Organ system network analysis and biological stability in critically ill patients

Acute kidney injury and distant organ dysfunction-network system analysis

Acute kidney injury (AKI) occurs in about half of critically ill patients and associates with high in-hospital mortality, increased long-term mortality post-discharge and subsequent progression to chronic kidney disease. Numerous clinical studies have shown that AKI is often complicated by dysfunction of distant organs, which is a cause of the high mortality associated with AKI. Experimental studies have elucidated many mechanisms of AKI-induced distant organ injury, which include inflammatory cytokines, oxidative stress and immune responses. This review will provide an update on evidence of organ crosstalk and potential therapeutics for AKI-induced organ injuries, and present the new concept of a systemic organ network to balance homeostasis and inflammation that goes beyond kidney-crosstalk with a single distant organ.

Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine

Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage.

Brain Biopsy for Neurological Diseases of Unknown Etiology in Critically Ill Patients: Feasibility, Safety, and Diagnostic Yield

OBJECTIVES

Brain biopsy is a useful surgical procedure in the management of patients with suspected neoplastic lesions. Its role in neurologic diseases of unknown etiology remains controversial, especially in ICU patients. This study was undertaken to determine the feasibility, safety, and the diagnostic yield of brain biopsy in critically ill patients with neurologic diseases of unknown etiology. We also aimed to compare these endpoints to those of non-ICU patients who underwent a brain biopsy in the same clinical context.

DESIGN

Monocenter, retrospective, observational cohort study.

SETTING

A French tertiary center.

PATIENTS

All adult patients with neurologic diseases of unknown etiology under mechanical ventilation undergoing in-ICU brain biopsy between January 2008 and October 2020 were compared with a cohort of non-ICU patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among the 2,207 brain-biopsied patients during the study period, 234 biopsies were performed for neurologic diseases of unknown etiology, including 29 who were mechanically ventilated and 205 who were not ICU patients. Specific histological diagnosis and final diagnosis rates were 62.1% and 75.9%, respectively, leading to therapeutic management modification in 62.1% of cases. Meningitis on prebiopsy cerebrospinal fluid analysis was the sole predictor of obtaining a final diagnosis (2.3 [1.4-3.8]; p = 0.02). ICU patients who experienced therapeutic management modification after the biopsy had longer survival (p = 0.03). The grade 1 to 4 (mild to severe) complication rates were: 24.1%, 3.5%, 0%, and 6.9%, respectively. Biopsy-related mortality was significantly higher in ICU patients compared with non-ICU patients (6.9% vs 0%; p = 0.02). Hematological malignancy was associated with biopsy-related mortality (1.5 [1.01-2.6]; p = 0.04).

CONCLUSIONS

Brain biopsy in critically ill patients with neurologic disease of unknown etiology is associated with high diagnostic yield, therapeutic modifications and postbiopsy survival advantage. Safety profile seems acceptable in most patients. The benefit/risk ratio of brain biopsy in this population should be carefully weighted.

Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research

Interoception, the ability to convey one's overall physiological state, allows people to describe their health along an experiential continuum, from excellent, very good, good, fair to poor. Each health state reflects a distinct pattern of one's overall function. This assay provides a new frame of understanding health and disease as complex-adaptive system states of the person as-a-whole. It firstly describes how complex patterns can emerge from simple equations. It then discusses how clinical medicine in certain domains has started to explore the pattern characteristics resulting in the heterogeneity of disease, and how this better understanding has improved patient management. The experiential state of health can be surprising to the observer-some are in good health with disabling disease, others are in poor health without the evidence of any. The main part of the assay describes the underlying complexity principles that contribute to health, and synthesizes available evidence from various research perspectives to support the philosophic/theoretical proposition of the complex-adaptive nature of health. It shows how health states arise from complex-adaptive system dynamics amongst the variables of a hierarchically layered system comprising the domains of a person's macro-level external environment to his nano-level biological blueprint. The final part suggests that the frame of health as a dynamic complex-adaptive state defines a new paradigm, and outlines ways of translating these expanded understandings to clinical practice, future research, and health system design.

Acute Kidney Injury Induces Innate Immune Response and Neutrophil Activation in the Lung

Complication in acute kidney injury (AKI) is significantly associated with developing acute respiratory failure (ARF), while ARF is one of the most important risks for AKI. These data suggest AKI and ARF may synergistically worsen the outcomes of critically ill patients and these organ injuries may not occur independently. Organ crosstalk between the kidney and the lung has been investigated by using animal models so far. This review will focus on innate immune response and neutrophil activation among the mechanisms that contribute to this organ crosstalk. AKI increased the blood level of an inflammatory mediator in high-mobility group box 1, which induces an innate immune reaction via toll-like receptor 4. The remarkable infiltration of neutrophils to the lung was observed in animal AKI models. IL-6 and IL-8 have been demonstrated to contribute to pulmonary neutrophil activation in AKI. In addition, the formation of a neutrophil extracellular trap was also observed in the lung after the exposure of renal ischemia reperfusion in the animal model. Further investigation is necessary to determine whether targeting innate immune response and neutrophil activation will be useful for developing new therapeutics that could improve multiple organ failure in critically ill patients.

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

BACKGROUND

A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis.

METHODS

201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups.

RESULTS

There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up.

CONCLUSION

This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.