5,207 research outputs found
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System
In this paper, we build a new, simple, and interpretable mathematical model
to describe the human glucose-insulin system. Our ultimate goal is the robust
control of the blood glucose (BG) level of individuals to a desired healthy
range, by means of adjusting the amount of nutrition and/or external insulin
appropriately. By constructing a simple yet flexible model class, with
interpretable parameters, this general model can be specialized to work in
different settings, such as type 2 diabetes mellitus (T2DM) and intensive care
unit (ICU); different choices of appropriate model functions describing uptake
of nutrition and removal of glucose differentiate between the models. In both
cases, the available data is sparse and collected in clinical settings, major
factors that have constrained our model choice to the simple form adopted.
The model has the form of a linear stochastic differential equation (SDE) to
describe the evolution of the BG level. The model includes a term quantifying
glucose removal from the bloodstream through the regulation system of the human
body, and another two terms representing the effect of nutrition and externally
delivered insulin. The parameters entering the equation must be learned in a
patient-specific fashion, leading to personalized models. We present numerical
results on patient-specific parameter estimation and future BG level
forecasting in T2DM and ICU settings. The resulting model leads to the
prediction of the BG level as an expected value accompanied by a band around
this value which accounts for uncertainties in the prediction. Such
predictions, then, have the potential for use as part of control systems which
are robust to model imperfections and noisy data. Finally, a comparison of the
predictive capability of the model with two different models specifically built
for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table
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Mathematical Models for Optimisation of Drug Administration in Intensive Care Units
Clinical status of critically ill patients is often extreme and rapidly evolving. Hence, pharmacological therapies must be tailored to patients' characteristics and adapted according to the evolution of their clinical pictures. To identify optimal personalized treatments, possible scenarios produced by different therapeutic choices must be predicted and compared. This process requires complex analyses involving the development of appropriate mathematical models.
In this Thesis, I focused on two important aspects of the pharmacological treatment of critically ill patients: the administration of antimicrobial drugs and the control of their glycaemic level. Although these problems are clinically very different, the modelling of their pathophysiological mechanisms can be addressed with similar tools.
I performed analyses based on retrospective clinical data collected with MargheritaTre, an electronic health record developed by GiViTI. The software to synchronize databases from hospitals to our laboratory and to preprocess data for analyses was written for the purpose of this Thesis.
Starting from the study of the physiological mechanisms at the basis of vancomycin pharmacokinetics I constructed a model to describe the evolution of the plasma concentration of this drug in critically ill patients. Compartment models were fitted on a sample of 141 patients, testing about 30 patient covariates and several functional dependencies for each variable.
Glucose dynamics were described through a system of delay differential equations reproducing intake, uptake and endogenous production of glucose, and organ-organ interactions mediated by hormones. Existing models, describing only the dynamics of glucose and insulin, fail to reproduce the correct evolution when glucose concentrations vary too rapidly. I improved these models, by introducing an equation describing glucagon dynamics and taking into account its effect on glucose metabolism. I investigated the dynamical properties of my model with analytical analyses, numerical simulations and fitting it to observed data
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Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication.
RationaleWe recently reported two novel biomarkers for acute kidney injury (AKI), tissue inhibitor of metalloproteinases (TIMP)-2 and insulin-like growth factor binding protein 7 (IGFBP7), both related to G1 cell cycle arrest.ObjectivesWe now validate a clinical test for urinary [TIMP-2]·[IGFBP7] at a high-sensitivity cutoff greater than 0.3 for AKI risk stratification in a diverse population of critically ill patients.MethodsWe conducted a prospective multicenter study of 420 critically ill patients. The primary analysis was the ability of urinary [TIMP-2]·[IGFBP7] to predict moderate to severe AKI within 12 hours. AKI was adjudicated by a committee of three independent expert nephrologists who were masked to the results of the test.Measurements and main resultsUrinary TIMP-2 and IGFBP7 were measured using a clinical immunoassay platform. The primary endpoint was reached in 17% of patients. For a single urinary [TIMP-2]·[IGFBP7] test, sensitivity at the prespecified high-sensitivity cutoff of 0.3 (ng/ml)(2)/1,000 was 92% (95% confidence interval [CI], 85-98%) with a negative likelihood ratio of 0.18 (95% CI, 0.06-0.33). Critically ill patients with urinary [TIMP-2]·[IGFBP7] greater than 0.3 had seven times the risk for AKI (95% CI, 4-22) compared with critically ill patients with a test result below 0.3. In a multivariate model including clinical information, urinary [TIMP-2]·[IGFBP7] remained statistically significant and a strong predictor of AKI (area under the curve, 0.70, 95% CI, 0.63-0.76 for clinical variables alone, vs. area under the curve, 0.86, 95% CI, 0.80-0.90 for clinical variables plus [TIMP-2]·[IGFBP7]).ConclusionsUrinary [TIMP-2]·[IGFBP7] greater than 0.3 (ng/ml)(2)/1,000 identifies patients at risk for imminent AKI. Clinical trial registered with www.clinicaltrials.gov (NCT 01573962)
An investigation into the effects of commencing haemodialysis in the critically ill
<b>Introduction:</b>
We have aimed to describe haemodynamic changes when haemodialysis is instituted in the critically ill. 3
hypotheses are tested: 1)The initial session is associated with cardiovascular instability, 2)The initial session is
associated with more cardiovascular instability compared to subsequent sessions, and 3)Looking at unstable
sessions alone, there will be a greater proportion of potentially harmful changes in the initial sessions compared
to subsequent ones.
