Traditional Vital Signs Research Articles

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model.

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients' impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss. We have previously combined feature extraction and machine learning methodologies applied to arterial waveform analysis to develop advanced metrics that have enabled the early and accurate detection of impending shock in a canine model of hemorrhage, including metrics that estimate blood loss such as the Blood Loss Volume Metric, the Percent Estimated Blood Loss metric, and the Hemorrhage Area metric. Importantly, these metrics were able to identify impending shock well before traditional vital signs, such as blood pressure, were altered enough to identify shock. Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. Based on the performance of these advanced metrics, we conclude that the framework for developing these metrics in the previous canine model remains applicable when applied to a swine model and results in accurate performance in these advanced metrics. The success of these advanced metrics in swine, which share physiological similarities to humans, shows promise in developing advanced blood loss metrics for humans, which would result in increased positive casualty outcomes due to hemorrhage in civilian and military medicine.

Effectiveness of newborn infant parasympathetic evaluation (NIPE) index for guiding intraoperative fentanyl administration in children under 2 years: a randomized controlled trial.

Assessing pain in infants and neonates is challenging due to their inability to communicate verbally. While validated subjective tools exist, they rely on interpreting the child's behavior, leading to potential inconsistencies and underestimation of pain. Based on heart rate variability, the newborn infant parasympathetic evaluation (NIPE) index offers a more objective approach to pain assessment in children under 2 years. Although promising, research on its effectiveness during surgery under general anesthesia remains limited and inconclusive. This study compared the effectiveness of NIPE-guided fentanyl administration to traditional vital signs (heart rate and mean arterial pressure) in managing pain during surgery in children under 2 years. Seventy children undergoing head, neck, or upper extremity surgery were randomized into group N (NIPE) or group C (Control) with 35 participants in each group. Both groups received standardized anesthesia and monitoring, including NIPE. Group N received fentanyl when NIPE scores fell below 50, while group C received fentanyl upon a 20% increase in heart rate or mean arterial pressure (MAP). Postoperative pain was assessed using the Face, Legs, Activity, Cry, and Consolability (FLACC) scores, alongside fentanyl consumption, sedation levels, and potential side effects. Both groups exhibited similar outcomes in terms of postoperative pain scores, fentanyl consumption, sedation levels, and absence of side effects (postoperative respiratory depression or nausea and vomiting). Additionally, intraoperative NIPE scores, heart rate, and MAP were comparable between the groups. There are strong correlations between both groups in terms of NIPE scores (r = 0.735, p < 0.0001), heart rate (r = 0.630, p < 0.0001), and MAP (r = 0.846, p < 0.0001). In both group C and group N, the NIPE scores reveal strong negative correlations with heart rate (r = -0.610, p < 0.0001, and r = -0.674, p < 0.0001) and MAP (r = -0.860, p < 0.0001, and r = -0.756, p < 0.0001). NIPE-guided intraoperative fentanyl administration was not superior to heart rate/MAP-guided administration, as both achieved similar pain management outcomes in this study. However, NIPE may offer a more practical and precise approach, as it is an objective tool with a defined threshold. These findings suggest NIPE's promise as a valuable tool for managing pain in children under 2 years undergoing surgery. However, confirmation of its widespread effectiveness requires further research with larger, multicenter studies encompassing procedures with a broader spectrum of pain severity.

The Role of Trending Shock Index Paediatric Adjusted (SIPA) in Paediatric Trauma as a Predictor of Morbidity and Mortality

