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Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins., Competing Interests: Conflict of Interest Statement The authors assert that there are no conflicts of interest that might be construed as exerting an influence on the content or interpretation of this work. This includes financial, personal, professional, or any other affiliations that could potentially introduce bias to the research or conclusions presented in this article. Complete disclosure of all financial support for this research and the manuscript’s development is provided within the Acknowledgments section.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model ( n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R 2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10 -3 |0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R 2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10 -3 , RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time (T e )., In a sample of 15 healthy human participants undergoing a protocol of five controlled breathing exercises in four postures each, electrocardiogram (ECG) and RSP signals were collected. RSP signals were processed to extract breath-by-breath estimates of RR, T i , and T e . These estimates were compared against ground truth spirometry-based estimates using Bland-Altman analysis. We find that incorporating quality indexing and outlier removal prior to feature extraction improves the 95% limits of agreement by 10-40%. We also find that by using ECG-derived respiration (EDR) during periods of RSP artifact, the data removal necessary for accurate respiratory timing estimation is significantly reduced ( for all postures). These findings encourage the use of quality assessment and EDR to enhance the robustness of RR, T i , and T e estimation from RSP signals. Clinical Relevance- Detecting stimulus-induced or pathological changes in respiratory function can enhance our understanding and monitoring of respiratory health. Quality assessment and the use of EDR help accomplish this by enabling more accurate measurement of respiratory timings.

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.

Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.

Objective: Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection., Methods: The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG., Results: SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET-a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage., Conclusion: These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation., Significance: Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings.

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

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 R 2 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.

A veterinarian and pet owner survey (Project Jake) examined the use and safety of isoxazoline parasiticides given to dogs. Data were received during August 1-31, 2018 from a total of 2,751 survey responses. Forty-two percent (1,157) reported no flea treatment or adverse events (AE), while 58% (1594) had been treated with some parasiticide for flea control, and of those that received a parasiticide, the majority, or 83% (1,325), received an isooxazoline. When any flea treatment was given, AE were reported for 66.6% of respondents, with no apparent AE noted for 36.1%. Project Jake findings were compared to a retrospective analysis of publicly available Food and Drug Administration (FDA) and European Medicines Agency (EMA) reported AE. The number of total AE reported to FDA and EMA were comparable, although a 7 to 10 times higher occurrence of death and seizures was reported from the EMA or from outside the United States (US). Serious AE responses for death, seizures and neurological effects reported in our survey were higher than the FDA but moderately lower than the EMA reports. These sizable global data sets combined with this pre- and post-parasiticide administration survey indicated that isoxazoline neurotoxicity was not flea- and tick-specific. Post-marketing serious AE were much higher than in Investigational New Drug (IND) submissions. Although the labels have recently been updated, dogs, cats and their caregivers remain impacted by their use. These aggregate data reports support the need for continued cross-species studies and critical review of product labelling by regulatory agencies and manufacturers., (© 2020 The Authors. Veterinary Medicine and Science Published by John Wiley & Sons Ltd.)

Purpose: Nonunion of the distal humerus after fracture or osteotomy is uncommon but can present an important clinical problem. Vascular insufficiency of bone may be an important contributing factor. The purpose of this study was to describe the intraosseous vascular anatomy of the distal humerus., Methods: Nine fresh-frozen upper extremity cadaver specimens were injected with India ink and Ward's blue latex solution. A modified Spalteholz technique was used to show the intraosseous vascular anatomy., Results: The distal humeral diaphysis was supplied by a single nutrient artery. The lateral column was supplied predominately by posterior segmental vessels, whereas the medial column was supplied by anterior and posterior segmental vessels. The trochlea, olecranon fossa, and coronoid fossa were watershed areas., Conclusions: Our study shows the intraosseous vascular anatomy of the distal humerus is relatively consistent. There also appears to be a consistent trochlear vessel medially and small anterior perforating vessels in the lateral column that have not previously been described.

