Toward computational identification of multiscale "tipping points" in acute inflammation and multiple organ failure

Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning

Objective

To carry out exhaustive data-driven computations for the performance of noninvasive vital signs heart rate (HR), respiratory rate (RR), peripheral oxygen saturation (SpO2), and temperature (Temp), considered both independently and in all possible combinations, for early detection of sepsis.

Materials and methods

By extracting features interpretable by clinicians, we applied Gradient Boosted Decision Tree machine learning on a dataset of 2630 patients to build 240 models. Validation was performed on a geographically distinct dataset. Relative to onset, predictions were clocked as per 16 pairs of monitoring intervals and prediction times, and the outcomes were ranked.

Results

The combination of HR and Temp was found to be a minimal feature set yielding maximal predictability with area under receiver operating curve 0.94, sensitivity of 0.85, and specificity of 0.90. Whereas HR and RR each directly enhance prediction, the effects of SpO2 and Temp are significant only when combined with HR or RR. In benchmarking relative to standard methods Systemic Inflammatory Response Syndrome (SIRS), National Early Warning Score (NEWS), and quick-Sequential Organ Failure Assessment (qSOFA), Vital-SEP outperformed all 3 of them.

Conclusion

It can be concluded that using intensive care unit data even 2 vital signs are adequate to predict sepsis upto 6 h in advance with promising accuracy comparable to standard scoring methods and other sepsis predictive tools reported in literature. Vital-SEP can be used for fast-track prediction especially in limited resource hospital settings where laboratory based hematologic or biochemical assays may be unavailable, inaccurate, or entail clinically inordinate delays. A prospective study is essential to determine the clinical impact of the proposed sepsis prediction model and evaluate other outcomes such as mortality and duration of hospital stay.

The future of burn care from a complexity science perspective

Healthcare is undergoing a profound technological and digital transformation and has become increasingly complex. It is important for burns professionals and researchers to adapt to these developments which may require new ways of thinking and subsequent new strategies. As Einstein has put it: 'We must learn to see the world anew'. The relatively new scientific discipline "Complexity science" can give more direction to this and is the metaphorical open door that should not go unnoticed in view of the burn care of the future. Complexity sciences studies 'why the whole is more than the sum of the parts'. It studies how multiple separate components interact with each other and their environment and how these interactions lead to 'behavior of the system'. Biological systems are always part of smaller and larger systems and exhibit the behavior of adaptivity, hence the name complex adaptive systems. From the perspective of complexity science, a severe burn injury is an extreme disruption of the 'human body system'. But this disruption also applies to the systems at the organ and cellular level. All these systems follow principles of complex systems. Awareness of the scaling process at multilevel helps to understand and manage the complex situation when dealing with severe burn cases. The aim of this paper is to create awareness of the concept of complexity and to demonstrate the value and possibilities of complexity science methods and tools for the future of burn care through examples from preclinical, clinical, and organizational perspective in burn care.

Rethinking the definition of rate-induced tipping

The current definition of rate-induced tipping is tied to the idea of a pullback attractor limiting in forward and backward time to a stable quasi-static equilibrium. Here, we propose a new definition that encompasses the standard definition in the literature for certain scalar systems and includes previously excluded N-dimensional systems that exhibit rate-dependent critical transitions.

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

Cell-to-cell variation in gene expression and the aging process

There is tremendous variation in biological traits, and much of it is not accounted for by variation in DNA sequence, including human diseases and lifespan. Emerging evidence points to differences in the execution of the genetic program as a key source of variation, be it stochastic variation or programmed variation. Here we discuss variation in gene expression as an intrinsic property and how it could contribute to variation in traits, including the rate of aging. The review is divided into sections describing the historical context and evidence to date for nongenetic variation, the different approaches that may be used to detect nongenetic variation, and recent findings showing that the amount of variation in gene expression can be both genetically programmed and epigenetically controlled. Finally, we present evidence that changes in cell-to-cell variation in gene expression emerge as part of the aging process and may be linked to disease vulnerability as a function of age. These emerging concepts are likely to be important across the spectrum of biomedical research and may well underpin what we understand as biological aging.

