Dr Risch Medical Laboratory, Vaduz, Liechtenstein.
Central Laboratory, Canton Hospital Graubünden, Chur, Switzerland.
BMJ Open. 2022 Jun 21;12(6):e058274. doi: 10.1136/bmjopen-2021-058274.
We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.
Interim analysis of a prospective cohort study.
SETTING, PARTICIPANTS AND INTERVENTIONS: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.
A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.
Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. ISRCTN51255782; Pre-results.
我们使用可穿戴设备研究基于机器学习的新冠病毒无症状期识别，以及检测与感染相关的生理变化。
前瞻性队列研究的中期分析。
地点、参与者和干预措施：纳入列支敦士登全国队列研究的参与者。他们每晚佩戴 Ava 腕带，测量呼吸频率（RR）、心率（HR）、心率变异性（HRV）、腕部皮肤温度（WST）和皮肤灌注。通过分子和/或血清学检测诊断 SARS-CoV-2 感染。
从 1163 名参与者中记录了 150 万小时的生理数据（平均年龄 44±5.5 岁）。127 名参与者确诊为 COVID-19，其中 66 名（52%）从基线到症状出现（SO）一直佩戴设备，包括在本次分析中。多水平模型显示，与基线相比，COVID-19 的潜伏期、无症状期、症状期和恢复期，有五个（RR、HR、HRV、HRV 比和 WST）设备测量的生理参数发生显著变化。训练集代表从 SO 前第 10 天到第 2 天提取的为期 8 天的实例。训练集由 66 名参与者的 40 天测量值组成。基于随机拆分，测试集包括 30%的参与者，70%被选为训练集。基于长短期记忆（LSTM）的递归神经网络（RNN）算法开发，在训练集的召回率（敏感性）为 0.73，在测试集的召回率为 0.68，当在 SO 前 2 天检测到 COVID-19 时。
可穿戴传感器技术可实现无症状期 COVID-19 的检测。我们提出的 RNN 算法在 SO 前 2 天识别了 68%的 COVID-19 阳性参与者，将进一步在一项随机、单盲、两期、两序列交叉试验中进行培训和验证。ISRCTN51255782；预结果。
