arXiv subjects

Jesper Mehlsen

Publications and source records attributed to Jesper Mehlsen.

5 recordsLinked to original sources

arXiv2021

Postural Orthostatic Tachycardia Syndrome explained using abaroreflex response model

Recent studies have shown that Postural Orthostatic Tachycardia Syndrome (POTS) patients have abnormal low frequency $\approx 0.1$ Hz blood pressure and heart rate dynamics. These dynamics are attributed to the baroreflex and can give insight into the mechanistic causes which are the basis for proposed subgroups of POTS. In this study we develop a baroreflex model replicating the low-frequency dynamics observed in POTS patient data as well as represent subgroups of POTS in our model. We utilize signal processing to quantify the effects that model parameters have on low-frequency oscillations. Results show that key physiological parameters that represent the hypothesized causes are central to our model's ability to reproduce observed dynamics from patient data.

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arXiv2020

Characterization of blood pressure and heart rate oscillations of POTS patients via uniform phase empirical mode decomposition

Objective: Postural Orthostatic Tachycardia Syndrome (POTS) is associated with the onset of tachycardia upon postural change. The current diagnosis involves the measurement of heart rate (HR) and blood pressure (BP) during head-up tilt (HUT) or active standing test. A positive diagnosis is made if HR changes with more than 30 bpm (40 bpm in patients aged 12-19 years), ignoring all of the BP and most of the HR signals. This study examines 0.1 Hz oscillations in systolic arterial blood pressure (SBP) and HR signals providing additional metrics characterizing the dynamics of the baroreflex. Methods: We analyze data from 28 control subjects and 28 POTS patients who underwent HUT. We extract beat-to-beat HR and SBP during a 10 min interval including 5 minutes of baseline and 5 minutes of HUT. We employ Uniform Phase Empirical Mode Decomposition (UPEMD) to extract 0.1 Hz stationary modes from both signals and use random forest machine learning and k-means clustering to analyze the outcomes. Results show that the amplitude of the 0.1 Hz oscillations is higher in POTS patients and that the phase response between the two signals is shorter (p < 0.005). Conclusion: POTS is associated with an increase in the amplitude of SBP and HR 0.1 Hz oscillation and a shortening of the phase between the two signals. Significance: The 0.1 Hz phase response and oscillation amplitude metrics provide new markers that can improve POTS diagnostic augmenting the existing diagnosis protocol only analyzing the change in heart rate.

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arXiv2019

A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during a pathogen challenge

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory issues like sepsis, one of the ten leading causes of death in the United States and one of the three leading causes of death in the intensive care unit. Understanding the effects of inflammation on the autonomic control system can improve a patient's chance of recovery after an inflammatory event such as surgery. Though the effects of the autonomic response on the inflammatory system are well defined, there remains a gap in understanding the reverse response. Specifically, the impact of the inflammatory response on the autonomic control system remains unknown. In this study, we investigate hypothesized interactions of the inflammatory system with the thermal and cardiovascular regulatory systems in response to an endotoxin challenge using mathematical modeling. We calibrate the model to data from two independent studies: a) of the inflammatory response in healthy young men and b) a comparative study of the inflammatory response between mice and humans. Simulation analysis is used to explore how the model responds to pathological input and treatment, specifically antibiotics, antipyretics, vasopressors, and combination therapy. Our findings show that multimodal treatment that simultaneously targets both the pathogen and the infection symptoms gives the most favorable recovery outcome.

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arXiv2016

Personalized Mathematical Model Predicting Endotoxin-Induced Inflammatory Responses in Young Men

The initial reaction of the body to pathogenic microbial infection or severe tissue trauma is an acute inflammatory response. The magnitude of such a response is of critical importance, since an uncontrolled response can cause further tissue damage, sepsis, and ultimately death, while an insufficient response can result in inadequate clearance of pathogens. A normal inflammatory response helps to annihilate threats posed by microbial pathogenic ligands, such as endotoxins, and thus, restore the body to a healthy state. Using a personalized mathematical model, comprehension and a detailed description of the interactions between pro- and anti-inflammatory cytokines can provide important insight in the evaluation of a patient with sepsis or a susceptible patient in surgery. Our model is calibrated to experimental data obtained from experiments measuring pro-inflammatory cytokines (interleukin-6 (IL-6), tumor necrosis factor (TNF-), and chemokine ligand-8 (CXCL8)) and the anti-inflammatory cytokine interleukin-10 (IL-10) over 8 hours in 20 healthy young male subjects, given a low dose intravenous injection of lipopolysaccharide (LPS), resulting in endotoxin-stimulated inflammation. Through the calibration process, we created a personalized mathematical model that can accurately determine individual differences between subjects, as well as identify those who showed an abnormal response.

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arXiv2016

Classification of syncope through data analytics

Objective: Syncope is a sudden loss of consciousness with loss of postural tone and spontaneous recovery; it is a common condition, albeit one that is challenging to accurately diagnose. Uncertainties about the triggering mechanisms and their underlying pathophysiology have led to various classifications of patients exhibiting this symptom. This study presents a new way to classify syncope types using machine learning. Method: we hypothesize that syncope types can be characterized by analyzing blood pressure and heart rate time series data obtained from the head-up tilt test procedure. By optimizing classification rates, we identify a small number of determining markers which enable data clustering. Results: We apply the proposed method to clinical data from 157 subjects; each subject was identified by an expert as being either healthy or suffering from one of three conditions: cardioinhibitory syncope, vasodepressor syncope and postural orthostatic tachycardia. Clustering confirms the three disease groups and identifies two distinct subgroups within the healthy controls. Conclusion: The proposed method provides evidence to question current syncope classifications; it also offers means to refine them. Significance: Current syncope classifications are not based on pathophysiology and have not led to significant improvements in patient care. It is expected that a more faithful classification will facilitate our understanding of the autonomic system for healthy subjects, which is essential in analyzing pathophysiology of the disease groups.

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