Dynamics of Systemic Inflammation as a Function of Developmental Stage in Pediatric Acute Liver Failure

The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infants and children diagnosed with this complex clinical syndrome. Outcomes in PALF reflect interactions among the child’s clinical condition, response to supportive care, disease severity, potential for recovery, and, if needed, availability of a suitable organ for liver transplantation (LTx). Previously, we used computational analyses of immune/inflammatory mediators that identified three distinct dynamic network patterns of systemic inflammation in PALF associated with spontaneous survivors, non-survivors (NS), and LTx recipients. To date, there are no data exploring age-specific immune/inflammatory responses in PALF. Accordingly, we measured a number of clinical characteristics and PALF-associated systemic inflammatory mediators in daily serum samples collected over the first 7 days following enrollment from five distinct PALF cohorts (all spontaneous survivors without LTx): infants (INF, <1 year), toddlers (TOD, 1–2 years.), young children (YCH, 2–4 years), older children (OCH, 4–13 years) and adolescents (ADO, 13–18 years). Among those groups, we observed significant (P<0.05) differences in ALT, creatinine, Eotaxin, IFN-γ, IL-1RA, IL-1β, IL-2, sIL-2Rα, IL-4, IL-6, IL-12p40, IL-12p70, IL-13, IL-15, MCP-1, MIP-1α, MIP-1β, TNF-α, and NO2−/NO3−. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all sub-groups, with MIG/CXCL9 being a central node in all groups except INF. Dynamic Network Analysis (DyNA) inferred different dynamic patterns and overall dynamic inflammatory network complexity as follows: OCH>INF>TOD>ADO>YCH. Hypothesizing that systemically elevated but sparsely connected inflammatory mediators represent pathological inflammation, we calculated the AuCon score (area under the curve derived from multiple measures over time divided by DyNA connectivity) for each mediator, and identified HMGB1, MIG, IP-10/CXCl10, sIL-2Rα, and MCP-1/CCL2 as potential correlates of PALF pathophysiology, largely in agreement with the results of Partial Least Squares Discriminant Analysis. Since NS were in the INF age group, we compared NS to INF and found greater inflammatory coordination and dynamic network connectivity in NS vs. INF. HMGB1 was the sole central node in both INF and NS, though NS had more downstream nodes. Thus, multiple machine learning approaches were used to gain both basic and potentially translational insights into a complex inflammatory disease.

Dynamics of cytokines, immune cell counts and disease severity in patients with community-acquired pneumonia - Unravelling potential causal relationships

BACKGROUND

Community acquired pneumonia (CAP) is a severe and often rapidly deteriorating disease. To better understand its dynamics and potential causal relationships, we analyzed time series data of cytokines, blood and clinical parameters in hospitalized CAP patients.

METHODS

Time series data of 10 circulating cytokines, blood counts and clinical parameters were related to baseline characteristics of 403 CAP patients using univariate mixed models. Bivariate mixed models were applied to analyze correlations between the time series. To identify potential causal relationships, we inferred cross-lagged relationships between pairs of parameters using latent curve models with structured residuals.

RESULTS

IL-6 levels decreased faster over time in younger patients (P_adj = 0.06). IL-8, VCAM-1, and IL-6 correlated strongly with disease severity as assessed by the sequential organ failure assessment (SOFA) score (r = 0.49, 0.48, 0.46, respectively; all P_adj < 0.001). IL-6 and bilirubin correlated with respect to their mean levels and slopes over time (r = 0.36 and r = 0.46, respectively; P_adj < 0.001). A number of potential causal relationships were identified, e.g., a negative effect of ICAM-1 on MCP-1, or a positive effect of the level of creatinine on the subsequent VCAM-1 concentration (P < 0.001).

