Abstract：
BACKGROUND: A key decision facing nonthrombectomy capable (spoke) hospitals is whether to transfer a suspected large vessel occlusion (LVO) patient to a comprehensive stroke center (CSC). In a retrospective cohort study, we investigated the rate of transfers resulting in endovascular thrombectomy (EVT) and associated costs before and after implementation of an artificial intelligence (AI)-based software.
METHODS: All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.
RESULTS: A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).
CONCLUSIONS: The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.
METHODS: All patients with a final diagnosis of acute ischemic stroke presenting across a five-spoke community hospital network in affiliation with a CSC were included. The Viz LVO (Viz.ai, Inc.) software was implemented across the spokes with image sharing and messaging between providers across sites. In a cohort of patients before (pre-AI, December 2018-October 2020) and after (post-AI, October 2020-August 2022) implementation, we compared the EVT rate among ischemic stroke patients transferred out of our health system to the CSC. Secondary outcomes included the EVT rate based on spoke computed tomography angiography (CTA) and estimated transfer costs.
RESULTS: A total of 3113 consecutive eligible patients (mean age 71 years, 50% female) presented to the spoke hospitals with 162 transfers pre-AI and 127 post-AI. The rate of transfers treated with EVT significantly increased (32.1% pre-AI vs. 45.7% post-AI, p = 0.02). There was a sharp increase in CTA use post-AI at the spoke hospitals for all patients and transfers that likely contributed to the increased EVT transfer rate, but prior spoke CTA use alone was not sufficient to account for all improvement in EVT transfer rate (37.2% pre-AI vs. 49.2% post-AI, p = 0.12). In a binary logistic regression model, the odds of an EVT transfer in the intervention period were 1.85 greater as compared to preintervention (adjusted odds ratio 1.85, 95% confidence interval 1.12-3.06). The decrease in non-EVT transfers resulted in an estimated annual benefit of $206,121 in spoke revenue and $119,921 in payor savings (all US dollars).
CONCLUSIONS: The implementation of an automated image interpretation and communication platform was associated with increased CTA use, more transfers treated with EVT, and potential economic benefits.
摘要：
背景：非血栓切除术（spoke）医院面临的关键决定是是否将疑似大血管闭塞（LVO）患者转移到综合卒中中心（CSC）。在一项回顾性队列研究中，我们调查了在实施基于人工智能(AI)的软件前后导致血管内血栓切除术(EVT)的转移率和相关成本.
方法：纳入了所有最终诊断为急性缺血性卒中的患者，这些患者通过与CSC相关的五分支社区医院网络出现。VizLVO（Viz。ai,Inc.）的软件是在辐条上实现的，具有跨站点提供商之间的图像共享和消息传递。在之前的一组患者中(AI前，2018年12月至2020年10月)及之后(人工智能后，2020年10月-2022年8月）实施，我们比较了从卫生系统转移到CSC的缺血性卒中患者的EVT率.次要结果包括基于轮辐计算机断层扫描血管造影（CTA）的EVT率和估计的转移成本。
结果：共有3113名连续合格患者（平均年龄71岁，50％的女性）向口语医院介绍了162个AI前转移和127个AI后转移。用EVT治疗的转移率显着增加（AI前32.1%vs.45.7%后人工智能，p=0.02)。在所有患者的口语医院中，CTA在AI后的使用急剧增加，并且转移可能导致EVT转移率增加，但以前的辐条CTA单独使用不足以说明EVT传输速率的所有改善(37.2%的前AI与49.2%后人工智能，p=0.12）。在二元逻辑回归模型中，与干预前相比,干预期发生EVT转移的几率为1.85(调整后比值比1.85,95%置信区间1.12~3.06).非EVT转移的减少导致轮辐收入的估计年度收益为206,121美元，付款人节省了119,921美元（均为美元）。
结论：自动图像解释和通信平台的实施与CTA使用的增加有关，用EVT治疗更多的转移，和潜在的经济效益。
方法：纳入了所有最终诊断为急性缺血性卒中的患者，这些患者通过与CSC相关的五分支社区医院网络出现。VizLVO（Viz。ai,Inc.）的软件是在辐条上实现的，具有跨站点提供商之间的图像共享和消息传递。在之前的一组患者中(AI前，2018年12月至2020年10月)及之后(人工智能后，2020年10月-2022年8月）实施，我们比较了从卫生系统转移到CSC的缺血性卒中患者的EVT率.次要结果包括基于轮辐计算机断层扫描血管造影（CTA）的EVT率和估计的转移成本。
结果：共有3113名连续合格患者（平均年龄71岁，50％的女性）向口语医院介绍了162个AI前转移和127个AI后转移。用EVT治疗的转移率显着增加（AI前32.1%vs.45.7%后人工智能，p=0.02)。在所有患者的口语医院中，CTA在AI后的使用急剧增加，并且转移可能导致EVT转移率增加，但以前的辐条CTA单独使用不足以说明EVT传输速率的所有改善(37.2%的前AI与49.2%后人工智能，p=0.12）。在二元逻辑回归模型中，与干预前相比,干预期发生EVT转移的几率为1.85(调整后比值比1.85,95%置信区间1.12~3.06).非EVT转移的减少导致轮辐收入的估计年度收益为206,121美元，付款人节省了119,921美元（均为美元）。
结论：自动图像解释和通信平台的实施与CTA使用的增加有关，用EVT治疗更多的转移，和潜在的经济效益。
