A Pilot Study Using Machine Learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery.
A Pilot Study Using Machine Learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery.
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使用机器学习算法和可穿戴技术早期检测心胸外科术后并发症的试点研究。
DOI：
10.1097/sla.0000000000006263
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发表时间：
2024
影响因子：
9
通讯作者：
Zhang,
中科院分区：
文献类型：
--
作者：
Beqari,Jorind;Powell,Joseph;Hurd,Jacob;Potter,AlexandraL;McCarthy,Meghan;Srinivasan,Deepti;Wang,Danny;Cranor,James;Zhang,Lizi;Webster,Kyle;Kim,Joshua;Rosenstein,Allison;Zheng,Zeyuan;Lin,TungHo;Li,Jing;Fang,Zhengyu;Zhang,
目的：评估机器学习算法（即“NightSignal”算法）是否可用于在心胸外科手术后症状发作前检测术后并发症。背景：需要能够早期检测心胸外科手术后并发症的方法。方法：这是一项前瞻性观察性队列研究，于2021年7月至2023年2月在一家学术三级护理医院进行。招募了18岁或以上计划接受心胸手术的患者。研究参与者在术前至少1周和术后90天内连续佩戴Fitbit手表。评估了NightSignal算法检测术后并发症的能力--NightSignal算法此前是为早期发现新冠肺炎而开发的。主要结果为术后事件检测的算法敏感性和特异性。结果：共56例接受心胸手术的患者符合纳入标准，其中24例（42.9%）接受胸部手术，32例（57.1%）接受心脏手术。中位年龄为62岁（四分位距：51-68），30例（53.6%）患者为女性。NightSignal算法在症状发作前中位数2天（四分位距：1-3）检测到21起术后事件中的17起，灵敏度为81%。术后事件检测算法的特异性、阴性预测值和阳性预测值分别为75%、97%和28%.Conclusions：对从可穿戴设备收集的生物特征数据进行机器学习分析，有可能在症状发作前-心胸外科手术后检测术后并发症。
Objective:To evaluate whether a machine-learning algorithm (ie, the “NightSignal” algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery.Background:Methods that enable the early detection of postoperative complications after cardiothoracic surgery are needed.Methods:This was a prospective observational cohort study conducted from July 2021 to February 2023 at a single academic tertiary care hospital. Patients aged 18 years or older scheduled to undergo cardiothoracic surgery were recruited. Study participants wore a Fitbit watch continuously for at least 1 week preoperatively and up to 90 days postoperatively. The ability of the NightSignal algorithm—which was previously developed for the early detection of Covid-19—to detect postoperative complications was evaluated. The primary outcomes were algorithm sensitivity and specificity for postoperative event detection.Results:A total of 56 patients undergoing cardiothoracic surgery met the inclusion criteria, of which 24 (42.9%) underwent thoracic operations and 32 (57.1%) underwent cardiac operations. The median age was 62 (Interquartile range: 51–68) years and 30 (53.6%) patients were female. The NightSignal algorithm detected 17 of the 21 postoperative events at a median of 2 (Interquartile range: 1–3) days before symptom onset, representing a sensitivity of 81%. The specificity, negative predictive value, and positive predictive value of the algorithm for the detection of postoperative events were 75%, 97%, and 28%, respectively.Conclusions:Machine-learning analysis of biometric data collected from wearable devices has the potential to detect postoperative complications—before symptom onset—after cardiothoracic surgery.