Abstract Background and Purpose There are several injury scoring systems, anatomic, physiologic and combined scores, which have been used for prediction of patients' outcome such as mortality. Among physiologic scoring systems evolved shock index (heart rate divided by systolic blood pressure) which has been first described in 1967. In the world of paediatric and due to the physiological differences between different age groups, few adjustments have to be done considering these changes evolving shock index paediatric adjusted (SIPA). This study was conducted to evaluate SIPA as a quick feasible method for triaging and managing pediatric trauma and also to predict ICU admission and need for CT scan as a primary outcome and also secondary outcomes like length of hospital stay and (need for surgical intervention) and mortality rate. Study Design Prospective randomized controlled study. Setting The children’s accident and emergency department in Armed Forces Hospitals and Ain Shams University hospitals. Subjects We included 124 pediatric trauma children, and they were divided into 2 groups: Group A (62 children): assessed by SIPA in an incidence of trauma, and Group B (62 children): assessed by traditional vital signs (blood pressure, pulse and capillary refill). Methods All patients were subjected to full history taking, SIPA assessment on admission and its daily changes (baseline up to 48 hours). Assessment for need for ICU admission, ventilation, CT scan, surgical intervention, and Length of hospital stay, along with mortality rate recording. Results In the studied population, the mean age of all patients was (6.8 ± 3.2) years, with (65.3%) of patient males; and (34.7%) females. Regarding outcome data, comparative study between the 2 groups revealed; significant decrease in ICU admission, need for CT scan, LOS and mortality rate in Group A (SIPA) (p &amp;lt; 0.05). As regards SIPA trend, we found non-significant difference between mortality and no-mortality subgroups in SBP, heart rate, Capillary refill time and SIPA during the serial (baseline up to 48-h) measurements. Also, regarding ICU LOS, we found non-significant difference between mortality and no-mortality subgroups in SBP, heart rate, Capillary refill time and SIPA during the serial (baseline up to 48-h) measurements. Conclusion To conclude, SIPA score proven to be an accurate and specific predictor of morbidity, hospital stay and ICU admission, and mortality rate in pediatric trauma patients and is superior to capillary refill time, tachycardia or hypotension alone for predicting different outcomes.

Noncontact Sensors for Vital Signs Measurement: A Narrative Review.

Vital signs are crucial for monitoring changes in patient health status. This review compared the performance of noncontact sensors with traditional methods for measuring vital signs and investigated the clinical feasibility of noncontact sensors for medical use. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) database for articles published through September 30, 2023, and used the key search terms "vital sign," "monitoring," and "sensor" to identify relevant articles. We included studies that measured vital signs using traditional methods and noncontact sensors and excluded articles not written in English, case reports, reviews, and conference presentations. In total, 129 studies were identified, and eligible articles were selected based on their titles, abstracts, and full texts. Three articles were finally included in the review, and the types of noncontact sensors used in each selected study were an impulse radio ultrawideband radar, a microbend fiber-optic sensor, and a mat-type air pressure sensor. Participants included neonates in the neonatal intensive care unit, patients with sleep apnea, and patients with coronavirus disease. Their heart rate, respiratory rate, blood pressure, body temperature, and arterial oxygen saturation were measured. Studies have demonstrated that the performance of noncontact sensors is comparable to that of traditional methods of vital signs measurement. Noncontact sensors have the potential to alleviate concerns related to skin disorders associated with traditional skin-contact vital signs measurement methods, reduce the workload for healthcare providers, and enhance patient comfort. This article reviews the medical use of noncontact sensors for measuring vital signs and aimed to determine their potential clinical applicability.

Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model

Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95–1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0–15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38–0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.

Predicting mortality among patients with severe COVID-19 pneumonia based on admission vital sign indices: a retrospective cohort study

BackgroundCoronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. Vital sign indices—shock index (SI; heart rate [HR]/systolic blood pressure [SBP]), shock index age (SIA, SI × age), MinPulse (MP; maximum HR–HR), Pulse max index (PMI; HR/maximum HR), and blood pressure–age index (BPAI; SBP/age)—are better predictors of mortality in patients with trauma compared to traditional vital signs. We hypothesized that these vital sign indices may serve as predictors of mortality in patients with severe COVID-19 pneumonia. This study aimed to describe the association between vital sign indices at admission and COVID-19 pneumonia mortality and to modify the CURB-65 with the best performing vital sign index to establish a new mortality prediction tool.MethodsThis retrospective study was conducted at a tertiary care center in southern Thailand. Adult patients diagnosed with COVID-19 pneumonia were enrolled in this study between January 2020 and July 2022. Patient demographic and clinical data on admission were collected from an electronic database. The area under the receiver operating characteristic (AUC) curve analysis was used to assess the predictive power of the resultant multivariable logistic regression model after univariate and multivariate analyses of variables with identified associations with in-hospital mortality.ResultsIn total, 251 patients with COVID-19 pneumonia were enrolled in this study. The in-hospital mortality rate was 27.9%. Non-survivors had significantly higher HR, respiratory rate, SIA, and PMI and lower MP and BPAI than survivors. A cutoff value of 51 for SIA (AUC, 0.663; specificity, 80%) was used to predict mortality. When SIA was introduced as a modifier for the CURB-65 score, the new score (the CURSIA score) showed a higher AUC than the Acute Physiology and Chronic Health Evaluation II and CURB-65 scores (AUCs: 0.785, 0.780, and 0.774, respectively) without statistical significance.ConclusionsSIA and CURSIA scores were significantly associated with COVID-19 pneumonia mortality. These scores may contribute to better patient triage than traditional vital signs.

