Biomarker Evidence of the Persistent Inflammation, Immunosuppression and Catabolism Syndrome (PICS) in Chronic Critical Illness (CCI) After Surgical Sepsis

Risk factors, biomarkers, and mechanisms for persistent inflammation, immunosuppression, and catabolism syndrome (PICS): a systematic review and meta-analysis

INTRODUCTION

Persistent inflammation, immunosuppression, and catabolism syndrome (PICS) has been proposed as an endotype of chronic critical illness (CCI). The aim of this systematic review is to synthesise the available evidence of risk factors, biomarkers, and biological mechanisms underlying PICS.

METHODS

MEDLINE, CENTRAL, and EMBASE were searched on June 2, 2023. Our population of interest was adult intensive care unit survivors. The exposure group was patients with PICS and the comparator group was patients with no PICS, CCI, or rapid recovery. Mean differences were pooled for each biomarker using a random effects DerSimonian-Laird method. Risk of bias assessment was done using the Newcastle-Ottawa Scale.

RESULTS

Six papers were included. Five were single-centre retrospective cohort studies, and one was a prospective cohort study, with sample sizes ranging from 22 to 391 patients. Two studies showed an increased incidence of PICS with age, and two studies showed an association between PICS and Charlson Comorbidity Index scores. PICS was associated with requiring mechanical ventilation in four studies. Meta-analysis showed a 34.4 mg L-1 higher C-reactive protein (95% confidence interval [CI] 12.7-56.2 mg L-1; P<0.01), a 4.4 g L-1 lower albumin (95% CI 0.5-8.3 g L-1; P<0.01), and a 0.36×109 L-1 lower lymphocyte count (95% CI 0.25-0.47×109 L-1; P=0.01) in the PICS compared with the non-PICS group. There are a large variety of other potential biomarkers but limited validation studies. The overall quality of evidence is limited, and these results should be interpreted accordingly.

CONCLUSIONS

While older patients and those with co-morbidities could be at greater risk for PICS, acquired risk factors, such as injury severity, are potentially more predictive of PICS than intrinsic patient characteristics. There are many potential biomarkers for PICS, but limited validation studies have been conducted. Persistent myeloid-derived suppressor cell expansion, the continual release of danger-associated molecular patterns and pathogen-associated molecular patterns propagating inflammation, and bioenergetic failure are all mechanisms underlying PICS that could offer potential for novel biomarkers and therapeutic interventions.

CLINICAL TRIAL REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO; CRD42023427749).

α-Ketoglutarate plays an inflammatory inhibitory role by regulating scavenger receptor class a expression through N6-methyladenine methylation during sepsis

Sepsis arises from an uncontrolled inflammatory response triggered by infection or stress, accompanied by alteration in cellular energy metabolism, and a strong correlation exists between these factors. Alpha-ketoglutarate (α-KG), an intermediate product of the TCA cycle, has the potential to modulate the inflammatory response and is considered a crucial link between energy metabolism and inflammation. The scavenger receptor (SR-A5), a significant pattern recognition receptor, assumes a vital function in anti-inflammatory reactions. In the current investigation, we have successfully illustrated the ability of α-KG to mitigate inflammatory factors in the serum of septic mice and ameliorate tissue damage. Additionally, α-KG has been shown to modulate metabolic reprogramming and macrophage polarization. Moreover, our findings indicate that the regulatory influence of α-KG on sepsis is mediated through SR-A5. We also elucidated the mechanism by which α-KG regulates SR-A5 expression and found that α-KG reduced the N6-methyladenosine level of macrophages by up-regulating the m6A demethylase ALKBH5. α-KG plays a crucial role in inhibiting inflammation by regulating SR-A5 expression through m6A demethylation during sepsis. The outcomes of this research provide valuable insights into the relationship between energy metabolism and inflammation regulation, as well as the underlying molecular regulatory mechanism.

