First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients

peer reviewe

Mathematical model of the mitral valve and the cardiovascular system, application for studying, monitoring and in the diagnosis of valvular pathologies

peer reviewedA cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiological accurate “open on pressure, close on flow” law. Thus, it does not consider the real time scale of the valve aperture dynamics and thus doesn’t fully capture valve dysfunction particularly where the dysfunction involves partial closure. This research describes a new closed-loop CVS model including a model describing the progressive aperture of the mitral valve and valid over the full cardiac cycle. This new model is solved for a healthy and diseased mitral valve

Validation of a model-based virtual trials method for tight glycemic control in intensive care

peer reviewedBACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology

Organ failure and tight glycemic control in the SPRINT study

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality. METHODS: A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA 2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB >/=0.5) to SOFA /=0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB >/=0.5) increased the likelihood SOFA /=0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials

Mathematical multi-scale model of the cardiovascular system including mitral valve dynamics. Application to ischemic mitral insufficiency

Valve dysfunction is a common cardiovascular pathology. Despite significant clinical research, there is little formal study of how valve dysfunction affects overall circulatory dynamics. Validated models would offer the ability to better understand these dynamics and thus optimize diagnosis, as well as surgical and other interventions. A cardiovascular and circulatory system (CVS) model has already been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiologically accurate “open on pressure, close on flow” law. However, it does not consider real-time valve opening dynamics and therefore does not fully capture valve dysfunction, particularly where the dysfunction involves partial closure. This research describes an updated version of this previous closed-loop CVS model that includes the progressive opening of the mitral valve, and is defined over the full cardiac cycle. Simulations of the cardiovascular system with healthy mitral valve are performed, and, the global hemodynamic behaviour is studied compared with previously validated results. The error between resulting pressure-volume (PV) loops of already validated CVS model and the new CVS model that includes the progressive opening of the mitral valve is assessed and remains within typical measurement error and variability. Simulations of ischemic mitral insufficiency are also performed. Pressure-Volume loops, transmitral flow evolution and mitral valve aperture area evolution follow reported measurements in shape, amplitude and trends. The resulting cardiovascular system model including mitral valve dynamics provides a foundation for clinical validation and the study of valvular dysfunction in vivo. The overall models and results could readily be generalised to other cardiac valves

Modeling Progressive Fibrosis with Pluripotent Stem Cells Identifies an Anti-fibrotic Small Molecule.

Progressive organ fibrosis accounts for one-third of all deaths worldwide, yet preclinical models that mimic the complex, progressive nature of the disease are lacking, and hence, there are no curative therapies. Progressive fibrosis across organs shares common cellular and molecular pathways involving chronic injury, inflammation, and aberrant repair resulting in deposition of extracellular matrix, organ remodeling, and ultimately organ failure. We describe the generation and characterization of an in&nbsp;vitro progressive fibrosis model that uses cell types derived from induced pluripotent stem cells. Our model produces endogenous activated transforming growth factor β (TGF-β) and contains activated fibroblastic aggregates that progressively increase in size and stiffness with activation of known fibrotic molecular and cellular changes. We used this model as a phenotypic drug discovery platform for modulators of fibrosis. We validated this platform by identifying a compound that promotes resolution of fibrosis in in&nbsp;vivo and ex&nbsp;vivo models of ocular and lung fibrosis

Insulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care

peer reviewedEffective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and evolution of stress response, and are independently associated with mortality. This study examines the daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, 54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points (25th and 75th percentiles) are used to assess differences between groups and their evolution over time. Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < 0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < 0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will require greater measurement frequency, reduced reliance on insulin, and more explicit specification of carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome

Tracing river chemistry in space and time : dissolved inorganic constituents of the Fraser River, Canada

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Geochimica et Cosmochimica Acta 124 (2014): 283-308, doi:10.1016/j.gca.2013.09.006.The Fraser River basin in southwestern Canada bears unique geologic and climatic features which make it an ideal setting for investigating the origins, transformations and delivery to the coast of dissolved riverine loads under relatively pristine conditions. We present results from sampling campaigns over three years which demonstrate the lithologic and hydrologic controls on fluxes and isotope compositions of major dissolved inorganic runoff constituents (dissolved nutrients, major and trace elements, 87Sr/86Sr, δD). A time series record near the Fraser mouth allows us to generate new estimates of discharge-weighted concentrations and fluxes, and an overall chemical weathering rate of 32 t km-2 y-1. The seasonal variations in dissolved inorganic species are driven by changes in hydrology, which vary in timing across the basin. The time series record of dissolved 87Sr/86Sr is of particular interest, as a consistent shift between higher (“more radiogenic”) values during spring and summer and less radiogenic values in fall and winter demonstrates the seasonal variability in source contributions throughout the basin. This seasonal shift is also quite large (0.709 – 0.714), with a discharge-weighted annual average of 0.7120 (2 s.d. = 0.0003). We present a mixing model which predicts the seasonal evolution of dissolved 87Sr/86Sr based on tributary compositions and water discharge. This model highlights the importance of chemical weathering fluxes from the old sedimentary bedrock of headwater drainage regions, despite their relatively small contribution to the total water flux.This work was supported by the WHOI Academic Programs Office and MIT PAOC Houghton Fund to BMV, a WHOI Arctic Research Initiative grant to ZAW, NSF-ETBC grant OCE-0851015 to BPE and TIE, and NSF grant EAR-1226818 to BPE

A community resource for paired genomic and metabolomic data mining

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.Peer reviewe

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments