Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins.

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

Patient, family caregiver, and economic outcomes of an integrated screening and novel stepped collaborative care intervention in the oncology setting in the USA (CARES): a randomised, parallel, phase 3 trial.

BACKGROUND: The current standard of care of screening and referring patients for treatment for symptoms, such as depression, pain, and fatigue, is not effective. This trial aimed to test the efficacy of an integrated screening and novel stepped collaborative care intervention versus standard of care for patients with cancer and at least one of the following symptoms: depression, pain, or fatigue. METHODS: This randomised, parallel, phase 3 trial was conducted in 29 oncology outpatient clinics associated with the UPMC Hillman Cancer Center in the USA. Patients (aged ≥21 years) with any cancer type and clinical levels of depression, pain, or fatigue (or all of these) were eligible. Eligible family caregivers were aged 21 years or older and providing care to a patient diagnosed with cancer who consented for this study. Patients were randomly assigned (1:1) to stepped collaborative care or standard of care using a central, permuted block design (sizes of 2, 4, and 6) stratified by sex and prognostic status. The biostatistician, oncologists, and outcome assessors were masked to treatment assignment. Stepped collaborative care was once-weekly cognitive behavioural therapy for 50-60 min from a care coordinator via telemedicine (eg, telephone or videoconferencing). Pharmacotherapy for symptoms might be initiated or changed if recommended by the treatment team or preferred by the patient. Standard of care was screening and referral to a health-care provider for treatment of symptoms. The primary outcome was health-related quality of life in patients at 6 months. Maintenance of the treatment benefits was assessed at 12 months. Participants included in the primary analysis were per intention to treat, which included patients missing one or both follow-up assessments. This trial was registered with ClinicalTrials.gov (NCT02939755). FINDINGS: Between Dec 5, 2016, and April 8, 2021, 459 patients and 190 family caregivers were enrolled. 222 patients were assigned to standard of care and 237 to stepped collaborative care. Of 459 patients, 201 (44%) were male and 258 (56%) were female. Patients in the stepped collaborative care group had a greater 0-6-month improvement in health-related quality of life than patients in the standard-of-care group (p=0·013, effect size 0·09). Health-related quality of life was maintained for the stepped collaborative care group (p=0·74, effect size 0·01). Patients in the stepped collaborative care group had greater 0-6-month improvements than the standard-of-care group in emotional (p=0·012), functional (p=0·042), and physical (p=0·033) wellbeing. No adverse events were reported by patients in either group and deaths were considered unrelated to the study. INTERPRETATION: An integrated screening and novel stepped collaborative care intervention, compared with the current standard of care, is recommended to improve health-related quality of life. The findings of this study will advance the implementation of guideline concordant care (screening and treatment) and has the potential to shift the practice of screening and treatment paradigm nationwide, improving outcomes for patients diagnosed with cancer. FUNDING: US National Cancer Institute.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

INTRODUCTION: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). METHODS: Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy. RESULTS: The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

Temporally complex inflammatory networks in an animal model reveal signatures for interstitial cystitis and bladder pain syndrome phenotype.

INTRODUCTION AND OBJECTIVE: Interstitial cystitis and bladder pain syndrome (IC/BPS) presents with symptoms of debilitating bladder pain and is typically a diagnosis of exclusion. The cystoscopic detection of Hunner's lesions increases the likelihood of detecting tissue inflammation on bladder biopsy and increases the odds of therapeutic success with anti-inflammatory drugs. However, the identification of this subgroup remains challenging with the current lack of surrogate biomarkers of IC/BPS. On the path towards identifying biomarkers of IC/BPS, we modeled the dynamic evolution of inflammation in an experimental IC/BPS rodent model using computational biological network analysis of inflammatory mediators (cytokines and chemokines) released into urine. The use of biological network analysis allows us to identify urinary proteins that could be drivers of inflammation and could therefore serve as therapeutic targets for the treatment of IC/BPS. METHODS: Rats subjected to cyclophosphamide (CYP) injection (150 mg/kg) were used as an experimental model for acute IC/BPS (n = 8). Urine from each void was collected from the rats over a 12-h period and was assayed for 13 inflammatory mediators using Luminex™. Time-interval principal component analysis (TI-PCA) and dynamic network analysis (DyNA), two biological network algorithms, were used to identify biomarkers of inflammation characteristic of IC/BPS over time. RESULTS: Compared to vehicle-treated rats, nearly all inflammatory mediators were elevated significantly (p < 0.05) in the urine of CYP treated rats. TI-PCA highlighted that GRO-KC, IL-5, IL-18, and MCP-1 account for the greatest variance in the inflammatory response. At early time points, DyNA indicated a positive correlation between IL-4 and IL-1ß and between TNF-α and IL-1ß. Analysis of TI-PCA and DyNA at later time points showed the emergence of IL-5, IL-6, and IFNγ as additional key mediators of inflammation. Furthermore, DyNA network complexity rose and fell before peaking at 9.5 h following CYP treatment. This pattern of inflammation may mimic the fluctuating severity of inflammation associated with IC/BPS flares. CONCLUSIONS: Computational analysis of inflammation networks in experimental IC/BPS analysis expands on the previously accepted inflammatory signatures of IC by adding IL-5, IL-18, and MCP-1 to the prior studies implicating IL-6 and GRO as IC/BPS biomarkers. This analysis supports a complex evolution of inflammatory networks suggestive of the rise and fall of inflammation characteristic of IC/BPS flares.

IRF3 Signaling within the Mouse Stroma Influences Sepsis Pathogenesis.

IFN regulatory factor 3 (IRF3) is a transcription factor that is activated by multiple pattern-recognition receptors. We demonstrated previously that IRF3 plays a detrimental role in a severe mouse model of sepsis, induced by cecal ligation and puncture. In this study, we found that IRF3-knockout (KO) mice were greatly protected from sepsis in a clinically relevant version of the cecal ligation and puncture model incorporating crystalloid fluids and antibiotics, exhibiting improved survival, reduced disease score, lower levels of serum cytokines, and improved phagocytic function relative to wild-type (WT) mice. Computational modeling revealed that the overall complexity of the systemic inflammatory/immune network was similar in IRF3-KO versus WT septic mice, although the tempo of connectivity differed. Furthermore, the mediators driving the network differed: TNF-α, IL-1ß, and IL-6 predominated in WT mice, whereas MCP-1 and IL-6 predominated in IRF3-KO mice. Network analysis also suggested differential IL-6-related inflammatory programs in WT versus IRF3-KO mice. We created bone marrow chimeras to test the role of IRF3 within leukocytes versus stroma. Surprisingly, chimeras with IRF3-KO bone marrow showed little protection from sepsis, whereas chimeras with IRF3-KO stroma showed a substantial degree of protection. We found that WT and IRF3-KO macrophages had a similar capacity to produce IL-6 and phagocytose bacteria in vitro. Adoptive transfer experiments demonstrated that the genotype of the host environment affected the capacity of monocytes to produce IL-6 during sepsis. Thus, IRF3 acts principally within the stromal compartment to exacerbate sepsis pathogenesis via differential impacts on IL-6-related inflammatory programs.

MetfOrmin BenefIts Lower Extremities with Intermittent Claudication (MOBILE IC): randomized clinical trial protocol.

BACKGROUND: Peripheral artery disease (PAD) affects over 230 million people worldwide and is due to systemic atherosclerosis with etiology linked to chronic inflammation, hypertension, and smoking status. PAD is associated with walking impairment and mobility loss as well as a high prevalence of coronary and cerebrovascular disease. Intermittent claudication (IC) is the classic presenting symptom for PAD, although many patients are asymptomatic or have atypical presentations. Few effective medical therapies are available, while surgical and exercise therapies lack durability. Metformin, the most frequently prescribed oral medication for Type 2 diabetes, has salient anti-inflammatory and promitochondrial properties. We hypothesize that metformin will improve function, retard the progression of PAD, and improve systemic inflammation and mitochondrial function in non-diabetic patients with IC. METHODS: 200 non-diabetic Veterans with IC will be randomized 1:1 to 180-day treatment with metformin extended release (1000 mg/day) or placebo to evaluate the effect of metformin on functional status, PAD progression, cardiovascular disease events, and systemic inflammation. The primary outcome is 180-day maximum walking distance on the 6-min walk test (6MWT). Secondary outcomes include additional assessments of functional status (cardiopulmonary exercise testing, grip strength, Walking Impairment Questionnaires), health related quality of life (SF-36, VascuQoL), macro- and micro-vascular assessment of lower extremity blood flow (ankle brachial indices, pulse volume recording, EndoPAT), cardiovascular events (amputations, interventions, major adverse cardiac events, all-cause mortality), and measures of systemic inflammation. All outcomes will be assessed at baseline, 90 and 180 days of study drug exposure, and 180 days following cessation of study drug. We will evaluate the primary outcome with linear mixed-effects model analysis with covariate adjustment for baseline 6MWT, age, baseline ankle brachial indices, and smoking status following an intention to treat protocol. DISCUSSION: MOBILE IC is uniquely suited to evaluate the use of metformin to improve both systematic inflammatory responses, cellular energetics, and functional outcomes in patients with PAD and IC. TRIAL REGISTRATION: The prospective MOBILE IC trial was publicly registered (NCT05132439) November 24, 2021.