Association between inflammation and post-intensive care syndrome: a systematic review

Post-intensive care syndrome describes the physical, cognitive and emotional symptoms which persist following critical illness. At present there is limited understanding of the pathological mechanisms contributing to the development of post-intensive care syndrome. The aim of this systematic review was to synthesise current evidence exploring the association between inflammation and features of post-intensive care syndrome in survivors of critical illness. Relevant databases were systematically searched for studies of human participants exposed to critical illness. We sought studies that reported results for biomarkers with an identified role in the pathophysiology of inflammation obtained at any time-point in the patient journey and an outcome measure of any feature of post-intensive care syndrome at any point following hospital discharge. We included 32 studies, with 23 in the primary analysis and nine in a brain injury subgroup analysis. In the primary analysis, 47 different biomarkers were sampled and 44 different outcome measures were employed. Of the biomarkers which were sampled in five or more studies, interleukin-8, C-reactive protein and interleukin-10 most frequently showed associations with post-intensive care syndrome outcomes in 71%, 62% and 60% of studies, respectively. There was variability in terms of which biomarkers were sampled, time-points of sampling and outcome measures reported. Overall, there was mixed evidence of a potential association between an inflammatory process and long-term patient outcomes following critical illness. Further high-quality research is required to develop a longitudinal inflammatory profile of survivors of critical illness over the recovery period and evaluate the association with outcomes.

The Tri-Steps Model of Critical Conditions in Intensive Care: Introducing a New Paradigm for Chronic Critical Illness

Background: The prevailing model for understanding chronic critical illness is a biphasic model, suggesting phases of acute and chronic critical conditions. A major challenge within this model is the difficulty in determining the timing of the process chronicity. It is likely that the triad of symptoms (inflammation, catabolism, and immunosuppression [ICIS]) could be associated with this particular point. We aimed to explore the impact of the symptom triad (inflammation, catabolism, immunosuppression) on the outcomes of patients hospitalized in intensive care units (ICUs). Methods: The eICU-CRD database with 200,859 ICU admissions was analyzed. Adult patients with the ICIS triad, identified by elevated CRP (>20 mg/L), reduced albumin (<30 g/L), and low lymphocyte counts (<0.8 × 109/L), were included. The cumulative risk of developing ICIS was assessed using the Nelson-Aalen estimator. Results: This retrospective cohort study included 894 patients (485 males, 54%), with 60 (6.7%) developing ICIS. The cumulative risk of ICIS by day 21 was 22.5%, with incidence peaks on days 2-3 and 10-12 after ICU admission. Patients with the ICIS triad had a 2.5-fold higher mortality risk (p = 0.009) and double the likelihood of using vasopressors (p = 0.008). The triad onset day did not significantly affect mortality (p = 0.104). Patients with ICIS also experienced extended hospital (p = 0.041) and ICU stays (p < 0.001). Conclusions: The symptom triad (inflammation, catabolism, immunosuppression) during hospitalization increases mortality risk by 2.5 times (p = 0.009) and reflects the chronicity of the critical condition. Identifying two incidence peaks allows the proposal of a new Tri-steps model of chronic critical illness with acute, extended, and chronic phases.

The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells

Introduction

Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness.

Methods

Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering.

Results

We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome.

Discussion

The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.

CHRONIC CRITICAL ILLNESS-INDUCED MUSCLE ATROPHY: INSIGHTS FROM A TRAUMA MOUSE MODEL AND POTENTIAL MECHANISM MEDIATED VIA SERUM AMYLOID A

