Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates.
Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates.
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DOI：
10.3389/fphys.2021.691074
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发表时间：
2021
影响因子：
4
通讯作者：
Swiston A
中科院分区：
文献类型：
--
作者：
Davis S;Milechin L;Patel T;Hernandez M;Ciccarelli G;Samsi S;Hensley L;Goff A;Trefry J;Johnston S;Purcell B;Cabrera C;Fleischman J;Reuther A;Claypool K;Rossi F;Honko A;Pratt W;Swiston A
背景和目的：在出现明显的临床症状之前，对细菌和病毒感染进行早期预警，不仅可以改善患者的护理和预后，还可以更快地实施公共卫生措施(患者隔离和接触者追踪)。我们在这项努力中的主要目标有三个。首先，我们寻求通过生理测量来确定早期预警检测的上限。其次，我们调查检测到的生理反应是否是病原体所特有的。第三，我们探讨了利用可穿戴设备扩展预警检测的可行性。研究方法：对于第一个目标，我们开发了一种有监督的随机森林算法来检测在显性症状(发烧)之前的无症状时期的病原体暴露。我们使用了接触两种病毒病原体的非人灵长类动物模型的高分辨率生理遥测数据(主动脉血压、胸腔内压、心电图和核心温度)：埃博拉和马尔堡病毒(N=20)。其次，为了确定不同病原体之间的可重用性，我们在暴露于三种不同病原体的非人类灵长类动物模型(N=13)中的三个独立生理数据集上评估了我们的算法：拉萨病毒和尼帕病毒以及鼠疫杆菌。对于第三个目标，我们评估了当算法仅限于从心电(ECG)波形中提取特征以模拟来自非侵入性可穿戴设备的数据时，性能下降。结果：首先，我们的交叉验证随机森林分类器提供了51±12小时的平均预警，接收器-操作特征曲线(AUC)下的面积为0.93±0.01。其次，我们的算法在应用于不同病原体暴露的数据集时取得了类似的性能-平均预警时间为51±14小时，AUC为0.95±0.01。最后，在仅从心电中提取退化特征集的情况下，我们观察到最小退化--平均预警时间为46±14小时，AUC0.91±0.001。结论：在受控实验条件下，生理测量可提供2天以上的高AUC预警。接触病原体后生理信号的偏离是由于潜在宿主的免疫反应，而不是病原体所特有的。即使当特征仅限于心电衍生品时，症状前检测也很强，这表明这种方法可能会转化为非侵入性可穿戴设备。
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
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影响因子：
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作者：
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影响因子：
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通讯作者：
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作者：
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