Exercise Training Decreases Hepatic Injury and Metastases Through Changes in Immune Response to Liver Ischemia/Reperfusion in Mice.

BACKGROUND AND AIMS: Liver ischemia/reperfusion injury (IRI) induces local and systemic inflammation in which neutrophil extracellular traps (NETs) are major drivers. IRI markedly augments metastatic growth, which is consistent with the notion that the liver IRI can serve as a premetastatic niche. Exercise training (ExT) confers a sustainable protection, reducing IRI in some animal models, and has been associated with improved survival in patients with cancer; however, the impact of ExT on liver IRI or development of hepatic metastases is unknown. APPROACH AND RESULTS: Mice were randomized into exercise (ExT) and sedentary groups before liver IRI and tumor injection. Computerized dynamic network analysis of 20 inflammatory mediators was used to dissect the sequence of mediator interactions after ischemia/reperfusion (I/R) that induce injury. ExT mice showed a significant decrease in hepatic IRI and tissue necrosis. This coincided with disassembly of complex networks among inflammatory mediators seen in sedentary mice. Neutrophil infiltration and NET formation were decreased in the ExT group, which suppressed the expression of liver endothelial cell adhesion molecules. Concurrently, ExT mice revealed a distinct population of infiltrating macrophages expressing M2 phenotypic genes. In a metastatic model, fewer metastases were present 3 weeks after I/R in the ExT mice, a finding that correlated with a marked increase in tumor-suppressing T cells within the tumor microenvironment. CONCLUSIONS: ExT preconditioning mitigates the inflammatory response to liver IRI, protecting the liver from injury and metastases. In light of these findings, potential may exist for the reduction of liver premetastatic niches induced by liver IRI through the use of ExT as a nonpharmacologic therapy before curative surgical approaches.

Optical Biopsy Using a Neural Network to Predict Gene Expression From Photos of Wounds.

BACKGROUND: The clinical characterization of the biological status of complex wounds remains a considerable challenge. Digital photography provides a non-invasive means of obtaining wound information and is currently employed to assess wounds qualitatively. Advances in machine learning (ML) image processing provide a means of identifying "hidden" features in pictures. This pilot study trains a convolutional neural network (CNN) to predict gene expression based on digital photographs of wounds in a canine model of volumetric muscle loss (VML). MATERIALS AND METHODS: Images of volumetric muscle loss injuries and tissue biopsies were obtained in a canine model of VML. A CNN was trained to regress gene expression values as a function of the extracted image segment (color and spatial distribution). Performance of the CNN was assessed in a held-back test set of images using Mean Absolute Percentage Error (MAPE). RESULTS: The CNN was able to predict the gene expression of certain genes based on digital images, with a MAPE ranging from ∼10% to ∼30%, indicating the presence and identification of distinct, and identifiable patterns in gene expression throughout the wound. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of ML regression on medical images. Specifically, the use of CNNs to determine the mechanistic biological state of a VML wound could aid both the design of future mechanistic interventions and the design of trials to test those therapies. Future work will expand the CNN training and/or test set, with potential expansion to predicting functional gene modules.

Spatiotemporally specific roles of TLR4, TNF, and IL-17A in murine endotoxin-induced inflammation inferred from analysis of dynamic networks.

BACKGROUND: Bacterial lipopolysaccharide (LPS) induces a multi-organ, Toll-like receptor 4 (TLR4)-dependent acute inflammatory response. METHODS: Using network analysis, we defined the spatiotemporal dynamics of 20, LPS-induced, protein-level inflammatory mediators over 0-48 h in the heart, gut, lung, liver, spleen, kidney, and systemic circulation, in both C57BL/6 (wild-type) and TLR4-null mice. RESULTS: Dynamic Network Analysis suggested that inflammation in the heart is most dependent on TLR4, followed by the liver, kidney, plasma, gut, lung, and spleen, and raises the possibility of non-TLR4 LPS signaling pathways at defined time points in the gut, lung, and spleen. Insights from computational analyses suggest an early role for TLR4-dependent tumor necrosis factor in coordinating multiple signaling pathways in the heart, giving way to later interleukin-17A-possibly derived from pathogenic Th17 cells and effector/memory T cells-in the spleen and blood. CONCLUSIONS: We have derived novel, systems-level insights regarding the spatiotemporal evolution acute inflammation.