CONCLUSIONS

These results suggest that IL-6 trajectories of CAP patients are associated with age and run parallel to bilirubin levels. The time series analysis also unraveled directed, potentially causal relationships between cytokines, blood parameters and clinical outcomes. This will facilitate the development of mechanistic models of CAP, and with it, improvements in treatment or surveillance strategies for this disease.

TRIAL REGISTRATION

clinicaltrials.gov NCT02782013, May 25, 2016, retrospectively registered.

The Septic Neutrophil-Friend or Foe

ABSTRACT

Neutrophils play a critical role in the eradication of pathogenic organisms, particularly bacteria. However, in the septic patient the prolonged activation and accumulation of neutrophils may augment tissue and organ injury. This review discusses the different activation states and chemotaxis of neutrophils in septic patients. Neutrophil killing of bacteria and the formation of neutrophil extracellular traps represent important components of the innate immune response and they become dysregulated during sepsis, possibly through changes in their metabolism. Delayed neutrophil apoptosis may contribute to organ injury, or allow better clearance of pathogens. Neutrophils provide a friendly immune response to clear infections, but excessive activation and recruitment has the potential to turn them into potent foes.

Blood purification by nonselective hemoadsorption prevents death after traumatic brain injury and hemorrhagic shock in rats

BACKGROUND

Patients who sustain traumatic brain injury (TBI) and concomitant hemorrhagic shock (HS) are at high risk of high-magnitude inflammation which can lead to poor outcomes and death. Blood purification by hemoadsorption (HA) offers an alternative intervention to reduce inflammation after injury. We tested the hypothesis that HA would reduce mortality in a rat model of TBI and HS.

METHODS

Male Sprague Dawley rats were subjected to a combined injury of a controlled cortical impact to their brain and pressure-controlled HS. Animals were subsequently instrumented with an extracorporeal blood circuit that passed through a cartridge for sham or experimental treatment. In experimental animals, the treatment cartridge was filled with proprietary beads (Cytosorbents, Monmouth Junction, NJ) that removed circulating molecules between 5 kDa and 60 kDa. Sham rats had equivalent circulation but no blood purification. Serial blood samples were analyzed with multiplex technology to quantify changes in a trauma-relevant panel of immunologic mediators. The primary outcome was survival to 96 hours postinjury.

RESULTS

Hemoadsorption improved survival from 47% in sham-treated rats to 86% in HA-treated rats. There were no treatment-related changes in histologic appearance. Hemoadsorption affected biomarker concentrations both during the treatment and over the ensuing 4 days after injury. Distinct changes in biomarker concentrations were also measured in survivor and nonsurvivor rats from the entire cohort of rats indicating biomarker patterns associated with survival and death after injury.

CONCLUSION

Blood purification by nonselective HA is an effective intervention to prevent death in a combined TBI/HS rat model. Hemoadsorption changed circulating concentrations of multiple inmmunologically active mediators during the treatment time frame and after treatment. Hemoadsorption has been safely implemented in human patients with sepsis and may be a treatment option after injury.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Computational Analysis Supports an Early, Type 17 Cell-Associated Divergence of Blunt Trauma Survival and Mortality

OBJECTIVE

Blunt trauma patients may present with similar demographics and injury severity yet differ with regard to survival. We hypothesized that this divergence was due to different trajectories of systemic inflammation and utilized computational analyses to define these differences.

DESIGN

Retrospective clinical study and experimental study in mice.

SETTING

Level 1 trauma center and experimental laboratory.

PATIENTS

From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24 hours but ultimately died (nonsurvivors; n = 19) and patients who, after ICU admission, went on to be discharged(survivors; n = 19).

INTERVENTIONS

None in patients. Neutralizing anti-interleukin-17A antibody in mice.

