Sepsis progression and outcome: a dynamical model

A nanotrap improves survival in severe sepsis by attenuating hyperinflammation

Targeting single mediators has failed to reduce the mortality of sepsis. We developed a telodendrimer (TD) nanotrap (NT) to capture various biomolecules via multivalent, hybrid and synergistic interactions. Here, we report that the immobilization of TD-NTs in size-exclusive hydrogel resins simultaneously adsorbs septic molecules, e.g. lipopolysaccharides (LPS), cytokines and damage- or pathogen-associated molecular patterns (DAMPs/PAMPs) from blood with high efficiency (92-99%). Distinct surface charges displayed on the majority of pro-inflammatory cytokines (negative) and anti-inflammatory cytokines (positive) allow for the selective capture via TD NTs with different charge moieties. The efficacy of NT therapies in murine sepsis is both time-dependent and charge-dependent. The combination of the optimized NT therapy with a moderate antibiotic treatment results in a 100% survival in severe septic mice by controlling both infection and hyperinflammation, whereas survival are only 50-60% with the individual therapies. Cytokine analysis, inflammatory gene activation and tissue histopathology strongly support the survival benefits of treatments.

Low vasopressin and progression of neonatal sepsis to septic shock: a prospective cohort study

The study objective was to analyze the association between low plasma vasopressin and progression of sepsis to septic shock in neonates < 34 weeks gestation. Septic neonates of < 34 weeks gestation were consecutively enrolled; moribund neonates and those with major malformations were excluded. Subjects were monitored for progression of sepsis to septic shock over the first 7 days from enrolment. Plasma vasopressin levels and inducible nitric oxide synthase levels were measured at the onset of sepsis (T0), severe sepsis (T1), and septic shock (T2). Primary outcome was plasma vasopressin levels at the point of sepsis in those who progressed to septic shock in comparison with matched nested controls in the non-progression group. Forty-nine (47%) enrolled subjects developed severe sepsis or septic shock. Plasma vasopressin levels (pg/ml) at the onset of sepsis were significantly low in those who progressed to septic shock (median (IQR), 31 (2.5-80) versus 100 (12-156); p = 0.02). After adjusting for confounders, vasopressin levels were independently associated with progression to septic shock (adjusted OR (95% CI), 0.97 (0.96, 0.99); p = 0.01).Conclusion: Preterm septic neonates who progressed to septic shock had suppressed vasopressin levels before the onset of shock. Low vasopressin levels were independently associated with progression to septic shock.What is known:• In animal sepsis models and adult septic patients, exuberant production of nitric oxide metabolites and low vasopressin levels have been reportedly associated with progression to septic shock.• Vasopressin levels have been variably reported as low as well as elevated in children with septic shock.What is New:• Preterm neonates who progressed from sepsis to septic shock had significantly lower levels of vasopressin before the onset of shock in comparison with those who did not progress.• Low vasopressin levels independently predicted the progression from sepsis to septic shock in this population.

Markov State Modelling of Disease Courses and Mortality Risks of Patients with Community-Acquired Pneumonia

Community-acquired pneumonia (CAP) is one of the most frequent infectious diseases worldwide, with high lethality. Risk evaluation is well established at hospital admission, and re-evaluation is advised for patients at higher risk. However, severe disease courses may develop from all levels of severity. We propose a stochastic continuous-time Markov model describing daily development of time courses of CAP severity. Disease states were defined based on the Sequential Organ Failure Assessment (SOFA) score. Model calibration was based on longitudinal data from 2838 patients with a primary diagnosis of CAP from four clinical studies (PROGRESS, MAXSEP, SISPCT, VISEP). We categorized CAP severity into five disease states and estimated transition probabilities for CAP progression between these states and corresponding sojourn times. Good agreement between model predictions and clinical data was observed. Time courses of mortality were correctly predicted for up to 28 days, including validation with patient data not used for model calibration. We conclude that CAP disease course follows a Markov process, suggesting the necessity of daily monitoring and re-evaluation of patient's risk. Our model can be used for regular updates of risk assessments of patients and could improve the design of clinical trials by estimating transition rates for different risk groups.

