Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN.
Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN.
J Vasc Surg. 2024 Jul;80(1):251-259.e3. doi: 10.1016/j.jvs.2024.02.024. Epub 2024 Feb 28.
Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events.
Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score.
Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27).
An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.
糖尿病（DM）患者患外周动脉疾病（PAD）及其并发症的风险增加。在该人群中，动脉钙化和非可压缩性可能会限制测试解释。开发能够识别 PAD 并预测 DM 患者主要不良心脏事件（MACE）和肢体事件（MALE）结局的工具将具有临床意义。使用静息多普勒动脉波形的深度神经网络分析来检测 DM 患者中的 PAD，并确定那些发生主要不良结局事件风险最大的患者。
连续纳入 2015 年 4 月 1 日至 2020 年 12 月 30 日接受下肢动脉检查的 DM 患者，随机分配至训练、验证和测试子集（60%、20%和 20%）。使用基于预测评分分布的四分位数，深度神经网络在静息胫骨后动脉多普勒波形上进行训练，以预测 5 年内的全因死亡率、MACE 和 MALE。
在总共 11384 名患者中，4211 名 DM 患者符合研究标准（平均年龄 68.6±11.9 岁；32.0%为女性）。在分配训练和验证子集后，最终测试子集包括 856 名患者。随访期间，有 262 例死亡，319 例 MACE 和 99 例 MALE。基于胫骨后动脉波形的深度神经网络分析的预测值处于较高四分位数的患者，可独立预测死亡（风险比[HR]，3.58；95%置信区间[CI]，2.31-5.56）、MACE（HR，2.06；95%CI，1.49-2.91）和 MALE（HR，13.50；95%CI，5.83-31.27）。
静息多普勒动脉波形的人工智能分析可识别 DM 患者的主要不良结局，包括全因死亡率、MACE 和 MALE。