<b>Methods:</b>
Data was collected for 209 patients, identifying 1605 dialysis sessions. Analysis was performed on hourly
records, classifying sessions as stable/unstable by a cutoff of >+/-20% change in baseline physiology
(HR/MAP). Data from 3 hours prior, and 4 hours after dialysis was included, and average and minimum values
derived. 3 time comparisons were made (pre-HD:during, during HD:post, pre-HD:post). Initial sessions were
analysed separately from subsequent sessions to derive 2 groups. If a session was identified as being unstable,
then the nature of instability was examined by recording whether changes crossed defined physiological ranges.
The changes seen in unstable sessions could be described as to their effects: being harmful/potentially harmful,
or beneficial/potentially beneficial.
<b>Results:</b>
Discarding incomplete data, 181 initial and 1382 subsequent sessions were analysed. A session was deemed to
be stable if there was no significant change (>+/-20%) in the time-averaged or minimum MAP/HR across time
comparisons. By this definition 85/181 initial sessions were unstable (47%, 95% CI SEM 39.8-54.2). Therefore
Hypothesis 1 is accepted. This compares to 44% of subsequent sessions (95% CI 41.1-46.3). Comparing these
proportions and their respective CI gives a 95% CI for the standard error of the difference of -4% to 10%.
Therefore Hypothesis 2 is rejected. In initial sessions there were 92/1020 harmful changes. This gives a
proportion of 9.0% (95% CI SEM 7.4-10.9). In the subsequent sessions there were 712/7248 harmful changes.
This gives a proportion of 9.8% (95% CI SEM 9.1-10.5). Comparing the two unpaired proportions gives a
difference of -0.08% with a 95% CI of the SE of the difference of -2.5 to +1.2. Hypothesis 3 is rejected. Fisher’s
exact test gives a result of p=0.68, reinforcing the lack of significant variance.
<b>Conclusions:</b>
Our results reject the claims that using haemodialysis is an inherently unstable choice of therapy. Although
proportionally more of the initial sessions are classed as unstable, the majority of MAP and HR changes are
beneficial in nature
Model-Based Analysis of User Behaviors in Medical Cyber-Physical Systems
Human operators play a critical role in various Cyber-Physical System (CPS) domains, for example, transportation, smart living, robotics, and medicine. The rapid advancement of automation technology is driving a trend towards deep human-automation cooperation in many safety-critical applications, making it important to explicitly consider user behaviors throughout the system development cycle. While past research has generated extensive knowledge and techniques for analyzing human-automation interaction, in many emerging applications, it remains an open challenge to develop quantitative models of user behaviors that can be directly incorporated into the system-level analysis.
This dissertation describes methods for modeling different types of user behaviors in medical CPS and integrating the behavioral models into system analysis. We make three main contributions. First, we design a model-based analysis framework to evaluate, improve, and formally verify the robustness of generic (i.e., non-personalized) user behaviors that are typically driven by rule-based clinical protocols. We conceptualize a data-driven technique to predict safety-critical events at run-time in the presence of possible time-varying process disturbances. Second, we develop a methodology to systematically identify behavior variables and functional relationships in healthcare applications. We build personalized behavior models and analyze population-level behavioral patterns. Third, we propose a sequential decision filtering technique by leveraging a generic parameter-invariant test to validate behavior information that may be measured through unreliable channels, which is a practical challenge in many human-in-the-loop applications. A unique strength of this validation technique is that it achieves high inter-subject consistency despite uncertain parametric variances in the physiological processes, without needing any individual-level tuning. We validate the proposed approaches by applying them to several case studies
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