Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.
Cardiovascular Department, Mayo Clinic, Rochester, MN, USA.
Vasc Med. 2022 Aug;27(4):333-342. doi: 10.1177/1358863X221094082. Epub 2022 May 10.
Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD.
Consecutive patients (4/8/2015 - 12/31/2020) undergoing rest and postexercise ankle-brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 - 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance.
Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92-0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91-0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison).
An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.
外周动脉疾病（PAD）患者发生主要肢体不良事件和心脏事件（包括死亡）的风险增加。开发能够准确识别 PAD 的筛查工具是预防不良结局策略的必要第一步。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机器分析是否能准确识别 PAD 患者。
连续纳入（2015 年 4 月 8 日-2020 年 12 月 31 日）接受静息和运动后踝臂指数（ABI）检查的患者。患者被随机分配到训练、验证和测试子集（70%/15%/15%）。使用静息和运动后 ABI 对静息后胫后动脉多普勒波形进行训练，以预测正常（>0.9）或 PAD（⩽0.9）。在模型创建和验证后（2021 年 1 月 1 日-3 月 31 日）使用一个包含 151 例患者的独立数据集进行二次验证。构建受试者工作特征曲线（AUC）来评估测试性能。
在 11748 例患者中，有 3432 例患者符合研究标准：1941 例 PAD（平均年龄 69 ± 12 岁）和 1491 例无 PAD（64 ± 14 岁）。性能最高的预测模型识别 PAD 的 AUC 为 0.94（CI = 0.92-0.96），灵敏度为 0.83，特异性为 0.88，准确性为 0.85，阳性预测值（PPV）为 0.90。验证数据集的结果相似：AUC 为 0.94（CI = 0.91-0.98），灵敏度为 0.91，特异性为 0.85，准确性为 0.89，PPV 为 0.89（运动后 ABI 比较）。
基于静息多普勒动脉波形的人工智能分析可在具有临床意义的水平上识别 PAD。