MEASUREMENTS AND MAIN RESULTS

Data on systemic inflammatory mediators assessed within the first 24 hours and over 7 days were analyzed with computational modeling to infer dynamic networks of inflammation. Network density among inflammatory mediators in nonsurvivors increased in parallel with organ dysfunction scores over 7 days, suggesting the presence of early, self-sustaining, pathologic inflammation involving high-mobility group protein B1, interleukin-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine interleukin-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-interleukin-17A.

CONCLUSIONS

Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality after blunt trauma and suggest a lymphoid cell-based switch from self-resolving to self-sustaining inflammation.

A Biomathematical Model of Pneumococcal Lung Infection and Antibiotic Treatment in Mice

Pneumonia is considered to be one of the leading causes of death worldwide. The outcome depends on both, proper antibiotic treatment and the effectivity of the immune response of the host. However, due to the complexity of the immunologic cascade initiated during infection, the latter cannot be predicted easily. We construct a biomathematical model of the murine immune response during infection with pneumococcus aiming at predicting the outcome of antibiotic treatment. The model consists of a number of non-linear ordinary differential equations describing dynamics of pneumococcal population, the inflammatory cytokine IL-6, neutrophils and macrophages fighting the infection and destruction of alveolar tissue due to pneumococcus. Equations were derived by translating known biological mechanisms and assuming certain response kinetics. Antibiotic therapy is modelled by a transient depletion of bacteria. Unknown model parameters were determined by fitting the predictions of the model to data sets derived from mice experiments of pneumococcal lung infection with and without antibiotic treatment. Time series of pneumococcal population, debris, neutrophils, activated epithelial cells, macrophages, monocytes and IL-6 serum concentrations were available for this purpose. The antibiotics Ampicillin and Moxifloxacin were considered. Parameter fittings resulted in a good agreement of model and data for all experimental scenarios. Identifiability of parameters is also estimated. The model can be used to predict the performance of alternative schedules of antibiotic treatment. We conclude that we established a biomathematical model of pneumococcal lung infection in mice allowing predictions regarding the outcome of different schedules of antibiotic treatment. We aim at translating the model to the human situation in the near future.

Using measures of single-cell physiology and physiological state to understand organismic aging

Genetically identical organisms in homogeneous environments have different lifespans and healthspans. These differences are often attributed to stochastic events, such as mutations and 'epimutations', changes in DNA methylation and chromatin that change gene function and expression. But work in the last 10 years has revealed differences in lifespan- and health-related phenotypes that are not caused by lasting changes in DNA or identified by modifications to DNA or chromatin. This work has demonstrated persistent differences in single-cell and whole-organism physiological states operationally defined by values of reporter gene signals in living cells. While some single-cell states, for example, responses to oxygen deprivation, were defined previously, others, such as a generally heightened ability to make proteins, were, revealed by direct experiment only recently, and are not well understood. Here, we review technical progress that promises to greatly increase the number of these measurable single-cell physiological variables and measureable states. We discuss concepts that facilitate use of single-cell measurements to provide insight into physiological states and state transitions. We assert that researchers will use this information to relate cell level physiological readouts to whole-organism outcomes, to stratify aging populations into groups based on different physiologies, to define biomarkers predictive of outcomes, and to shed light on the molecular processes that bring about different individual physiologies. For these reasons, quantitative study of single-cell physiological variables and state transitions should provide a valuable complement to genetic and molecular explanations of how organisms age.

Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

SIGNIFICANCE

Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models.

RECENT ADVANCES

Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies.

CRITICAL ISSUES

Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments.

FUTURE DIRECTIONS

These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets.

Reducing acute respiratory distress syndrome occurrence using mechanical ventilation

The standard treatment for acute respiratory distress syndrome (ARDS) is supportive in the form of low tidal volume ventilation applied after significant lung injury has already developed. Nevertheless, ARDS mortality remains unacceptably high (> 40%). Indeed, once ARDS is established it becomes refractory to treatment, and therefore avoidance is key. However, preventive techniques and therapeutics to reduce the incidence of ARDS in patients at high-risk have not been validated clinically. This review discusses the current data suggesting that preemptive application of the properly adjusted mechanical breath can block progressive acute lung injury and significantly reduce the occurrence of ARDS.