Since several lines of evidence implicate the 3'-flanking region in regulating alpha1(I) collagen gene transcription, we analyzed 12. 4-kilobase pairs of 3'-flanking sequence of the murine alpha1(I) collagen gene for transcriptional elements. A region of the 3'-flanking region stimulated expression of the heterologous beta-globin gene promoter in an enhancer trap plasmid and of the alpha1(I) collagen gene promoter in a collagen-luciferase reporter gene construct when located 3' to the luciferase reporter gene. DNase I footprinting analysis demonstrated the presence of three regions where DNA binding proteins specifically interact within this 3'-stimulatory region. Inspection of the DNA sequence revealed a consensus E-box, a binding site for basic helix-loop-helix proteins, in one of the protein binding sites. Mobility shift assays demonstrated that upstream stimulatory factors (USF) USF-1 and USF-2 bind to this E-box. Mutating the E-box in the context of the 3'-flanking region confirmed that it contributes to the enhancement of transcriptional activity of the alpha1(I) collagen gene promoter. Mutations in all three protein binding sites abolished transcriptional activation by the 3'-flanking region, suggesting a complex interaction among the trans-acting factors in enhancing transcriptional activity. Thus, a region of the 3'-flanking region of the alpha1(I) collagen gene stimulates transcription of the alpha1(I) collagen gene promoter, and USF-1 and USF-2 contribute to this transcriptional stimulation.

Tepoxalin [5- (4-chlorophenyl)-N-hydroxy-1-(4-methoxyphenyl)-N-methyl-1H-pyrazole -3-propanamide] is an orally active anti-inflammatory agent, which inhibits both cyclooxygenase and 5-lipoxygenase activities. The oral toxicity of tepoxalin was evaluated in 1- and 6-month rat (up to 50 mg/kg/day) and dog (up to 150 mg/kg bid) studies. In rats, increased liver weight, centrilobular hypertrophy, and hepatic necrosis were observed at dosages >/=20 mg/kg/day. Renal changes indicative of analgesic nephropathy syndrome (i.e., papillary edema or necrosis, cortical tubular dilatation) were seen at >/=15 mg/kg. In rats treated for 1 month, these hepatic and renal effects were largely reversible after a 1-month recovery period. Gastrointestinal erosions and ulcers were seen in female rats given 40 mg/kg/day for 6 months. Changes in clinical pathology parameters included decreases in red blood cell count, hemoglobin, and hematocrit mean values; elevation in platelet counts; and an increase in prothrombin and activated partial thromboplastin times. Mild increases in alanine aminotransferase, aspartate aminotransferase, and cholesterol were also noted in rats. Decreased erythrocyte parameters, increased leukocyte counts, and decreased total protein, albumin, and/or calcium were noted in some dogs in the 300 mg/kg/day group following 6 months of dosing. Small pyloric ulcerations were seen at 100 and 300 mg/kg/day dosages for up to 6 months. In both rats and dogs, no accumulation of tepoxalin or its carboxylic acid metabolite was detected in plasma following multiple dosing over a range of 5 to 50 mg/kg/day for rats and 20 to 300 mg/kg/day for dogs. Plasma concentrations of the carboxylic acid metabolite were severalfold higher than those of the parent compound. The no-effect dosages in rats (5 mg/kg/day) and dogs (20 mg/kg/day) were approximately one and six times the ED50 (3.5 mg/kg), respectively, for inhibition of inflammatory effects in the adjuvant arthritic rat without gastric mucosal damage. In terms of severity, the relative lack of gastrointestinal side effects, within the estimated therapeutic dose range, distinguishes tepoxalin from most marketed anti-inflammatory drugs.

Ipazilide fumarate (Win 54, 177-4) is a chemically novel antiarrhythmic agent that prolongs ventricular refractoriness and possesses antiectopic activity. Subchronic (29 days) nonclinical safety evaluation of ipazilide was conducted following oral and iv administration in Sprague-Dawley rats (20-320 mg/kg oral and 1.25-10 mg/kg iv) and 14 and 28 days in beagle dogs (3-30 mg/kg oral and 2.5-20 mg/kg iv). The pharmacokinetic parameters of ipazilide indicate that ipazilide is absorbed (tmax less than or equal to 1 hr) in fasted rats and dogs following single and repeated oral administrations. The apparent elimination half-life in the two species is approximately 1 hr (except in rats at a dosage of 320 mg/kg), suggesting rapid clearance. Increases in liver weights (rats 320 mg/kg) accompanied by the observation of centrilobular hypertrophy of hepatocytes were considered an expression of an adaptive metabolic response of the liver to ipazilide and may be associated with the induction of microsomal enzymes. Duodenal villous atrophy and epithelial hyperplasia (rats, 80 and 320 mg/kg) were interpreted to represent an irritant response to the drug. Local irritation was also observed at the injection site in rats and dogs. Dogs tolerated the oral and the iv administration of ipazilide at dosages of up to 30 and 20 mg/kg, respectively. Despite emesis (oral dogs), which was reduced in frequency following repeated treatment over several weeks, plasma levels in treated dogs (i.e., Cmax 4-5 micrograms/ml) were approximately twice that required to convert spontaneous arrhythmias in the conscious dog model 24 hr after myocardial infarction.(ABSTRACT TRUNCATED AT 250 WORDS)

