A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

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: R2 = 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: R2 = 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.

Noninvasive Vagal Nerve Stimulation for Opioid Use Disorder.

Background: Opioid Use Disorder (OUD) is an escalating public health problem with over 100,000 drug overdose-related deaths last year most of them related to opioid overdose, yet treatment options remain limited. Non-invasive Vagal Nerve Stimulation (nVNS) can be delivered via the ear or the neck and is a non-medication alternative to treatment of opioid withdrawal and OUD with potentially widespread applications. Methods: This paper reviews the neurobiology of opioid withdrawal and OUD and the emerging literature of nVNS for the application of OUD. Literature databases for Pubmed, Psychinfo, and Medline were queried for these topics for 1982-present. Results: Opioid withdrawal in the context of OUD is associated with activation of peripheral sympathetic and inflammatory systems as well as alterations in central brain regions including anterior cingulate, basal ganglia, and amygdala. NVNS has the potential to reduce sympathetic and inflammatory activation and counter the effects of opioid withdrawal in initial pilot studies. Preliminary studies show that it is potentially effective at acting through sympathetic pathways to reduce the effects of opioid withdrawal, in addition to reducing pain and distress. Conclusions: NVNS shows promise as a non-medication approach to OUD, both in terms of its known effect on neurobiology as well as pilot data showing a reduction in withdrawal symptoms as well as physiological manifestations of opioid withdrawal.

Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features.

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Pain is reduced by transcutaneous cervical vagus nerve stimulation and correlated with cardiorespiratory variability measures in the context of opioid withdrawal.

Over 100,000 individuals in the United States lost their lives secondary to drug overdose in 2021, with opioid use disorder (OUD) being a leading cause. Pain is an important component of opioid withdrawal, which can complicate recovery from OUD. This study's objectives were to assess the effects of transcutaneous cervical vagus nerve stimulation (tcVNS), a technique shown to reduce sympathetic arousal in other populations, on pain during acute opioid withdrawal and to study pain's relationships with objective cardiorespiratory markers. Twenty patients with OUD underwent opioid withdrawal while participating in a two-hour protocol. The protocol involved opioid cues to induce opioid craving and neutral conditions for control purposes. Adhering to a double-blind design, patients were randomly assigned to receive active tcVNS (n = 9) or sham stimulation (n = 11) throughout the protocol. At the beginning and end of the protocol, patients' pain levels were assessed using the numerical rating scale (0-10 scale) for pain (NRS Pain). During the protocol, electrocardiogram and respiratory effort signals were measured, from which heart rate variability (HRV) and respiration pattern variability (RPV) were extracted. Pre- to post- changes (denoted with a Δ) were computed for all measures. Δ NRS Pain scores were lower (P = 0.045) for the active group (mean ± standard deviation: -0.8 ± 2.4) compared to the sham group (0.9 ± 1.0). A positive correlation existed between Δ NRS pain scores and Δ RPV (Spearman's ρ = 0.46; P = 0.04). Following adjustment for device group, a negative correlation existed between Δ HRV and Δ NRS Pain (Spearman's ρ = -0.43; P = 0.04). This randomized, double-blind, sham-controlled pilot study provides the first evidence of tcVNS-induced reductions in pain in patients with OUD experiencing opioid withdrawal. This study also provides the first quantitative evidence of an association between breathing irregularity and pain. The correlations between changes in pain and changes in objective physiological markers add validity to the data. Given the clinical importance of reducing pain non-pharmacologically, the findings support the need for further investigation of tcVNS and wearable cardiorespiratory sensing for pain monitoring and management in patients with OUD.

Sketch of 2018 dengue outbreak in a megacity, Bangladesh.

BACKGROUND: Dengue has become a major public health threat in Bangladesh since 2000, when the first outbreak was reported. Each outbreak has distinct characteristics, and thus, the report of the outbreak helps to understand the disease process and subsequent clinical management of these patients. On that ground, the study was designed to sketch the clinico-epidemiological characteristics of the 2018 dengue outbreak in Bangladesh. METHODS: This hospital-based cross-sectional study was conducted in one of the largest public medical college hospitals and a single private hospital located in the southern and northern parts of the megacity of the country. A total of 297 confirmed dengue cases were assessed with a preformed pretested questionnaire. Clinico-epidemiological and laboratory parameters were reported along with sociodemographic details. Statistical analysis was performed with SPSS 20. RESULTS: Male patients were predominantly affected by dengue infection. The mean age of the patients was 31.24 ± 13.99 (SD) years, with a range from 2 to 85 years. Eighty-two percent of patients reported from the Dhaka metropolitan city. The highest percentage of cases (37.1%) was isolated from Bansree, Dhaka city, followed by Rampura (21.4%) and Khilgaon (6.2%). In addition to common symptoms, e.g., fever (90.6%), headache (90.6%), chills (81.8%), anorexia and vomiting (76.4%), backache, and redness of the eyes were two prominent symptoms that affected more than two-thirds of the study population. On the other hand, less common symptoms, such as cough, abdominal pain, and respiratory distress, were present in 39.7%, 33.7%, and 15.5% of patients, respectively. Overall, 17.6% of patients were hypotensive during admission, with a mean systolic blood pressure of 107.65 ± 18.17 (SD) mmHg. Other prominent signs were dehydration (80.5%) and rash (33%). CONCLUSION: This outbreak was especially characterized by gastrointestinal symptoms, which were predominant along with other typical features.

Topological Descriptors for Parkinson's Disease Classification and Regression Analysis.

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson's would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson's disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson's disease dataset comprised of healthy-elderly, healthy-young and Parkinson's disease patients. Our code is available at https://github.com/itsmeafra/Sublevel-Set-TDA.