Mathematical Modeling of Early Cellular Innate and Adaptive Immune Responses to Ischemia/Reperfusion Injury and Solid Organ Allotransplantation

A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression.

Computational Studies of the Intestinal Host-Microbiota Interactome

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.

Removal of inflammatory ascites is associated with dynamic modification of local and systemic inflammation along with prevention of acute lung injury: in vivo and in silico studies

BACKGROUND

Sepsis-induced inflammation in the gut/peritoneal compartment occurs early in sepsis and can lead to acute lung injury (ALI). We have suggested that inflammatory ascites drives the pathogenesis of ALI and that removal of ascites with an abdominal wound vacuum prevents ALI. We hypothesized that the time- and compartment-dependent changes in inflammation that determine this process can be discerned using principal component analysis (PCA) and Dynamic Bayesian Network (DBN) inference.

METHODS

To test this hypothesis, data from a previous study were analyzed using PCA and DBN. In that study, two groups of anesthetized, ventilated pigs were subjected to experimental sepsis via intestinal ischemia/reperfusion and placement of a peritoneal fecal clot. The control group (n = 6) had the abdomen opened at 12 h after injury (T12) with attachment of a passive drain. The peritoneal suction treatment (PST) group (n = 6) was treated in an identical fashion except that a vacuum was applied to the peritoneal cavity at T12 to remove ascites and maintained until T48. Multiple inflammatory mediators were measured in ascites and plasma and related to lung function (PaO2/FIO2 ratio and oxygen index) using PCA and DBN.

RESULTS

Peritoneal suction treatment prevented ALI based on lung histopathology, whereas control animals developed ALI. Principal component analysis revealed that local to the insult (i.e., ascites), primary proinflammatory cytokines play a decreased role in the overall response in the treatment group as compared with control. In both groups, multiple, nested positive feedback loops were inferred from DBN, which included interrelated roles for bacterial endotoxin, interleukin 6, transforming growth factor β1, C-reactive protein, PaO2/FIO2 ratio, and oxygen index. von Willebrand factor was an output in control, but not PST, ascites.

CONCLUSIONS

These combined in vivo and in silico studies suggest that in this clinically realistic paradigm of sepsis, endotoxin drives the inflammatory response in the ascites, interplaying with lung dysfunction in a feed-forward loop that exacerbates inflammation and leads to endothelial dysfunction, systemic spillover, and ALI; PST partially modifies this process.

Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses

The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.

Rapid antimicrobial susceptibility testing with electrokinetics enhanced biosensors for diagnosis of acute bacterial infections

Rapid pathogen detection and antimicrobial susceptibility testing (AST) are required in diagnosis of acute bacterial infections to determine the appropriate antibiotic treatment. Molecular approaches for AST are often based on the detection of known antibiotic resistance genes. Phenotypic culture analysis requires several days from sample collection to result reporting. Toward rapid diagnosis of bacterial infection in non-traditional healthcare settings, we have developed a rapid AST approach that combines phenotypic culture of bacterial pathogens in physiological samples and electrochemical sensing of bacterial 16S rRNA. The assay determines the susceptibility of pathogens by detecting bacterial growth under various antibiotic conditions. AC electrokinetic fluid motion and Joule heating induced temperature elevation are optimized to enhance the sensor signal and minimize the matrix effect, which improve the overall sensitivity of the assay. The electrokinetics enhanced biosensor directly detects the bacterial pathogens in blood culture without prior purification. Rapid determination of the antibiotic resistance profile of Escherichia coli clinical isolates is demonstrated.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Analysis of serum inflammatory mediators identifies unique dynamic networks associated with death and spontaneous survival in pediatric acute liver failure

Background

Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF.

Methods

We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference.

Findings

Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA.

Interpretation

The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.

