Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.

COVID-19 exerts deleterious cardiopulmonary effects, leading to a worse prognosis in the most affected. This retrospective multi-center observational cohort study aimed to analyze the trajectories of key vitals amongst hospitalized COVID-19 patients using a chest-patch wearable providing continuous remote patient monitoring of numerous vital signs. The study was conducted in five COVID-19 isolation units. A total of 492 COVID-19 patients were included in the final analysis. Physiological parameters were measured every 15 min. More than 3 million measurements were collected including heart rate, systolic and diastolic blood pressure, cardiac output, cardiac index, systemic vascular resistance, respiratory rate, blood oxygen saturation, and body temperature. Cardiovascular deterioration appeared early after admission and in parallel with changes in the respiratory parameters, showing a significant difference in trajectories within sub-populations at high risk. Early detection of cardiovascular deterioration of COVID-19 patients is achievable when using frequent remote patient monitoring.

Intradialytic hypotension (IDH) is a severe complication of hemodialysis (HD) with a significant impact on morbidity and mortality. In this study, we used a wearable device for the continuous monitoring of hemodynamic vitals to detect hemodynamic changes during HD and attempted to identify IDH. End-stage kidney disease patients were continuously monitored 15 min before starting the session and until 15 min after completion of the session, measuring heart rate (HR), noninvasive cuffless systolic and diastolic blood pressure (SBP and DBP), stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR). Data were analyzed retrospectively and included comparing BP measured by the wearable devices (recorded continuously every 5 s) and the cuff-based devices. A total of 98 dialysis sessions were included in the final analysis, and IDH was identified in 22 sessions (22.5%). Both SBP and DBP were highly correlated (r > 0.62, p < 0.001 for all) between the wearable device and the cuff-based measurements. Based on the continuous monitoring, patients with IDH had earlier and more profound reductions in SBP and DBP during the HD treatment. In addition, nearly all of the advanced vitals differed between groups. Further studies should be conducted in order to fully understand the potential of noninvasive advanced continuous monitoring in the prediction and prevention of IDH events.

Tourniquet application is an urgent life-saving procedure. Previous studies demonstrated several drawbacks in tourniquet design and application methods that limit their efficacy; among them, loose application of the device before windlass twisting is a main pitfall. A new generation of modern combat tourniquets was developed to overcome these pitfalls. The objective of this study was to assess the effectiveness of three new tourniquet designs: the CAT Generation 7 (CAT7), the SAM Extremity Tourniquet (SAM-XT), and the SOF Tactical Tourniquet Wide (SOFTT-W) as well as its correlation to the degree of slack. The three tourniquet models were applied in a randomized sequence on a HapMed leg tourniquet trainer, simulating an above-the-knee traumatic amputation by 60 military medicine track cadets. Applied pressure, hemorrhage control status, time until the bleeding stopped, estimated blood volume loss, and slack were measured. The mean (±SD) pressure applied using the SAM-XT (186 mmHg ±63) or the CAT7 (175 mmHg ±79) was significantly higher compared to the pressure applied by the SOFTT-W (104 mmHg ±101, P < 0.017), with no significant difference between the first two (P > 0.05). Hemorrhage control rate was similar (P > 0.05) with SAM-XT (73.3%) and CAT7 (67.7%), and both were significantly better than the SOFTT-W (35%, P < 0.017). Slack was similar between CAT7 and SAM-XT (5.2 mm ± 3.4 vs. 5 mm ± 3.5, P > 0.05), yet significantly lower compared to the SOFTT-W (9 mm ± 5, P < 0.017). A strong negative correlation was found between slack and hemorrhage control rate (3.2 mm ± 1.5 mm in success vs. 10.5 mm ± 3.4 mm in failure, P < 0.001) and applied pressure (Pearson’s correlation coefficient of −0.83, P < 0.001). Both SAM-XT and CAT7 demonstrated a better pressure profile and hemorrhage control rate compared to SOFTT-W, with no significant difference between the two. The better outcome measures were strongly correlated to less slack.

There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their responses. In this study, 29 HF patients were monitored during outpatient intravenous diuresis, using a noninvasive wearable multi-parameter monitor. Analysis of changes in these parameters during the course of diuresis aimed to recognize subgroups with different response patterns. Parameters did not change significantly, however, subgroup analysis of the last quartile of treatment showed significant differences in cardiac output, cardiac index, stroke volume, pulse rate, and systemic vascular resistance according to gender, and in systolic blood pressure according to habitus. Changes in the last quartile could be differentiated using k-means, a technique of unsupervised machine learning. Moreover, patients’ responses could be best clustered into four groups. Analysis of baseline parameters showed that two of the clusters differed by baseline parameters, body mass index, and diabetes status. To conclude, we show that physiological changes during diuresis in HF patients can be categorized into subgroups sharing similar response trends, making noninvasive monitoring a potential key to personalized treatment in HF.