A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine

Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.

Mathematical modeling of septic shock based on clinical data

BACKGROUND

Mathematical models of diseases may provide a unified approach for establishing effective treatment strategies based on fundamental pathophysiology. However, models that are useful for clinical practice must overcome the massive complexity of human physiology and the diversity of patients' environmental conditions. With the aim of modeling a complex disease, we choose sepsis, which is highly complex, life-threatening systemic disease with high mortality. In particular, we focused on septic shock, a subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality. Our model includes cardiovascular, immune, nervous system models and a pharmacological model as submodels and integrates them to create a sepsis model based on pathological facts.

RESULTS

Model validation was done in two steps. First, we established a model for a standard patient in order to confirm the validity of our approach in general aspects. For this, we checked the correspondence between the severity of infection defined in terms of pathogen growth rate and the ease of recovery defined in terms of the intensity of treatment required for recovery. The simulations for a standard patient showed good correspondence. We then applied the same simulations to a patient with heart failure as an underlying disease. The model showed that spontaneous recovery would not occur without treatment, even for a very mild infection. This is consistent with clinical experience. We next validated the model using clinical data of three sepsis patients. The model parameters were tuned for these patients based on the model for the standard patient used in the first part of the validation. In these cases, the simulations agreed well with clinical data. In fact, only a handful parameters need to be tuned for the simulations to match with the data.

CONCLUSIONS

We have constructed a model of septic shock and have shown that it can reproduce well the time courses of treatment and disease progression. Tuning of model parameters for each patient could be easily done. This study demonstrates the feasibility of disease models, suggesting the possibility of clinical use in the prediction of disease progression, decisions on the timing of drug dosages, and the estimation of time of infection.

Epinecidin-1 protects mice from LPS-induced endotoxemia and cecal ligation and puncture-induced polymicrobial sepsis

The antimicrobial peptide, epinecidin-1 (Epi), was identified from Epinephelus coioides and may have clinical application for treating sepsis. Epi has been shown to ameliorate antibiotic-resistant bacteria-induced sepsis in mice, but further evaluation in mixed-flora models and a description of the protective mechanisms are essential to establish this peptide as a potential therapeutic. Therefore, we first tested the protective effects of Epi against polymicrobial sepsis-induced bactericidal infection, inflammation and lung injury that result from cecal ligation and puncture in mice. Furthermore, since lipopolysaccharide (LPS) is a key inducer of inflammation during bacterial infection and sepsis, we also tested the LPS-antagonizing activity and related mechanisms of Epi-mediated protection in mice with LPS-induced endotoxemia and LPS-treated Raw264.7 mouse macrophage cells. Epi rescued mice from both polymicrobial sepsis and endotoxemia after delayed administration and suppressed both lung and systemic inflammatory responses, while attenuating lung injury and diminishing bacterial load. In vitro studies revealed that Epi suppressed LPS-induced inflammatory cytokine production. Mechanistically, Epi disrupted the interaction between LPS and LPS binding protein, competed with LPS for binding on the cell surface, and inhibited Toll-like receptor 4 endocytosis, resulting in inhibition of LPS-induced reactive oxygen species/p38/Akt/NF-κB signaling and subsequent cytokine production. Overall, our results demonstrate that Epi is a promising therapeutic agent for endotoxemia and polymicrobial sepsis.