A Systems Engineering Perspective on Homeostasis and Disease

Engineered systems are coupled networks of interacting sub-systems, whose dynamics are constrained to requirements of robustness and flexibility. They have evolved by design to optimize function in a changing environment and maintain responses within ranges. Analysis, synthesis, and design of complex supply chains aim to identify and explore the laws governing optimally integrated systems. Optimality expresses balance between conflicting objectives while resiliency results from dynamic interactions among elements. Our increasing understanding of life’s multi-scale architecture suggests that living systems share similar characteristics with much to be learned about biological complexity from engineered systems. If health reflects a dynamically stable integration of molecules, cell, tissues, and organs; disease indicates displacement compensated for and corrected by activation and combination of feedback mechanisms through interconnected networks. In this article, we draw analogies between concepts in systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Hybrid equation/agent-based model of ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects with spinal cord injury

Pressure ulcers are costly and life-threatening complications for people with spinal cord injury (SCI). People with SCI also exhibit differential blood flow properties in non-ulcerated skin. We hypothesized that a computer simulation of the pressure ulcer formation process, informed by data regarding skin blood flow and reactive hyperemia in response to pressure, could provide insights into the pathogenesis and effective treatment of post-SCI pressure ulcers. Agent-Based Models (ABM) are useful in settings such as pressure ulcers, in which spatial realism is important. Ordinary Differential Equation-based (ODE) models are useful when modeling physiological phenomena such as reactive hyperemia. Accordingly, we constructed a hybrid model that combines ODEs related to blood flow along with an ABM of skin injury, inflammation, and ulcer formation. The relationship between pressure and the course of ulcer formation, as well as several other important characteristic patterns of pressure ulcer formation, was demonstrated in this model. The ODE portion of this model was calibrated to data related to blood flow following experimental pressure responses in non-injured human subjects or to data from people with SCI. This model predicted a higher propensity to form ulcers in response to pressure in people with SCI vs. non-injured control subjects, and thus may serve as novel diagnostic platform for post-SCI ulcer formation.

Hemoadsorption reprograms inflammation in experimental gram-negative septic peritonitis: insights from in vivo and in silico studies

Improper compartmentalization of the inflammatory response leads to systemic inflammation in sepsis. Hemoadsorption (HA) is an emerging approach to modulate sepsis-induced inflammation. We sought to define the effects of HA on inflammatory compartmentalization in Escherichia coli-induced fibrin peritonitis in rats.

HYPOTHESIS

HA both reprograms and recompartmentalizes inflammation in sepsis. Sprague Dawley male rats were subjected to E. coli peritonitis and, after 24 h, were randomized to HA or sham treatment (sepsis alone). Venous blood samples collected at 0, 1, 3 and 6 h (that is, 24-30 h of total experimental sepsis), and peritoneal samples collected at 0 and 6 h, were assayed for 14 cytokines along with NO(2)(-/)NO(3)(-). Bacterial counts were assessed in the peritoneal fluid at 0 and 6 h. Plasma tumor necrosis factor (TNF)-α, interleukin (IL)-6, CXCL-1, and CCL2 were significantly reduced in HA versus sham. Principal component analysis (PCA) suggested that inflammation in sham was driven by IL-6 and TNF-α, whereas HA-associated inflammation was driven primarily by TNF-α, CXCL-1, IL-10 and CCL2. Whereas -peritoneal bacterial counts, plasma aspartate transaminase levels and peritoneal IL-5, IL-6, IL-18, interferon (IFN)-γ and NO(2)(-)/NO(3)(-) were significantly lower, both CXCL-1 and CCL2 as well as the peritoneal-to-plasma ratios of TNF-α, CXCL-1 and CCL2 were significantly higher in HA versus sham, suggesting that HA-induced inflammatory recompartmentalization leads to the different inflammatory drivers discerned in part by PCA. In conclusion, this study demonstrates the utility of combined in vivo/in silico methods and suggests that HA exerts differential effects on mediator gradients between local and systemic compartments that ultimately benefit the host.