Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

Abboud, Andrew; Mi, Qi; Puccio, Ava M.; Okonkwo, David O.; Buliga, Marius; Constantine, Gregory; Vodovotz, Yoram

doi:10.3389/fphar.2016.00342

Cited by 39 publications

(44 citation statements)

References 38 publications

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“…The Pearson correlation between these two scores is only 0.06 across subjects. The correlation becomes 0.56 across surviving subjects, however (not shown; Abboud et al, 2016). Accordingly, we counted the deaths within a cluster (or equivalently, upon dividing to the cluster size, the probability of death within a cluster) as the primary component of the cluster weight.…”

Section: Resultsmentioning

confidence: 94%

Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients

Constantine

Buliga

et al. 2016

Front. Pharmacol.

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Inflammation induced by traumatic brain injury (TBI) is complex, individual-specific, and associated with morbidity and mortality. We sought to develop dynamic, data-driven, predictive computational models of TBI-induced inflammation based on cerebrospinal fluid (CSF) biomarkers. Thirteen inflammatory mediators were determined in serial CSF samples from 27 severe TBI patients. The Glasgow Coma Scale (GCS) score quantifies the initial severity of the neurological status of the patient on a numerical scale from 3 to 15. The 6-month Glasgow Outcome Scale (GOS) score, the outcome variable, was taken as the variable to express and predict as a function of the other input variables. Data on each subject consisting of ten clinical (one-dimensional) variables, such as age, gender, and presence of infection, along with inflammatory biomarker time series were used to generate both multinomial logistic as well as probit models that predict low (poor outcome) or high (favorable outcome) levels of the GOS score. To determine if CSF inflammation biomarkers could predict TBI outcome, a logistic model for low (≤3; poor neurological outcome) or high levels (≥4; favorable neurological outcome) of the GOS score involving a full effect of the pro-inflammatory cytokine tumor necrosis factor-α and both linear and quadratic effects of the anti-inflammatory cytokine interleukin-10 was obtained. To better stratify patients as their pathology progresses over time, a technique called “Dynamic Profiling” was developed in which patients were clustered, using the spectral Laplacian and Hartigan’s k-means method, into disjoint groups at different stages. Initial clustering was based on GCS score; subsequent clustering was performed based on clinical and demographic information and then further, sequential clustering based on the levels of individual inflammatory mediators over time. These clusters assess the risk of mortality of a new patient after each inflammatory mediator reading, based on the existing information in the previous data in the cluster to which the new patient belongs at the time, in essence acting as a “virtual clinician.” Using the Dynamic Profiling method, we show examples that suggest that severe TBI patient neurological outcomes could be predicted as a function of time post-TBI using CSF inflammatory mediators.

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Section: Resultsmentioning

confidence: 94%

Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients

Constantine

Buliga

et al. 2016

Front. Pharmacol.

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“…There were 34 prospective studies, all being observational studies (30–61, 64, 65). One study was a retrospective case series (63).…”

Section: Resultsmentioning

confidence: 99%

Cerebrospinal Fluid and Microdialysis Cytokines in Severe Traumatic Brain Injury: A Scoping Systematic Review

et al. 2017

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ObjectiveTo perform two scoping systematic reviews of the literature on cytokine measurement in: 1. cerebral microdialysis (CMD) and 2. cerebrospinal fluid (CSF) in severe traumatic brain injury (TBI) patients.MethodsTwo separate systematic reviews were conducted: one for CMD cytokines and the second for CSF cytokines. Both were conducted in severe TBI (sTBI) patients only.Data sourcesArticles from MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library (inception to October 2016), reference lists of relevant articles, and gray literature were searched.Study selectionTwo reviewers independently identified all manuscripts utilizing predefined inclusion/exclusion criteria. A two-tier filter of references was conducted.Data extractionPatient demographic and study data were extracted to tables.ResultsThere were 19 studies identified describing the analysis of cytokines via CMD in 267 sTBI patients. Similarly, there were 32 studies identified describing the analysis of CSF cytokines in 1,363 sTBI patients. The two systematic reviews demonstrated: 1. limited literature available on CMD cytokine measurement in sTBI, with some preliminary data supporting feasibility of measurement and associations between cytokines and patient outcome. 2. Various CSF measured cytokines may be associated with patient outcome at 6–12 months, including interleukin (IL)-1b, IL-1ra, IL-6, IL-8, IL-10, and tumor necrosis factor 3. There is little to no literature in support of an association between CSF cytokines and neurophysiologic or tissue outcomes.ConclusionThe evaluation of CMD and CSF cytokines is an emerging area of the literature in sTBI. Further, large prospective multicenter studies on cytokines in CMD and CSF need to be conducted.