ABSTRACT

Background: Chronic critical illness (CCI), which was characterized by persistent inflammation, immunosuppression, and catabolism syndrome (PICS), often leads to muscle atrophy. Serum amyloid A (SAA), a protein upregulated in critical illness myopathy, may play a crucial role in these processes. However, the effects of SAA on muscle atrophy in PICS require further investigation. This study aims to develop a mouse model of PICS combined with bone trauma to investigate the mechanisms underlying muscle weakness, with a focus on SAA. Methods: Mice were used to examine the effects of PICS after bone trauma on immune response, muscle atrophy, and bone healing. The mice were divided into two groups: a bone trauma group and a bone trauma with cecal ligation and puncture group. Tibia fracture surgery was performed on all mice, and PICS was induced through cecal ligation and puncture surgery in the PICS group. Various assessments were conducted, including weight change analysis, cytokine analysis, hematological analysis, grip strength analysis, histochemical staining, and immunofluorescence staining for SAA. In vitro experiments using C2C12 cells (myoblasts) were also conducted to investigate the role of SAA in muscle atrophy. The effects of inhibiting receptor for advanced glycation endproducts (RAGE) or JAK2 on SAA-induced muscle atrophy were examined. Bioinformatic analysis was conducted using a dataset from the GEO database to identify differentially expressed genes and construct a coexpression network. Results: Bioinformatic analysis confirmed that SAA was significantly upregulated in muscle tissue of patients with intensive care unit-induced muscle atrophy. The PICS animal models exhibited significant weight loss, spleen enlargement, elevated levels of proinflammatory cytokines, and altered hematological profiles. Evaluation of muscle atrophy in the animal models demonstrated decreased muscle mass, grip strength loss, decreased diameter of muscle fibers, and significantly increased expression of SAA. In vitro experiment demonstrated that SAA decreased myotube formation, reduced myotube diameter, and increased the expression of muscle atrophy-related genes. Furthermore, SAA expression was associated with activation of the FOXO signaling pathway, and inhibition of RAGE or JAK2/STAT3-FOXO signaling partially reversed SAA-induced muscle atrophy. Conclusions: This study successfully develops a mouse model that mimics PICS in CCI patients with bone trauma. Serum amyloid A plays a crucial role in muscle atrophy through the JAK2/STAT3-FOXO signaling pathway, and targeting RAGE or JAK2 may hold therapeutic potential in mitigating SAA-induced muscle atrophy.

Isolation and microbial transformation of tea sapogenin from seed pomace of Camellia oleifera with anti-inflammatory effects

In the current study, tea saponin, identified as the primary bioactive constituent in seed pomace of Camellia oleifera Abel., was meticulously extracted and hydrolyzed to yield five known sapogenins: 16-O-tiglogycamelliagnin B (a), camelliagnin A (b), 16-O-angeloybarringtogenol C (c), theasapogenol E (d), theasapogenol F (e). Subsequent biotransformation of compound a facilitated the isolation of six novel metabolites (a1-a6). The anti-inflammatory potential of these compounds was assessed using pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns molecules (DAMPs)-mediated cellular inflammation models. Notably, compounds b and a2 demonstrated significant inhibitory effects on both lipopolysaccharide (LPS) and high-mobility group box 1 (HMGB1)-induced inflammation, surpassing the efficacy of the standard anti-inflammatory agent, carbenoxolone. Conversely, compounds d, a3, and a6 selectivity targeted endogenous HMGB1-induced inflammation, showcasing a pronounced specificity. These results underscore the therapeutic promise of C. oleifera seed pomace-derived compounds as potent agents for the management of inflammatory diseases triggered by infections and tissue damage.

Adverse outcomes and an immunosuppressed endotype in septic patients with reduced IFN-γ ELISpot

BACKGROUNDSepsis remains a major clinical challenge for which successful treatment requires greater precision in identifying patients at increased risk of adverse outcomes requiring different therapeutic approaches. Predicting clinical outcomes and immunological endotyping of septic patients generally relies on using blood protein or mRNA biomarkers, or static cell phenotyping. Here, we sought to determine whether functional immune responsiveness would yield improved precision.METHODSAn ex vivo whole-blood enzyme-linked immunosorbent spot (ELISpot) assay for cellular production of interferon γ (IFN-γ) was evaluated in 107 septic and 68 nonseptic patients from 5 academic health centers using blood samples collected on days 1, 4, and 7 following ICU admission.RESULTSCompared with 46 healthy participants, unstimulated and stimulated whole-blood IFN-γ expression was either increased or unchanged, respectively, in septic and nonseptic ICU patients. However, in septic patients who did not survive 180 days, stimulated whole-blood IFN-γ expression was significantly reduced on ICU days 1, 4, and 7 (all P < 0.05), due to both significant reductions in total number of IFN-γ-producing cells and amount of IFN-γ produced per cell (all P < 0.05). Importantly, IFN-γ total expression on days 1 and 4 after admission could discriminate 180-day mortality better than absolute lymphocyte count (ALC), IL-6, and procalcitonin. Septic patients with low IFN-γ expression were older and had lower ALCs and higher soluble PD-L1 and IL-10 concentrations, consistent with an immunosuppressed endotype.CONCLUSIONSA whole-blood IFN-γ ELISpot assay can both identify septic patients at increased risk of late mortality and identify immunosuppressed septic patients.TRIAL REGISTRYN/A.FUNDINGThis prospective, observational, multicenter clinical study was directly supported by National Institute of General Medical Sciences grant R01 GM-139046, including a supplement (R01 GM-139046-03S1) from 2022 to 2024.

