Inspired by risk aversion principles in financial models, our study employs an optimization approach to maximize the opportunity of patients receiving definitive treatment within the time window, considering uncertainties in in-hospital processing times, as they can significantly impact the likelihood of patients receiving timely treatment. We discuss cases of patients with and without large vessel occlusion (LVO) separately to gain a more comprehensive understanding of the impact of the proposed Risk-Averse strategy.

Data from a citywide stroke registry from July 1, 2019, to December 31, 2020, were used to develop an optimization model for conducting retrospective analysis. The locations of the prehospital scenes and the 10 hospitals where stroke care was provided, along with the scores of the Cincinnati Prehospital Stroke Scale (CPSS), the prehospital times from symptom onset for each patient, and the in-hospital processing times—including endovascular thrombectomy for patients with LVO and intravenous thrombolysis for those without LVO—in each hospital, were collected. Google Maps and mathematical programming language were used to calculate the transport time and hospital delivery decisions respectively. Subsequently, we assessed the Risk-Averse strategy against 4 prehospital triage strategies by employing Monte Carlo simulations to generate random in-hospital times for each of the 726 patients ten times and conducting bootstrapping iterations of each strategy to evaluate potential benefits or losses under the proposed strategy.

The Risk-Averse strategy consistently outperforms alternatives for stroke patients with LVO. Within 6 hours, (i.e., the endovascular thrombectomy (EVT) goal time window), 81.983% (95% CI: [79.762, 84.127]) of stroke patients with LVO receive EVT, representing a 4.755% increase over the Mothership strategy and a 13.665% increase over the Nearest strategy. It is essential to emphasize that Risk-Averse accelerates treatment initiation for patients arriving hospital within 2.5 to 5.75 hours, with a mean time of 268.045 minutes (95% CI: [258.276, 278.516]). For stroke patients without LVO, within 4.5 hours, (i.e., the intravenous thrombolysis (IVT) goal time window), 80.576% (95% CI: [79.583, 81.567]) receive IVT. This rate shows the secondary outcome compared to other strategies, though it is not significantly different from them. However, this strategy may entail a risk of delayed initiation of therapy for patients whose onset time is over 3 hours due to the lower threshold set. We find that suspected stroke patients diagnosed with AIS without LVO who follow the Risk-Averse strategy must accept a potential risk of an average delay of 4.995 minutes in initiating IVT (95% CI: [-0.425, 10.414]) compared to the Pessimistic strategy, which leads to the shortest mean time. This confidence interval includes zero, signifying that this delay shows no significant difference for such patients in the long term. Another important consideration is that when the time from symptom onset to receiving definitive treatment for IVT for AIS patients exceeds 4.5 hours, patients are usually excluded as candidates for such treatment. We find that exploring this trade-off of delays in IVT might be beneficial. Finally, we have developed a website based on this optimization model for use by emergency medical technicians and to discuss with guiding physicians to explore the feasibility of implementing this strategy, establishing related protocols to be prepared for various possible scenarios.

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