Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates - Davis, Shakti; Milechin, Lauren; Patel, Tejash; Hernandez, Mark; Ciccarelli, Greg; Samsi, Siddharth; Hensley, Lisa; Goff, Arthur; Trefry, John; Johnston, Sara; Purcell, Bret; Cabrera, Catherine; et al. (Frontiers in Physiology, 2021)
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, contact tracing). Our primary objectives, in this effort, are three-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±12h, 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±14h 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±14h and AUC of 0.91±0.001. Conclusions Under controlled experimental conditions, physiological measurements can provide over two 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.