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“…In the setting of inflammation and critical illness, PCA has been used to discern principal characteristics of experimental hemorrhagic shock [15], sepsis [20,21], and wound healing [22], as well as in clinical settings such traumatic brain injury [23] as well as pediatric acute liver failure [18]. This includes a recent demonstration of the potential use of patient-specific PCA followed by hierarchical clustering of circulating inflammation biomarkers to define trauma patient outcome groups [24].…”

Section: From Minutes To Seconds: Insights Into Principal Drivers Of mentioning

confidence: 99%

“…More recently, we have demonstrated the potential to generate mechanistic models based on insights derived from dynamic network and PCA models (in this case, in the setting of traumatic brain injury [23]. Furthermore, these models formed increasingly granular variants of a core hypothesis of how inflammation evolved, namely that inflammation induces secondary damage/dysfunction at the cell/tissue/organ/whole organism level, which in turn drives further inflammation [1].…”

Section: Mechanistic Computational Modeling Defines An Abstract Inflamentioning

confidence: 99%

“…We were faced with the realization that inflammation induced by traumatic injury, severe infection, or organ failure progresses extremely rapidly to drive a life or death bifurcation [18,19,23,32,33], and were driven by the aforementioned hypothesis regarding a feed-forward process of inflammation to damage/dysfunction to more inflammation [1]. Thus, the concept of finding an appropriate diagnostic test and administering an individual-specific therapy in a timely fashion is daunting, even given the possibility of mathematically modeling acute inflammation in individuals and populations in order to predict clinical outcomes [24,38].…”

Section: Conclusion: Resetting Not Reinventing the “Clock”mentioning

confidence: 99%

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Reverse engineering the inflammatory “clock”: from computational modeling to rational resetting

Vodovotz

2016

Drug Discovery Today: Disease Models

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Properly-regulated inflammation is central to homeostasis. Traumatic injury, hemorrhagic shock, septic shock, and other injury-related processes such as wound healing are associated with dysregulated inflammation. Like many biological processes, inflammation is a dynamic, complex system whose function, like that of an analog clock, cannot be discerned simply from a laundry list of its parts (data). The advent of multiplexed platforms for gathering biological data, while providing an unprecedented level of detailed information about the inflammatory response, has paradoxically also proven to be overwhelming. This problem is especially acute when the datasets involve time courses, since typical statistical analyses and data-driven modeling are geared towards single time points. Various groups have addressed this problem using dynamic approaches to data-driven and mechanistic computational modeling. These modeling tools can be thought of as the “gears” and “hands” of the “clock,” and have led to insights regarding principal drivers, dynamic networks, feedbacks, and regulatory switches that characterize and perhaps regulate the inflammatory response. In parallel, mechanistic computational models have given an abstracted sense of how the inflammatory “clock” works, leading to in silico models of critically ill individuals and populations. Integrating data-driven and mechanistic modeling may point the way to a rational “resetting” of inflammation via model-driven precision medicine.

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Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death

Cited by 39 publications

References 38 publications

Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients

Dynamic Profiling: Modeling the Dynamics of Inflammation and Predicting Outcomes in Traumatic Brain Injury Patients

Cerebrospinal Fluid and Microdialysis Cytokines in Severe Traumatic Brain Injury: A Scoping Systematic Review

Reverse engineering the inflammatory “clock”: from computational modeling to rational resetting

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