Zhang Hanjie, Nandorine Ban Andrea, Kotanko Peter
Renal Research Institute, 315 East 62nd Street, 3rd Floor, New York, NY, 10065, USA.
Icahn School of Medicine at Mount Sinai, New York, NY, USA.
BMC Nephrol. 2025 Apr 28;26(1):214. doi: 10.1186/s12882-025-04133-z.
Maintenance hemodialysis patients experience high morbidity and mortality, primarily from cardiovascular and infectious diseases. It was discovered recently that low arterial oxygen saturation (SaO) is associated with a pro-inflammatory phenotype and poor patient outcomes. Sleep apnea is highly prevalent in maintenance hemodialysis patients and may contribute to intradialytic hypoxemia. In sleep apnea, normal respiration patterns are disrupted by episodes of apnea because of either disturbed respiratory control (i.e., central sleep apnea) or upper airway obstruction (i.e., obstructive sleep apnea). Intermittent SaO saw-tooth patterns are a hallmark of sleep apnea. Continuous intradialytic measurements of SaO provide an opportunity to follow the temporal evolution of SaO during hemodialysis. Using artificial intelligence, we aimed to automatically identify patients with repetitive episodes of intermittent SaO saw-tooth patterns.
The analysis utilized intradialytic SaO measurements by the Crit-Line device (Fresenius Medical Care, Waltham, MA). In patients with an arterio-venous fistula as vascular access, this FDA approved device records 150 SaO measurements per second in the extracorporeal blood circuit of the hemodialysis system. The average SaO of a 10-second segment is computed and streamed to the cloud. Periods comprising thirty 10-second segments (i.e., 300 s or five minutes) were independently adjudicated by two researchers for the presence or absence of SaO saw-tooth pattern. We built one-dimensional convolutional neural networks (1D-CNN), a state-of-the-art deep learning method, for SaO pattern classification and randomly assigned SaO time series segments to either a training (80%) or a test (20%) set.
We analyzed 4,075 consecutive 5-minute segments from 89 hemodialysis treatments in 22 hemodialysis patients. While 891 (21.9%) segments showed saw-tooth pattern, 3,184 (78.1%) did not. In the test data set, the rate of correct SaO pattern classification was 96% with an area under the receiver operating curve of 0.995 (95% CI: 0.993 to 0.998).
Our 1D-CNN algorithm accurately classifies SaO saw-tooth pattern. The SaO pattern classification can be performed in real time during an ongoing hemodialysis treatment, provide timely alert in the event of respiratory instability or sleep apnea, and trigger further diagnostic and therapeutic interventions.
维持性血液透析患者的发病率和死亡率较高，主要源于心血管疾病和感染性疾病。最近发现，低动脉血氧饱和度（SaO）与促炎表型及患者不良预后相关。睡眠呼吸暂停在维持性血液透析患者中非常普遍，可能导致透析期间低氧血症。在睡眠呼吸暂停中，正常呼吸模式会因呼吸控制紊乱（即中枢性睡眠呼吸暂停）或上呼吸道阻塞（即阻塞性睡眠呼吸暂停）引起的呼吸暂停发作而中断。间歇性SaO锯齿波模式是睡眠呼吸暂停的一个标志。在透析过程中持续测量SaO为观察血液透析期间SaO的时间演变提供了机会。我们旨在利用人工智能自动识别具有间歇性SaO锯齿波模式重复发作的患者。
分析使用Crit-Line设备（费森尤斯医疗保健公司，马萨诸塞州沃尔瑟姆）在透析期间测量的SaO。对于采用动静脉内瘘作为血管通路的患者，这种经美国食品药品监督管理局批准的设备每秒在血液透析系统的体外血液回路中记录150次SaO测量值。计算10秒时间段的平均SaO并传输到云端。由两名研究人员独立判定包含30个10秒时间段（即300秒或5分钟）的时间段是否存在SaO锯齿波模式。我们构建了一维卷积神经网络（1D-CNN），这是一种先进的深度学习方法，用于SaO模式分类，并将SaO时间序列段随机分配到训练集（80%）或测试集（20%）。
我们分析了22例血液透析患者89次血液透析治疗中的4075个连续5分钟时间段。其中891个（21.9%）时间段显示出锯齿波模式，3184个（78.1%）未显示。在测试数据集中，SaO模式正确分类率为96%，受试者操作特征曲线下面积为0.995（95%置信区间：0.993至0.998）。
我们的1D-CNN算法能准确分类SaO锯齿波模式。SaO模式分类可在正在进行的血液透析治疗期间实时进行，在呼吸不稳定或出现睡眠呼吸暂停时及时发出警报，并触发进一步的诊断和治疗干预。