Everything About Pulse Oximetry-Part 1: History, Principles, Advantages, Limitations, Inaccuracies, Cost Analysis, the Level of Knowledge About Pulse Oximeter Among Clinicians, and Pulse Oximetry Versus Tissue Oximetry.

Purpose: Pulse oximetry is a noninvasive medical technique that measures the amount of oxygen in a person's blood by shining light through their skin. It is widely used in medical care and is considered as important as the 4 traditional vital signs. In this article, it was aimed to review all aspects of pulse oximetry in detail. Materials and Methods: The international and national reliable sources were used in the literature review for critical data analysis. A total of 13 articles including 9 reviews, 1 comparative clinical research, 1 cost-saving quality improvement project, 1 cross-sectional and multicenter descriptive study, and 1 questionnaire study were used for the preparation of this part of the review. Results: The history, principles, advantages, limitations inaccuracies, cost analysis, the level of knowledge about pulse oximeter among clinicians, and pulse oximetry versus tissue oximetry were all reviewed in detail. Conclusion: The device has a significant impact on modern medicine, allowing continuous monitoring of hemoglobin oxygen saturation in arterial blood. Oximeters are valuable in managing oxygen levels in respiratory and nonrespiratory diseases and have become an essential tool in hospital settings. Detecting low levels of oxygen saturation early can alert patients to seek medical attention promptly. It is crucial to comprehend the working and limitations of pulse oximetry technology to ensure patient safety.

A new method for vital sign detection using FMCW radar based on random body motion cancellation.

In this study, we present a new method for acquiring human vital signs using a Range-Doppler matrix (RDM) of FMCW radar data and a Gaussian interpolation algorithm (GIA). First, the RDM is derived by applying a two-dimensional fast Fourier transform (2D-FFT) to the radar data, and the GIA is applied in the Doppler dimension to estimate the target velocity signal. Subsequently, a robust enhanced trend filtering (RETF) algorithm is used to eliminate the large-scale body motion from the vital signs. Finally, the time-varying filter-based empirical mode decomposition (TVF-EMD) algorithm is employed to extract the respiratory and heartbeat intrinsic mode functions (IMFs), which are filtered according to their respective spectral power to obtain the respiratory and heartbeat frequencies. The proposed method was evaluated using vital signs data collected from seven volunteers (4 males and 3 females) with Texas Instrument's AWR1642, and the results were compared with data from a reference monitor. The experiments showed that the method had an accuracy of 93 % for respiration and 95 % for heart rate in the presence of random body movements. Unlike traditional radar-based vital signs detection methods, this approach does not rely on range bin selection of the range profile matrix (RPM), thereby avoiding phase wrap problems and producing more accurate results. Currently, research in this field is limited.

Real Time Risk Prediction of heart patients using HRV and IoT: A Survey

Abstract: In today's world, the leading cause of death is heart disease. The term heart disease is described as several different conditions that affect the heart. There are many types of heart disease, but the most common are coronary heart disease, congestive heart failure, and arrhythmia. Currently, the healthcare system is hospital essential which is inefficient to treatthese conditions, which require immediate assistance. This is a roundabout way to point to a rise in the death rates. Heart rate variability (HRV) is a useful tool for identifying and monitoring potential risk factors for heart disease. HRV measures the time between each heartbeat and can be used to identify subtle changes in heart rate that may be indicative of an underlying health condition. With the help of Machine learning and (Internet of Things) IoT-based heart patients real-time monitoring and predicting risk analysis will enable doctors to view patient's health status online. Internet of Things (IoT) technology to continuously track and monitor vital signs,including heart rate, pulse rate, and body temperature. In addition to these traditional vital signs, the system also includes afeature for monitoring heart rate variability (HRV), which has been shown to be a useful indicator of heart disease risk prediction. Using machine learning and HRV capabilities, the system will be able to assess health status and anticipate patient'sheart health (low-risk or high-risk) based on the ECG waveform and HRV characteristics such as time domain and frequency domain values