IL-17A - A regulator in acute inflammation: Insights from in vitro, in vivo and in silico studies.

Acute inflammation following sterile injury is both inevitable and necessary to restore homeostasis and promote tissue repair. However, when excessive, inflammation can jeopardize the viability of organs and cause detrimental systemic effects. Identifying key-regulators of the immune cascade induced by surgery is vital to attenuating excessive inflammation and its subsequent effects. In this review, we describe the emerging role of IL-17A as a key-regulator in acute inflammation. The role of IL-17A in chronic disease states, such as rheumatoid arthritis, psoriasis and cancer has been well documented, but its significance in acute inflammation following surgery, sepsis, or traumatic injury has not been well studied. We aim to highlight the role of IL-17A in acute inflammation caused by trauma, liver ischemia, and organ transplantation, as well as in post-operative surgical infections. Further investigation of the roles of this cytokine in acute inflammation may stimulate novel therapies or diagnostic modalities.

Computational Analysis Supports IL-17A as a Central Driver of Neutrophil Extracellular Trap-Mediated Injury in Liver Ischemia Reperfusion.

Hepatic ischemia reperfusion (I/R) is a clinically relevant model of acute sterile inflammation leading to a reverberating, self-sustaining inflammatory response with resultant necrosis. We hypothesized that computerized dynamic network analysis (DyNA) of 20 inflammatory mediators could help dissect the sequence of post-I/R mediator interactions that induce injury. Although the majority of measured inflammatory mediators become elevated in the first 24 h, we predicted that only a few would be secreted early in the process and serve as organizational centers of downstream intermediator complexity. In support of this hypothesis, DyNA inferred a central organizing role for IL-17A during the first 3 h of reperfusion. After that, DyNA revealed connections among almost all the inflammatory mediators, representing an ongoing cytokine storm. Blocking IL-17A immediately after reperfusion disassembled the inflammatory networks and protected the liver from injury. Disassembly of the networks was not achieved if IL-17A blockage was delayed two or more hours postreperfusion. Network disassembly was accompanied by decrease in neutrophil infiltration and neutrophil extracellular trap (NET) formation. By contrast, administration of recombinant IL-17A increased neutrophil infiltration, NET formation, and liver necrosis. The administration of DNase, a NET inhibitor, significantly reduced hepatic damage despite prior administration of IL-17A, and DNase also disassembled the inflammatory networks. In vitro, IL-17A was a potent promoter of NET formation. Therefore, computational analysis identified IL-17A's early, central organizing role in the rapid evolution of a network of inflammatory mediators that induce neutrophil infiltration and NET formation responsible for hepatic damage after liver I/R.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

Adverse childhood experiences (ACEs), cell-mediated immunity, and survival in the context of cancer.

PURPOSE: Adverse childhood experiences (ACEs) have been shown to be associated with increased risk of mortality. The biobehavioral mechanisms linking adverse events and survival in cancer patients remain unclear. The aims of the study were to: (1) examine the rates and types of early adverse events in patients diagnosed with cancer; (2) investigate the association of adverse events with circulating cytokines, representing immune status of the patient; and (3) test whether immune markers mediated the association between early adverse events and survival while adjusting for other factors that are associated with immunity (e.g., fatigue) and survival (e.g., depression). PATIENTS AND METHODS: The patients were recruited from an outpatient oncology clinic. Patients were administered a battery of questionnaires including the Traumatic Events Survey and the Center for Epidemiological Studies-Depression scale. Blood was collected and serum levels of cytokines were assessed to characterize immune status. Descriptive statistics, Mann-Whitney U tests and Cox regression were performed to address study aims. RESULTS: Of the 408 patients, 66% reported at least one ACE. After adjusting for demographic, disease-specific factors, and psychological/behavioral factors; having had a major upheaval between parents during childhood or adolescence was associated with poorer survival [ß = -0.702, HR = 0.496, p = 0.034]. Lower levels of interleukin-2 (IL-2) explained, in part, the link between this early adverse event and poorer survival as when IL-2 was entered into the model, a major upheaval between one's parents and survival was no longer significant [ß = -0.612, HR = 0.542, p = 0.104]. CONCLUSION: Having experienced an ACE was associated with lower IL-2 levels-a growth factor for anti-inflammatory T-regulatory lymphocytes-central in contemporary immunotherapy, as well as poorer survival in those diagnosed with cancer. Since lower IL-2 levels also explained, in part, the link between the ACE involving parental upheaval and survival, there is support for a psychoneuroimmunological model of disease course in this vulnerable population.