The effects of ionic and nonionic radiographic contrast media on human blood in plastic syringes were investigated in an in vitro double-blind study. Venous blood, which was drawn into plastic syringes containing one of four (iohexol, iopamidol, diatrizoate sodium meglumine, and ioxaglate sodium meglumine) contrast media, was visually inspected at predetermined time intervals before and after mixing. Aliquots of the mixtures of blood and contrast media also were evaluated microscopically. Irregular red blood cell (RBC) aggregates were observed with the nonionic contrast media. Pronounced RBC morphologic alterations occurred with diatrizoate (ionic), and marked crenation was observed with ioxaglate (ionic). Observed aggregates were freely disaggregated in isotonic saline. Recovered supernatant blood from centrifuged aliquots of the mixtures was evaluated for clot formation. There was no evidence of blood clot formation within 1 hour after blood was introduced into syringes containing either ionic or nonionic contrast media. This time period exceeded the normal clotting time, since blood in syringes without contrast media formed clots within 30 to 45 minutes. Both the nonionic and ionic contrast media prolonged coagulation.

During a toxicology study in cynomolgus (long-tailed or crab-eating) monkeys (Macaca fascicularis), a randomly distributed incidence of significantly increased hepatic enzyme activity was observed. Premedication hepatic enzyme activity in all monkeys of this study was normal, but increased alanine aminotransferase (ALT) activity was found in 4 of the 24 animals 2 weeks after initiation of the study and in 10 of 24 at 4 weeks. A drug-related effect was considered unlikely initially because the increases were not doserelated, and a 3-year review of 655 cynomolgus monkeys revealed a 15-20% incidence of increased hepatic enzyme activity. Good correlation was subsequently established between increased hepatic enzyme activity, active hepatitis A virus (HAV) infection, and histomorphologic confirmation of hepatitis (chronic periportal inflammation). Follow-up viral serodiagnostic screening of resident macaques revealed an overall incidence of anti-HAV IgG in 80% (155/193) of cynomolgus and in 70% (14/20) of rhesus monkeys. Serial screening demonstrated that several initially negative monkeys became seropositive for anti-HAV IgG, and a few acquired active infection (anti-HAV IgM). Among newly acquired cynomolgus monkeys, 2.5% (2/80) had an acute HAV infection, and 35% (28/80) eventually tested positive for anti-HAV IgG while quarantined in the primate facility. The characterization of an enzootic HAV infection in incoming monkeys posed a significant risk for the primate colony and handlers. Rigorous sanitation, isolation, and quarantine procedures, including personnel training and additional protective clothing for personnel working in the primate colony, reduced tho potential for transmission and arrested the outbreak. Experimenters should be cautious in ascribing toxicity to a test article based solely on increased hepatic enzyme activity associated with chronic periportal inflammation., (Copyright © 1988 Wiley-Liss, Inc., A Wiley Company.)

A non-ionic diagnostic medium, iohexol, was administered by subarachnoid injection to groups of six cynomolgus monkeys and compared with the vehicle, physiologically normal saline, and/or saline of equal osmolality to determine its potential for increasing total protein and leucocyte levels in cerebrospinal fluid. Also investigated was the effect of repeated spinal taps not subsequently followed by the intrathecal injection of test or control articles. In the monkey, unlike man, low-level leucocyte counts were consistently observed following initial withdrawal of spinal fluid. Elevated leucocyte and total protein levels were observed in the present investigations one day to a week after intrathecal injection of radiopaque, vehicle or saline solution. Total protein returned to normal levels earlier than did leucocyte counts. However, repeated needle puncture alone was found to be sufficient to cause an elevation of leucocytes 3 to 4 times the baseline level, while inflammatory effects were observed histologically only when autopsy was performed soon after the final spinal tap.