Cardio-respiratory interactions in response to lower-body negative pressure

The relationship between heart rate and blood pressure, as well as cardiorespiratory coupling, play a critical role in maintaining blood pressure and organ perfusion during conditions of blood loss. Traditional vital signs such as blood pressure, breathing rate, and oxygen saturation are poor markers of blood loss, making it difficult for medics to assess the severity of central hypovolemia. Monitoring hemorrhage is further complicated by the fact that some patients have a low tolerance to hemorrhage and would reach the point of cardiovascular collapse in less time than high tolerant individuals. Therefore, this study aimed to investigate the potential of the physiological interaction between heart rate and blood pressure, and cardiorespiratory coupling to track the progression of simulated hemorrhage, as well as distinguish individuals with low tolerance (LT) from the ones with high tolerance (HT) to hypovolemia. Nineteen subjects (age: 28 ± 6 years; height: 170 ± 7 cm; weight: 68 ± 10 kg) underwent a progressive lower body negative pressure (LBNP) protocol in which the participant was supine inside the chamber for 12 min (baseline) before 12 min of chamber decompression at –20, –30, –40, –50 and –60 mmHg followed by a 12 min recovery period. Twelve subjects reached presyncope before or during –60 mmHg LBNP stage and were considered low tolerant (LT, 12 participants), while the ones who completed –60 mmHg were considered high tolerant (HT, 7 participants). Continuous blood pressure (BP), respiration (RSP), and electrocardiogram (ECG) signals were acquired simultaneously during baseline and each LBNP stage. RR interval was calculated using ECG, while systolic blood pressure (SBP), and pulse pressure were derived from BP waveform. Wavelet transform coherence and convergent cross-mapping techniques were employed to study the physiological interdependence and the causal relationship between heart rate, blood pressure, and respiration. The interaction between blood pressure and heart rate in terms of gain, active gain, and fraction time active to maintain homeostasis was higher in the LT group during baseline, and LBNP simulated mild, moderate, and severe hemorrhage. The significant time of interaction between SBP and RSP, and the causal effect of blood pressure on respiration were higher in the HT group during baseline compared to the LT group. HT participants also had a higher causal effect of respiration on blood pressure during –30 and –40 mmHg compared to LT. Moreover, the HT group displayed a higher causal drive of respiratory-related changes in heart rate and heart rate mediated changes in respiration during severe simulated hemorrhage (–40 mmHg) compared to the LT group. The calculated metrics to distinguish between individual LT from HT subjects achieved a sensitivity of 58%–83%, an accuracy of 63%–84%, and an area under the ROC curve of 74%–86%, while the overlap of LT individual responses with HT was 0%–33%. These results indicate the potential of cardiorespiratory coupling, and heart rate and blood pressure interaction toward tracking the progression of hemorrhage and distinguishing individuals with low tolerance to hypovolemia from those with high tolerance. Measurements of such interactions could improve clinical outcomes for patients with low tolerance to hypovolemia and therefore reduce morbidity and mortality through early implementation of life-saving interventions.

Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability.

Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever. Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats. Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs. HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.

Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation

Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.