A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice.

Bacterial lipopolysaccharide (LPS) induces an acute inflammatory response across multiple organs, primarily via Toll-like receptor 4 (TLR4). We sought to define novel aspects of the complex spatiotemporal dynamics of LPS-induced inflammation using computational modeling, with a special focus on the timing of pathological systemic spillover. An analysis of principal drivers of LPS-induced inflammation in the heart, gut, lung, liver, spleen, and kidney to assess organ-specific dynamics, as well as in the plasma (as an assessment of systemic spillover), was carried out using data on 20 protein-level inflammatory mediators measured over 0-48h in both C57BL/6 and TLR4-null mice. Using a suite of computational techniques, including a time-interval variant of Principal Component Analysis, we confirm key roles for cytokines such as tumor necrosis factor-α and interleukin-17A, define a temporal hierarchy of organ-localized inflammation, and infer the point at which organ-localized inflammation spills over systemically. Thus, by employing a systems biology approach, we obtain a novel perspective on the time- and organ-specific components in the propagation of acute systemic inflammation.

Elevations in Circulating sST2 Levels Are Associated With In-Hospital Mortality and Adverse Clinical Outcomes After Blunt Trauma.

BACKGROUND: Soluble suppression of tumorigenicity 2 (sST2), a decoy receptor for interleukin (IL)-33, has emerged as a novel biomarker in various disease processes. Recent studies have elucidated the role of the sST2/IL-33 complex in modulating the balance of Th1/Th2 immune responses after tissue stress. However, the role of sST2 as a biomarker after traumatic injury remains unclear. To address this, we evaluated serum sST2 correlations with mortality and in-hospital adverse outcomes as endpoints in blunt trauma patients. METHODS: We retrospectively analyzed clinical and biobank data of 493 blunt trauma victims 472 survivors (mean age: 48.4 ± 0.87; injury severity score [ISS]: 19.6 ± 0.48) and 19 nonsurvivors (mean age: 58.8 ± 4.5; ISS: 23.3 ± 2.1) admitted to the intensive care unit. Given the confounding impact of age on the inflammatory response, we derived a propensity-matched survivor subgroup (n = 19; mean age: 59 ± 3; ISS: 23.4 ± 2) using an IBM SPSS case-control matching algorithm. Serial blood samples were obtained from all patients (3 samples within the first 24 h and then once daily from day [D] 1 to D5 after injury). sST2 and twenty-nine inflammatory biomarkers were assayed using enzyme-linked immunosorbent assay and Luminex, respectively. Two-way analysis of variance on ranks was used to compare groups (P < 0.05). Spearman rank correlation was performed to determine the association of circulating sST2 levels with biomarker levels and in-hospital clinical outcomes. RESULTS: Circulating sST2 levels of the nonsurvivor cohort were statistically significantly elevated at 12 h after injury and remained elevated up to D5 when compared either to the overall 472 survivor cohort or a matched 19 survivor subcohort. Admission sST2 levels obtained from the first blood draw after injury in the survivor cohort correlated positively with admission base deficit (correlation coefficient [CC] = 0.1; P = 0.02), international normalized ratio (CC = 0.1, P = 0.03), ISS (CC = 0.1, P = 0.008), and the average Marshall multiple organ dysfunction score between D2 and D5 (CC = 0.1, P = 0.04). Correlations with ISS revealed a positive correlation of ISS with plasma sST2 levels across the mild ISS (CC = 0.47, P < 0.001), moderate ISS (CC = 0.58, P < 0.001), and severe ISS groups (CC = 0.63, P < 0.001). Analysis of biomarker correlations in the matched survivor group over the initial 24 h after injury showed that sST2 correlates strongly and positively with IL-4 (CC = 0.65, P = 0.002), IL-5 (CC = 0.57, P = 0.01), IL-21 (CC = 0.52, P = 0.02), IL-2 (CC = 0.51, P = 0.02), soluble IL-2 receptor-α (CC = 0.5, P = 0.02), IL-13 (CC = 0.49, P = 0.02), and IL-17A (CC = 0.48, P = 0.03). This was not seen in the matched nonsurvivor group. sST2/IL-33 ratios were significantly elevated in nonsurvivors and patients with severe injury based on ISS ≥ 25. CONCLUSIONS: Elevations in serum sST2 levels are associated with poor clinical trajectories and mortality after blunt trauma. High sST2 coupled with low IL-33 associates with severe injury, mortality, and worse clinical outcomes. These findings suggest that sST2 could serve as an early prognostic biomarker in trauma patients and that sustained elevations of sST2 could contribute to a detrimental suppression of IL-33 bioavailability in patients with high injury severity.