Disease types discovery from a large database of inpatient records: A sepsis study

Data-driven phenotype discoveries on Electronic Health Records (EHR) data have recently drawn benefits across many aspects of clinical practice. In the method described in this paper, we map a very large EHR database containing more than a million inpatient cases into a low dimensional space where diseases with similar phenotypes have similar representation. This embedding allows for an effective segmentation of diseases into more homogeneous categories, an important task of discovering disease types for precision medicine. In particular, many diseases have heterogeneous nature. For instance, sepsis, a systemic and progressive inflammation, can be caused by many factors, and can have multiple manifestations on different human organs. Understanding such heterogeneity of the disease can help in addressing many important issues regarding sepsis, including early diagnosis and treatment, which is of huge importance as sepsis is one of the main causes of in-hospital deaths in the United States. This study analyzes state of the art embedding models that have had huge success in various fields, applying them to disease embedding from EHR databases. Particular interest is given to learning multi-type representation of heterogeneous diseases, which leads to more homogeneous groups. Our results show evidence that such representations have phenotypes of higher quality and also provide benefit when predicting mortality of inpatient visits.

Effectiveness of Multiple Blood-Cleansing Interventions in Sepsis, Characterized in Rats

Sepsis is a serious, life-threatening condition that presents a growing problem in medicine, but there is still no satisfying solution for treating it. Several blood cleansing approaches recently gained attention as promising interventions that target the main site of problem development–the blood. The focus of this study is an evaluation of the theoretical effectiveness of hemoadsorption therapy and pathogen reduction therapy. This is evaluated using the mathematical model of Murine sepsis, and the results of over 2,200 configurations of single and multiple intervention therapies simulated on 5,000 virtual subjects suggest the advantage of pathogen reduction over hemoadsorption therapy. However, a combination of two approaches is found to take advantage of their complementary effects and outperform either therapy alone. The conducted computational experiments provide unprecedented evidence that the combination of two therapies synergistically enhances the positive effects beyond the simple superposition of the benefits of two approaches. Such a characteristic could have a profound influence on the way sepsis treatment is conducted.

Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding

Sepsis represents the host's systemic inflammatory response to a severe infection. It causes substantial human morbidity resulting in hundreds of thousands of deaths each year. Despite decades of intense research, the basic mechanisms still remain elusive. In either experimental animal models of sepsis or human patients, there are substantial physiological changes, many of which may result in subsequent organ injury. Variations in age, gender, and medical comorbidities including diabetes and renal failure create additional complexity that influence the outcomes in septic patients. Specific system-based alterations, such as the coagulopathy observed in sepsis, offer both potential insight and possible therapeutic targets. Intracellular stress induces changes in the endoplasmic reticulum yielding misfolded proteins that contribute to the underlying pathophysiological changes. With these multiple changes it is difficult to precisely classify an individual's response in sepsis as proinflammatory or immunosuppressed. This heterogeneity also may explain why most therapeutic interventions have not improved survival. Given the complexity of sepsis, biomarkers and mathematical models offer potential guidance once they have been carefully validated. This review discusses each of these important factors to provide a framework for understanding the complex and current challenges of managing the septic patient. Clinical trial failures and the therapeutic interventions that have proven successful are also discussed.

Mathematical modeling suggests that periodontitis behaves as a non-linear chaotic dynamical process

BACKGROUND

This study aims to expand on a previously presented cellular automata model and further explore the non-linear dynamics of periodontitis. Additionally the authors investigated whether their mathematical model could predict the two known types of periodontitis, aggressive (AgP) and chronic periodontitis (CP).

METHODS

The time evolution of periodontitis was modeled by an iterative function, based on the hypothesis that the host immune response level determines the rate of periodontitis progression. The chaotic properties of this function were investigated by direct iteration, and the model was validated by immunologic and clinical parameters derived from two clinical study populations.

RESULTS

Periodontitis can be described as chaos with the level of the host immune response determining its progression rate; the dynamics of the proposed model suggest that by increasing the host immune response level, periodontitis progression rate decreases. Renormalization transformations show the presence of two overlapping zones of disease activity corresponding to AgP and CP. By k-means cluster analysis, immunologic parameters corroborated the findings of the renormalization transformations. Periodontitis progression rates are modeled to scale with a power law of 1.3, and the mean exponential speed of the system is found to be 1.85 (metric entropy); clinical datasets confirmed the mathematical estimates.