Utility of online cognitive test in cognitive neurology unit

AbstractBackgroundDelirium is one of the most common complications in hospitalized elders and corresponds with a higher risk of cognitive decline and death. Numerous test batteries exist to assist clinicians in detecting delirium, but it continues to be undiagnosed in up to 88% of patients, particularly those with AD where “confusion” is mistakenly attributed to impaired baseline (1). This pilot study investigated the utility of a digital cognitive assessment (D‐Cog), in the form of an iPad game, which would allow for rapid and routine baseline testing in patients with AD so that delirium‐induced changes can be easily detected.MethodWhile collecting traditional vital signs, medical assistants in the Beth Israel Deaconess Center (BIDMC) Cognitive Neurology Unit (CNU) prompted 67 willing patients to complete the D‐Cog assessment, an adaptive visuospatial span task, on an iPad. We then retrospectively reviewed the medical records of each patient to collect diagnosis, demographics, and pen‐and‐paper neuropsychological scores (e.g., MoCA or MMSE). Of the 67 patients (mean age 67, 29 female), 22 were clinically diagnosed with mild cognitive impairment (MCI) or varying severities of AD.ResultThe assessment was successfully completed by 52 participants. 15 participants, including 5 participants with moderate AD, experienced great difficulty during the task and were subsequently excluded. The 17 included participants with AD (mean age 75, 6 female) averaged a D‐Cog score of 4.1 and a MMSE score of 23.1. In comparison, a control group with no cognitive concerns (n = 16, mean age 60, 9 female) garnered a 6.7 average D‐Cog score and 29.3 MMSE score. The difference in D‐Cog scores was significantly different at p = 0.001.ConclusionThe D‐Cog assessment is feasible in clinical practice for patients with a wide range of cognitive impairments. These scores can act as an attentional baseline to compare against later testing in the event of suspected delirium. While the D‐Cog is derived from traditional pen‐and‐paper testing, its digital framework offers new opportunities for adaptive and remote testing.ReferenceFong TG, Davis D, Grodon ME, Albuquerque A, Inouye SK. The interface between delirium and dementia in elderly adults. Lancet Neruol. 2015; 14(8): 823–32

COMPENSATORY RESERVE INDEX: NORMATIVE VALUES OF A NEW VITAL SIGN SURROUNDING EXERCISE IN THE HEAT

Hypovolemic shock is a threat to the health and safety of active individuals when exposed to extreme environmental conditions. Non-invasive measurement of central volume loss, opposed to traditional vital signs, has been shown to be a stronger indicator of the onset of hypovolemic shock resulting from dehydration or hemorrhage. Compensatory reserve index (CRI), a scale ranging from 0-1.0, categorizes the stages of shock as a predictive measure of the remaining reserve of blood volume before compensation is diminished. Utilization of CRI in a clinical setting has been validated, while normative values surrounding exercise have yet to be established. PURPOSE: To assess normative CRI values surrounding exercise in the heat in active adults. METHODS: Data was collected on 41 active adults who completed a 11 km road race. Age (mean ± standard deviation [SD]): 44.7 ± 15.7 years; VO2max: 42.7 ± 9.2 ml/kg/min; percent body fat: 22.4 ± 9.6%. CRI was assessed at baseline for 10 minutes in a supine position in a thermoneutral environment. At the road race, CRI was assessed for 2 minutes pre-race and post-race in the supine position. Heart rate and oxygen saturation were assessed alongside CRI. Environmental conditions were captured surrounding the race. Core temperature was assessed post-race. Descriptive statistics (mean ± SD) were calculated and paired-samples t-tests were utilized to compare baseline to pre-race, baseline to post-race, and pre-race to post-race. RESULTS: Post-race CRI (mean ± SD: 0.70 ± 0.32) significantly diminished compared to baseline values (0.91 ± 0.07; p < 0.001). Post-race CRI was significantly diminished (p < 0.001) compared to pre-race CRI measures (mean ± SD: 0.88 ± 0.09). Resting heart rate increased from baseline (mean ± SD: 59.6 ± 10.4 bpm) to pre-race (65.4 ± 11.2 bpm) and to post-race (85.1 ± 16.9 bpm). Runners were characterized as hyperthermic following the race (core temperature: 38.90 ± 1.20 °C). Environmental conditions upon finishing the race were 22.78 °C, 53% RH, and 21.67 °C WBGT. CONCLUSIONS: Following physically demanding exercise in the heat, CRI monitoring may be able to detect changes resulting from increased physiological stress and may be utilized to prevent collapse. Future studies should assess the extent to which thermal stress is correlated to changes in CRI.

Continuous vital signs monitors: a replacement for traditional vital signs?

Lightweight, wireless vital signs monitors offer the ability to continuously monitor ward-based patients, which could revolutionise identification of patient deterioration. Continuous vital signs require correlation to traditional vital signs measurements in acutely unwell inpatients if we are to understand how best to integrate continuous data into track-and-trigger systems. We report data collected during Continuous Signs Monitoring in COVID-19 Patients (COSMIC-19) (clinicaltrials.gov: NCT04581031), a feasibility study of continuous vital signs data recorded using wearable sensors in hospitalised patients with COVID-19.

