Dynamic tracking of microvascular hemoglobin content for continuous perfusion monitoring in the intensive care unit: pilot feasibility study

Purpose: There is a need for bedside methods to monitor oxygen delivery in the microcirculation. Near-infrared spectroscopy commonly measures tissue oxygen saturation, but does not reflect the time-dependent variability of microvascular hemoglobin content (MHC) that attempts to match oxygen supply with demand. The objective of this study is to determine the feasibility of MHC monitoring in critically ill patients using high-resolution near-infrared spectroscopy to assess perfusion in the peripheral microcirculation. Methods: Prospective observational cohort of 36 patients admitted within 48 h at a tertiary intensive care unit. Perfusion was measured on the quadriceps, biceps, and/or deltoid, using the temporal change in optical density at the isosbestic wavelength of hemoglobin (798 nm). Continuous wavelet transform was applied to the hemoglobin signal to delineate frequency ranges corresponding to physiological oscillations in the cardiovascular system. Results: 31/36 patients had adequate signal quality for analysis, most commonly affected by motion artifacts. MHC signal demonstrates inter-subject heterogeneity in the cohort, indicated by different patterns of variability and frequency composition. Signal characteristics were concordant between muscle groups in the same patient, and correlated with systemic hemoglobin levels and oxygen saturation. Signal power was lower for patients receiving vasopressors, but not correlated with mean arterial pressure. Mechanical ventilation directly impacts MHC in peripheral tissue. Conclusion: MHC can be measured continuously in the ICU with high-resolution near-infrared spectroscopy, and reflects the dynamic variability of hemoglobin distribution in the microcirculation. Results suggest this novel hemodynamic metric should be further evaluated for diagnosing microvascular dysfunction and monitoring peripheral perfusion

Computational health engineering applied to model infectious diseases and antimicrobial resistance spread

Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host?pathogen?protein interactions, combined with a better understanding of a host?s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination

Toward More Predictive Models by Leveraging Multimodal Data

Data is often composed of structured and unstructured data. Both forms of data have information that can be exploited by machine learning models to increase their prediction performance on a task. However, integrating the features from both these data forms is a hard, complicated task. This is all the more true for models which operate on time-constraints. Time-constrained models are machine learning models that work on input where time causality has to be maintained such as predicting something in the future based on past data. Most previous work does not have a dedicated pipeline that is generalizable to different tasks and domains, especially under time-constraints. In this work, we present a systematic, domain-agnostic pipeline for integrating features from structured and unstructured data while maintaining time causality for building models. We focus on the healthcare and consumer market domain and perform experiments, preprocess data, and build models to demonstrate the generalizability of the pipeline. More specifically, we focus on the task of identifying patients who are at risk of an imminent ICU admission. We use our pipeline to solve this task and show how augmenting unstructured data with structured data improves model performance. We found that by combining structured and unstructured data we can get a performance improvement of up to 8.5

Experimental, theoretical, and translational studies of RBC distribution in capillary networks

The purpose of this thesis was to evaluate the physiology of red blood cell (RBC) distribution in skeletal muscle capillary networks. Because this is the terminal site of oxygen exchange in the microcirculation, characterization of this fundamental process informs an understanding of how microvascular blood flow regulation matches oxygen supply with local tissue demand. Prior studies in this field have focused on small groups of capillaries, and have not linked capillary network structure with functional measurements, nor evaluated the temporal complexity of RBC distribution over physiologically-relevant scales. It is also unclear how the functional units called capillary modules – comprised of parallel capillaries from arteriole to venule – relate together within large capillary networks. In this thesis, we employed multiple methodologies to achieve this goal with preclinical animal models, theoretical simulations, and translational studies in human patients. First, we used intravital videomicroscopy of resting extensor digitorum longus muscle in rats and discovered that skeletal muscle capillary networks are organized into columns of interconnected capillary modules spanning thousands of microns – a structure we called the Capillary Fascicle. We showed that capillary-RBC hemodynamics are heterogeneous within a module and between modules. Next, we evaluated capillary module hemodynamics and demonstrated that RBC flow is independent of module resistance, providing evidence for regulation of driving pressure at the level of the capillary module, that requires pre- and post-capillary mechanisms of control. Using a dual-phase mathematical model of blood flow within artificial capillary module geometries, we showed that RBC flow heterogeneity is an intrinsic property of capillary module structure, and that variations to inflow hematocrit and pressure impact RBC distribution as a consequence of the rheological properties of microvascular blood flow. Finally, we used high-resolution near-infrared spectroscopy to monitor the temporal variability of hemoglobin content in skeletal muscle of patients in the intensive care unit (ICU). We showed that RBC perfusion characteristics are not consistent between patients, and that ICU interventions directly impact microvascular RBC distribution. Together, these studies support a theory of capillary networks as active participants in microvascular blood flow regulation, with structural features of capillary networks contributing to functional characteristics of RBC distribution