Kotovich Dmitry, Twig Gilad, Itsekson-Hayosh Zeev, Klug Maximiliano, Simon Asaf Ben, Yaniv Gal, Konen Eli, Tau Noam, Raskin Daniel, Chang Paul J, Orion David
The Institute for Research in Military Medicine, The Faculty of Medicine, The Hebrew University of Jerusalem, Tel Aviv, Israel.
The IDF Medical Corps, 9112102, Tel Aviv, Israel.
Int J Emerg Med. 2023 Aug 11;16(1):50. doi: 10.1186/s12245-023-00523-y.
To assess the effect of a commercial artificial intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single level 1 trauma center.
A retrospective cohort study for two time periods-pre-AI (1.1.2017-1.1.2018) and post-AI (1.1.2019-1.1.2020)-in a level 1 trauma center was performed. The ICH algorithm was applied to 587 consecutive patients with a confirmed diagnosis of ICH on head CT upon admission to the emergency department. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke (IS) and myocardial infarction (MI)) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. The secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge.
Five hundred eighty-seven participants (289 pre-AI-age 71 ± 1, 169 men; 298 post-AI-age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH, and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality were significantly reduced in the post-AI group when compared to the pre-AI group (27.7% vs 17.5%; p = 0.004 and 31.8% vs 21.7%; p = 0.017, respectively). Modified Rankin Scale (mRS) at discharge was significantly reduced post-AI implementation (3.2 vs 2.8; p = 0.044).
The added value of this study emphasizes the introduction of artificial intelligence (AI) computer-aided triage and prioritization software in an emergent care setting that demonstrated a significant reduction in a 30- and 120-day all-cause mortality and morbidity for patients diagnosed with intracranial hemorrhage (ICH). Along with mortality rates, the AI software was associated with a significant reduction in the Modified Ranking Scale (mRs).
评估在一家一级创伤中心的急诊科实施商用人工智能(AI)解决方案对临床结局的影响。
在一家一级创伤中心进行了一项回顾性队列研究,研究两个时间段——人工智能应用前(2017年1月1日至2018年1月1日)和人工智能应用后(2019年1月1日至2020年1月1日)。将颅内出血(ICH)算法应用于急诊科收治的587例入院时头部CT确诊为ICH的连续患者。研究变量包括人口统计学数据、患者结局和影像数据。在同一时间段因其他急性诊断(缺血性卒中(IS)和心肌梗死(MI))入住急诊科的患者作为对照组。主要结局是30天和120天的全因死亡率。次要结局是出院时基于改良Rankin神经功能残疾量表(mRS)的发病率。
587例ICH患者(289例人工智能应用前——年龄71±1岁,169例男性;298例人工智能应用后——年龄69±1岁,187例男性)符合分析时间段要求。两个时间段之间的人口统计学数据、合并症、急诊严重程度评分、ICH类型和住院时间无显著差异。与人工智能应用前组相比,人工智能应用后组30天和120天的全因死亡率显著降低(分别为27.7%对17.5%;p = 0.004和31.8%对21.7%;p = 0.017)。人工智能应用后出院时的改良Rankin量表(mRS)显著降低(3.2对2.8;p = 0.044)。
本研究的附加价值强调了在紧急护理环境中引入人工智能(AI)计算机辅助分诊和优先排序软件,该软件可显著降低诊断为颅内出血(ICH)患者的30天和120天全因死亡率及发病率。除死亡率外,人工智能软件还与改良Rankin量表(mRs)的显著降低相关。