CONCLUSIONS

This study introduces a mathematical model that identifies periodontitis as a non-linear chaotic process. It offers a quantitative assessment of the disease progression rate and identifies two zones of disease activity that correspond to the existing classification of periodontitis in the AgP and CP types.

Systems modeling and simulation applications for critical care medicine

Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area.

Multiple organ dysfunction syndrome pathogenesis and care: a complex systems' theory perspective

AIMS AND OBJECTIVES

To discuss multiple organ dysfunction syndrome (MODS) from a complex systems' theory perspective and to delineate a conceptual framework for the development and care of MODS.

BACKGROUND

MODS is an intricate and devastating manifestation of critical illness characterized by widespread aberrant molecular, cellular and systemic responses.

DESIGN AND METHODS

Narrative literature review (MEDLINE, CINAHL databases) and knowledge synthesis with the theoretical assertions of chaos and complex systems' theory. Cellular and systemic response paradoxes in MODS (including cellular hypoxia, cell death and signalling) are reviewed.

RESULTS

The diseased person is depicted as a complex adaptive system. The relevancy of some of the principles of complex chaotic systems' theory to the proposed model is illustrated, including sensitive dependence on initial conditions, emergence, attractors, self-organization, self-organized criticality and emerging order. The transition from life-supporting to death-related organismic responses is illustrated as a critical event in MODS and care implications are drawn.

CONCLUSIONS

Patient responses in MODS appear to conform to the principles of chaotic systems. Death is illustrated not as a consequence of homeostatic failure but as a 'deliberate' self-organized phenomenon entailing multiple dynamically evolving mechanisms.

RELEVANCE TO CLINICAL PRACTICE

Some of the principles of chaotic complex systems may need to be taken into account to advance care in MODS. An alternative theoretical perspective may support nurses to conceptualize both MODS and their role in a way that will help them to cope better with this devastating syndrome and develop practice.

Insulin regulates macrophage activation through activin A

Strict control of serum glucose with insulin has been associated with a reduction in the development of multiple organ dysfunction syndrome potentially through alterations in macrophage activation. Although the mechanism responsible for this effect remains poorly elucidated, recent work has suggested that this may occur through the PI3K/AKT pathway. As a result, we set out to investigate the role and means of activation of this pathway by insulin on endotoxin-mediated activation of tissue-fixed macrophages.

METHODS

THP-1 cells were stimulated with endotoxin with or without 24 h of insulin pretreatment. Cellular protein was extracted and analyzed by immunoblot for factors essential to Toll-like receptor 4 signaling. Supernatants were analyzed by enzyme-linked immunosorbent assay for TNF-alpha and IL-8 production. In addition, potential effect of the transforming growth factor superfamily was analyzed through selective inhibition of either the transforming growth factor beta or activin A receptors.

RESULTS

Endotoxin exposure resulted in the activation of extracellular signal-regulated kinase 1/2, p38 and Jun kinase, the degradation of IkappaB, the activation of nuclear factor kappaB, and the production of TNF-alpha and IL-8. Insulin pretreatment delayed endotoxin-mediated extracellular signal-regulated kinase 1/2, p38 and Jun kinase, the degradation of IkappaB, the activation of nuclear factor kappaB, and the production of TNF-alpha and IL-8. Insulin alone was associated with an increase in cytoplasmic SH2-containing inositol 5'-phosphatase (SHIP) but a decrease in lipid raft bound SHIP. The changes induced by insulin on SHIP and endotoxin-mediated signaling were reversed by activin A blockade.

CONCLUSIONS

Insulin results in regulation of macrophage activity in response to endotoxin through the release of activin A and subsequent production of SHIP. This increase in cytoplasmic SHIP results in attenuated endotoxin-mediated intracellular signaling and inflammatory mediator production.