Association Between Plasma Levels of Monocyte Chemoattractant Protein-1 (MCP-1) and 28-Day Mortality in Elderly Patients with Sepsis: A Retrospective Single-Center Study

BACKGROUND Previous studies have identified an association between plasma levels of the inflammatory cytokine, monocyte chemoattractant protein-1 (MCP-1), and outcomes for patients with sepsis. This retrospective single-center study assessed the association between plasma levels of MCP-1 and 28-day mortality in 136 patients ≥65 years diagnosed with sepsis between October 2020 and October 2021. MATERIAL AND METHODS The objective was to compare and analyze the parameters in the survival group (n=35) and the 28-day mortality group (n=101), including Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), plasma MCP-1, and laboratory test results. Plasma MCP-1 was quantified by cytokine test kit (LKTM014B, R&D). Statistical analysis was carried out in SPSS 26.0 and MedCalc 92.1.0 software. RESULTS The 28-day mortality group exhibited higher levels of SOFA, APACHEII, and plasma MCP-1 (all P<0.001), as well as lower levels of albumin, compared to the survival group (P<0.05). The logistic regression analysis findings indicated that SOFA, APACHEII, plasma MCP-1, and SBP are all independent risk factors for 28-day mortality. The area under the curve for SOFA, APACHEII, MCP-1, MCP-1+ SOFA, and MCP-1+APACHEII were 0.845, 0.744, 0.712, 0.879, and 0.822, respectively. MCP-1+SOFA exhibited higher sensitivity than SOFA alone. Furthermore, the assessment values of plasma MCP-1 combined with SOFA were superior to those of APACHE II or plasma MCP-1 (Z₁=2.661, Z₂=3.272, both P<0.01). CONCLUSIONS The findings from this study from a single center support those of previous studies that increased plasma levels of MCP-1 are significantly associated with 28-day mortality in patients with sepsis.

Longitudinal study evaluating post-ICU syndrome differences between acute care surgery and trauma SICU survivors

BACKGROUND

Post-intensive care unit (ICU) syndrome (PICS) occurs at an exorbitant rate in surgical ICU (SICU) survivors. It remains unknown if critical illness due to trauma versus acute care surgery (ACS) may represent different pathophysiologic entities. In this longitudinal study, we determined if admission criteria in a cohort of trauma and ACS patients were associated with differences in the occurrence of PICS.

METHODS

Patients were 18 years or older, admitted to a Level I trauma center to the trauma or ACS services, remained in the SICU for ≥72 hours, and were seen in an ICU Recovery Center at 2 weeks, 12 weeks, and 24 weeks after hospital discharge. Post-ICU syndrome sequelae were diagnosed by dedicated specialist staffing using clinical criteria and screening questionnaires. The PICS symptoms were distilled into physical, cognitive, and psychiatric categories. Preadmission histories, hospital courses, and recovery data were collected via retrospective chart review.

RESULTS

One hundred twenty-six patients were included: 74 (57.3%) trauma patients and 55 (42.6%) ACS patients. Prehospital psychosocial histories were similar between groups. Acute care surgery patients had a significantly longer hospital course, higher APACHE II and III scores, were intubated for longer, and had higher rates of sepsis, acute renal failure, open abdomen, and hospital readmissions. At the 2-week follow-up visit, ACS patients had higher rates of PICS sequelae (ACS, 97.8% vs. trauma 85.3%; p = 0.03), particularly in the physical (ACS, 95.6% vs. trauma 82.0%, p = 0.04), and psychiatric domains (ACS, 55.6% vs. trauma 35.0%, p = 0.04). At the 12-week and 24-week visits, rates of PICS symptoms were comparable between groups.

CONCLUSION

The occurrence of PICS is extraordinarily high in both trauma and ACS SICU survivors. Despite entering the SICU with similar psychosocial histories, the two cohorts have different pathophysiologic experiences, which are associated with a higher rate of impairment in the ACS patients during early follow-up.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level III.

