McBane Robert D, Murphree Dennis H, Liedl David, Lopez-Jimenez Francisco, Attia Itzhak Zachi, Arruda-Olson Adelaide M, Scott Christopher G, Prodduturi Naresh, Nowakowski Steve E, Rooke Thom W, Casanegra Ana I, Wysokinski Waldemar E, Houghton Damon E, Bjarnason Haraldur, Wennberg Paul W
Gonda Vascular Center Mayo Clinic Rochester MN.
Cardiovascular Department Mayo Clinic Rochester MN.
J Am Heart Assoc. 2024 Feb 6;13(3):e031880. doi: 10.1161/JAHA.123.031880. Epub 2024 Jan 19.
Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events.
Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years.
An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.
外周动脉疾病患者发生主要不良心脏事件、主要不良肢体事件和全因死亡的风险增加。开发能够识别外周动脉疾病中发生主要不良事件风险最高的患者的工具是预防不良结局的第一步。本研究旨在确定使用深度神经网络对静息多普勒波形进行计算机辅助分析是否能够准确识别外周动脉疾病中发生不良结局事件风险最高的患者。
纳入2015年4月1日至2020年12月31日期间连续接受踝臂指数检测的患者。患者被随机分配到训练、验证和测试子集（60%/20%/20%）。基于静息胫后动脉多普勒波形训练深度神经网络，以预测5年时的主要不良心脏事件、主要不良肢体事件和全因死亡。然后根据训练集中每个预测分数的四分位数对患者进行分组分析。在总共11384例患者中，10437例患者符合研究纳入标准（平均年龄65.8±14.8岁；40.6%为女性）。测试子集包括2084例患者。在5年的随访期间，有447例死亡、585例主要不良心脏事件和161例主要不良肢体事件。在调整年龄、性别和Charlson合并症指数后，胫后动脉波形的深度神经网络分析提供了5年时死亡（风险比[HR]，2.44[95%CI，1.78 - 3.34]）、主要不良心脏事件（HR，1.97[95%CI，1.49 - 2.61]）和主要不良肢体事件（HR，11.03[95%CI，5.43 - 22.39]）的独立预测。
基于人工智能的多普勒动脉波形分析能够识别外周动脉疾病患者中的主要不良结局，这可能促进早期采用并坚持风险因素修正。