Compensatory reserve versus traditional vital signs in the obstetric patient

The Compensatory Reserve Index (CRI) is a new metric, estimated from pulsatile waveforms, that accurately tracks the compensatory phase of central volume loss or gain. It reports values between 1 and 0 to indicate the proportion of central volume loss the subject can tolerate before collapse, or how well the patient is compensating for acute volume loss. While studied in non-obstetrical patients, we sought to demonstrate how CRI compares to traditional vital signs in the obstetric patient at time of delivery.

RF-RES: Respiration Monitoring With COTS RFID Tags by Dopplershift

In health assessment and disease prevention, vital signs such as breathing rate, heartbeat rate, blood pressure and posturography play a critical role. Not only the elderly, but also young people need to pay attention to vital signs monitoring because more and more young people die of sudden diseases, such as sudden death and myocardial infarction. Traditional vital signs monitoring methods are invasive and inconvenient to use, especially for the elderly because these methods usually require users to wear some dedicated sensors. In recent years, some non-invasive vital signs monitoring methods based on wireless sensing have been proposed. Usually, these methods need the user to remain still. When the user walks around, these methods cannot work well. One of the challenges of mobile people breathing monitoring is that the small body chest displacement caused by breathing is disturbed by people’s walking. In this paper, we propose RF-RES, which is an accurate continuous breathing monitoring method based on RFID technology and uses a pair of tags to eliminate the influence of the user’s body movement. We merge the data of the tag pairs to eliminate the influence of the user’s entire body movement and propose an algorithm based on doppler frequency shift to track the user’s displacement during exercise. We conduct practical experiments to evaluate the performance with Impinj R420 and passive tags. The results show that RF-RES achieves accurate respiratory rate monitoring when estimating the respiratory rate of different users, with an average error of 0.54 BPM.

The Doppler shock index measured by a wearable ultrasound patch accurately detects moderate-to-severe central hypovolemia during lower body negative pressure.

ObjectiveModerate‐to‐severe hemorrhage is a life‐threatening condition, which is challenging to detect in a timely fashion using traditional vital signs because of the human body's robust physiologic compensatory mechanisms. Measuring and trending blood flow could improve diagnosis of clinically significant exsanguination. A lightweight, wireless, wearable Doppler ultrasound patch that captures and trends blood flow velocity could enhance hemorrhage detection.MethodsIn 11 healthy volunteers undergoing simulated hemorrhage and resuscitation during graded lower body negative pressure (LBNP) and release, we studied the relationship between stroke volume (SV) and common carotid artery velocity time integral (VTI) and corrected flow time (FTc). We assessed the diagnostic accuracy of 2 variations of a novel metric, the Doppler shock index (ie, the DSIVTI and DSIFTc), at capturing moderate‐to‐severe central hypovolemia defined as a 30% reduction in SV. The DSIVTI and DSIFTc are calculated as the heart rate divided by either the VTI or FTc, respectively.ResultsA total of 17,822 cardiac cycles were analyzed across 22 LBNP protocols. The average SV reduction to the lowest tolerated LBNP stage was 40%; there was no clinically significant fall in the mean arterial pressure. Correlations between changing SV and the common carotid artery VTI and FTc were strong (R 2 of 0.87, respectively) and concordant. The DSIVTI and DSIFTc accurately detected moderate‐to‐severe central hypovolemia with values for the area under the receiver operator curves of 0.96 and 0.97, respectively.ConclusionIn a human model of hemorrhage and resuscitation, measures from a wearable Doppler ultrasound patch correlated strongly with SV and identified moderate‐to‐severe central hypovolemia with excellent diagnostic accuracy.

Accessing Artificial Intelligence for Clinical Decision-Making.

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

Unifying the Estimation of Blood Volume Decompensation Status in a Porcine Model of Relative and Absolute Hypovolemia Via Wearable Sensing.

Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia to assist in triage and resuscitation. This work evaluates random forest models trained on different subsets of data from a pig model (n = 6) of absolute (bleeding) and relative (nitroglycerin-induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation. Features for the models were derived from a multi-modal set of wearable sensors, comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG) and were normalized to each subject.s baseline. The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for the best model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R2 value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an AUROC of 0.80. This study: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.