PDF(Pubmed)
Abstract：
BACKGROUND: Point-of-care ultrasound has become a universal practice, employed by physicians across various disciplines, contributing to diagnostic processes and decision-making.
OBJECTIVE: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes.
METHODS: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020.
RESULTS: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure.
CONCLUSIONS: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
OBJECTIVE: To assess the association of reduced (<50%) left-ventricular ejection fraction (LVEF) based on prospective point-of-care ultrasound operated by medical students using an artificial intelligence (AI) tool and 1-year primary composite outcome, including mortality and readmission for cardiovascular-related causes.
METHODS: Eight trained medical students used a hand-held ultrasound device (HUD) equipped with an AI-based tool for automatic evaluation of the LVEF of non-selected patients hospitalized in a cardiology department from March 2019 through March 2020.
RESULTS: The study included 82 patients (72 males aged 58.5 ± 16.8 years), of whom 34 (41.5%) were diagnosed with AI-based reduced LVEF. The rates of the composite outcome were higher among patients with reduced systolic function compared to those with preserved LVEF (41.2% vs. 16.7%, p = 0.014). Adjusting for pertinent variables, reduced LVEF independently predicted the composite outcome (HR 2.717, 95% CI 1.083-6.817, p = 0.033). As compared to those with LVEF ≥ 50%, patients with reduced LVEF had a longer length of stay and higher rates of the secondary composite outcome, including in-hospital death, advanced ventilatory support, shock, and acute decompensated heart failure.
CONCLUSIONS: AI-based assessment of reduced systolic function in the hands of medical students, independently predicted 1-year mortality and cardiovascular-related readmission and was associated with unfavorable in-hospital outcomes. AI utilization by novice users may be an important tool for risk stratification for hospitalized patients.
摘要：
背景：定点护理超声已成为一种普遍的做法，受雇于不同学科的医生，有助于诊断过程和决策。
目的：根据医学生使用人工智能（AI）工具操作的前瞻性护理点超声和1年主要综合结局，评估左心室射血分数（LVEF）降低（<50％）的相关性。包括死亡率和心血管相关原因的再入院。
方法：从2019年3月至2020年3月，八名受过训练的医学生使用配备有基于AI的工具的手持式超声设备（HUD）自动评估心内科住院的非选择患者的LVEF。
结果：该研究包括82名患者（72名男性，年龄58.5±16.8岁），其中34人(41.5%)被诊断为基于AI的LVEF降低。与LVEF保留的患者相比，收缩功能降低的患者的复合结局发生率更高（41.2％vs.16.7%,p=0.014)。调整相关变量，LVEF降低独立预测复合结局(HR2.717,95%CI1.083-6.817,p=0.033)。与LVEF≥50%的患者相比，LVEF降低的患者住院时间更长,次要复合结局发生率更高,包括住院死亡,先进的通气支持，震惊,和急性失代偿性心力衰竭.
结论：基于AI的医学生手收缩功能降低评估，独立预测1年死亡率和心血管相关再入院率,并与不良住院结局相关.新手用户使用AI可能是住院患者风险分层的重要工具。
目的：根据医学生使用人工智能（AI）工具操作的前瞻性护理点超声和1年主要综合结局，评估左心室射血分数（LVEF）降低（<50％）的相关性。包括死亡率和心血管相关原因的再入院。
方法：从2019年3月至2020年3月，八名受过训练的医学生使用配备有基于AI的工具的手持式超声设备（HUD）自动评估心内科住院的非选择患者的LVEF。
结果：该研究包括82名患者（72名男性，年龄58.5±16.8岁），其中34人(41.5%)被诊断为基于AI的LVEF降低。与LVEF保留的患者相比，收缩功能降低的患者的复合结局发生率更高（41.2％vs.16.7%,p=0.014)。调整相关变量，LVEF降低独立预测复合结局(HR2.717,95%CI1.083-6.817,p=0.033)。与LVEF≥50%的患者相比，LVEF降低的患者住院时间更长,次要复合结局发生率更高,包括住院死亡,先进的通气支持，震惊,和急性失代偿性心力衰竭.
结论：基于AI的医学生手收缩功能降低评估，独立预测1年死亡率和心血管相关再入院率,并与不良住院结局相关.新手用户使用AI可能是住院患者风险分层的重要工具。
