Data_Sheet_1_Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates.docx

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.

研究背景与目标：在出现显性临床症状前对细菌与病毒感染实施早期预警，不仅可优化患者诊疗与预后效果，还能助力更快落实公共卫生防控措施（如患者隔离与接触者追踪）。本研究的核心目标共三项：其一，明确基于生理测量的感染早期预警检测的上限阈值；其二，探究检测到的生理应答是否具备病原体特异性；其三，探索借助可穿戴设备拓展感染早期预警检测能力的可行性。
研究方法：针对第一项研究目标，我们构建了监督式随机森林（supervised random forest）算法，用于在发热等显性症状出现前的无症状阶段检测病原体暴露情况。我们使用了暴露于埃博拉（Ebola）与马尔堡（Marburg）两种病毒的非人灵长类动物模型的高分辨率生理遥测数据，包括主动脉血压、胸内压、心电图（electrocardiogram, ECG）以及核心体温，总样本量N=20。其二，为验证算法对不同病原体的通用性，我们将算法应用于另一组暴露于三种不同病原体（拉沙病毒、尼帕病毒以及鼠疫耶尔森菌（Y. pestis））的非人灵长类动物模型的独立生理数据集，总样本量N=13。其三，为模拟无创可穿戴设备的采集数据，我们将算法限定为仅使用心电图（ECG）波形衍生的特征，并以此评估算法的性能衰减情况。
研究结果：其一，经交叉验证的随机森林分类器可实现平均51±12小时的感染早期预警，受试者工作特征曲线下面积（area under the receiver-operating characteristic curve, AUC）为0.93±0.01。其二，将算法应用于不同病原体暴露的数据集时，仍可取得相近的性能表现：平均早期预警时长为51±14小时，AUC为0.95±0.01。其三，当仅使用心电图衍生的特征集时，算法性能仅出现微小衰减：平均早期预警时长为46±14小时，AUC为0.91±0.001。
研究结论：在受控实验条件下，生理测量可实现超过2天的感染早期预警，且具备较高的AUC值。病原体暴露后引发的生理信号偏差，源于宿主自身的免疫应答，而非病原体特异性反应。即便仅使用心电图衍生特征，症状前检测仍可取得优异性能，这表明该方法可适配无创可穿戴设备的应用场景。