Hepatic Encephalopathy in Children With Acute Liver Failure: Utility of Serum Neuromarkers.

BACKGROUND: Pediatric acute liver failure (PALF) is a public heath burden, often requiring prolonged hospitalization and liver transplantation. Hepatic encephalopathy (HE) is a complication of PALF with limited diagnostic tools to predict outcomes. Serum neurological markers (neuron-specific enolase, S100ß, and myelin basic protein) can be elevated in traumatic or ischemic brain injury. We hypothesized that these neuromarkers would be associated with the development of HE in PALF. METHODS: PALF study participants enrolled between May 2012 and December 2014 by 12 participating centers were the subjects of this analysis. Daily HE assessments were determined by study investigators. Neurological and inflammatory markers were measured using enzyme-linked immunosorbent assay and MILLIPLEX techniques, respectively. To model encephalopathy, these markers were log2 transformed and individually examined for association with HE using a generalized linear mixed model with a logit link and random intercept. RESULTS: Eighty-two children had neurological and inflammatory marker levels and HE assessments recorded, with the majority having assessments for 3 days during their illness. An indeterminate diagnosis (29%) was most common and the median age was 2.9 years. Significant associations were observed for HE with S100ß (odds ratio 1.16, 95% confidence interval [1.03-1.29], P = 0.04) and IL-6 (odds ratio 1.24 [1.11-1.38], P = 0.006). CONCLUSIONS: Serum S100ß and IL-6 are associated with HE in children with PALF. Measuring these markers may assist in assessing neurological injury in PALF, impacting clinical decisions.

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis.

Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures: All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures: Derived phenotype (α, ß, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results: The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the ß phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the ß phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

Prospective Analyses of Cytokine Mediation of Sleep and Survival in the Context of Advanced Cancer.

OBJECTIVE: The aims of this study were to examine the potential association between sleep problems, symptom burden, and survival in patients with advanced cancer. METHODS: A prospective study of 294 patients with gastrointestinal cancer administered questionnaires assessing sleep, depression, anxiety, stress, pain, fatigue, and health-related quality of life. Serum levels of cytokines including interleukin (IL)-1α, IL-1ß, tumor necrosis factor α, IL-10, IL-2, and interferon-γ were measured to assess biological mediation between sleep and survival. Survival was measured as time from diagnosis to death. RESULTS: Fifty-nine percent of patients reported poor sleep quality, 53% reported poor sleep efficiency, 39% reported sleep latency greater than 30 minutes, and 45% reported sleeping less than 6 hours or greater than 10 hours. We found a significant association between sleep duration and symptom burden. Shorter sleep duration was significantly associated with higher levels of fatigue (r = -0.169, p = .01), pain (r = -0.302, p = .01), anxiety (r = -0.182, p = .01), depression (r = -0.172, p = .003), and lower levels of quality of life (r = 0.240, p = .01). After adjustment for demographic, psychological, and disease-specific factors, short sleep duration was associated with reduced survival (hazard ratio [HR] linear = 0.485, 95% confidence interval = 0.275-0.857) and there was also evidence for a quadratic pattern (HR quadrati = 1.064, 95% confidence interval = 1.015-1.115) suggesting a curvilinear relationship between sleep duration and survival. Interleukin 2 was the only cytokine significantly related to survival (HR = 1.01, p = .003) and sleep duration (ß = -30.11, p = .027). When of IL-2 was added to the multivariable model, short and long sleep (ß = -0.557, p = .097; ß = 0.046, p = .114) were no longer significantly related to survival, suggesting mediation by IL-2. CONCLUSION: Sleep duration was associated with symptom burden and poorer survival and IL-2 was found to mediate the association between sleep and survival. Screening and treatment of sleep problems in patients diagnosed with cancer are warranted.

Time for trauma immunology.

Timothy Billiar summarizes the role of the innate immune response in the clinical course following severe injury and envisions a field of "trauma immunology."