The persistent inflammation, immunosuppression, and catabolism syndrome 10 years later

With the implementation of new intensive care unit (ICU) therapies in the 1970s, multiple organ failure (MOF) emerged as a fulminant inflammatory phenotype leading to early ICU death. Over the ensuing decades, with fundamental advances in care, this syndrome has evolved into a lingering phenotype of chronic critical illness (CCI) leading to indolent late post-hospital discharge death. In 2012, the University of Florida (UF) Sepsis Critical Illness Research Center (SCIRC) coined the term Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) to provide a mechanistic framework to study CCI in surgical patients. This was followed by a decade of research into PICS-CCI in surgical ICU patients in order to define the epidemiology, dysregulated immunity, and long-term outcomes after sepsis. Other focused studies were performed in trauma ICU patients and emergency department sepsis patients. Early deaths were surprisingly low (4%); 63% experienced rapid recovery. Unfortunately, 33% progressed to CCI, of which 79% had a poor post-discharge disposition and 41% were dead within one year. These patients had biomarker evidence of PICS, and these biomarkers enhanced clinical prediction models for dismal one-year outcomes. Emergency myelopoiesis appears to play a central role in the observed persistent immune dysregulation that characterizes PICS-CCI. Older patients were especially vulnerable. Disturbingly, over half of the older CCI patients were dead within one year and older CCI survivors remained severely disabled. Although CCI is less frequent (20%) after major trauma, PICS appears to be a valid concept. This review will specifically detail the epidemiology of CCI, PICS biomarkers, effect of site of infection, acute kidney injury, effect on older patients, dysfunctional high-density lipoproteins, sarcopenia/cachexia, emergency myelopoiesis, dysregulated erythropoiesis, and potential therapeutic interventions.

A review of UF SCIRC’s research efforts characterizing CCI, PICS biomarkers, effect of site of infection, acute kidney injury, effects on older patients, dysfunctional high-density lipoproteins, sarcopenia/cachexia, emergency myelopoiesis, and dysregulated erythropoiesis.

Development and validation of a nomogram for predicting persistent inflammation, immunosuppression, and catabolism syndrome in trauma patients

Background

Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PIICS) is a significant contributor to adverse long-term outcomes in severe trauma patients.

Objective

The objective of this study was to establish and validate a PIICS predictive model in severe trauma patients, providing a practical tool for early clinical prediction.

Patients and methods

Adult severe trauma patients with an Injury Severity Score (ISS) of ≥16, admitted between October 2020 and December 2022, were randomly divided into a training set and a validation set in a 7:3 ratio. Patients were classified into PIICS and non-PIICS groups based on diagnostic criteria. LASSO regression was used to select appropriate variables for constructing the prognostic model. A logistic regression model was developed and presented in the form of a nomogram. The performance of the model was evaluated using calibration and ROC curves.

Results

A total of 215 patients were included, consisting of 155 males (72.1%) and 60 females (27.9%), with a median age of 51 years (range: 38-59). NRS2002, ISS, APACHE II, and SOFA scores were selected using LASSO regression to construct the prognostic model. The AUC of the ROC analysis for the predictive model in the validation set was 0.84 (95% CI 0.72-0.95). The Hosmer-Lemeshow test in the validation set yielded a χ2 value of 14.74, with a value of p of 0.098.

Conclusion

An accurate and easily implementable PIICS risk prediction model was established. It can enhance risk stratification during hospitalization for severe trauma patients, providing a novel approach for prognostic prediction.

Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study

Background

The great heterogeneity of patients with chronic critical illness (CCI) leads to difficulty for intensive care unit (ICU) management. Identifying subphenotypes could assist in individualized care, which has not yet been explored. In this study, we aim to identify the subphenotypes of patients with CCI and reveal the heterogeneous treatment effect of fluid balance for them.

Methods

In this retrospective study, we defined CCI as an ICU length of stay over 14 days and coexists with persistent organ dysfunction (cardiovascular Sequential Organ Failure Assessment (SOFA) score ≥1 or score in any other organ system ≥2) at Day 14. Data from five electronic healthcare record datasets covering geographically distinct populations (the US, Europe, and China) were studied. These five datasets include (1) subset of Derivation (MIMIC-IV v1.0, US) cohort (2008-2019); (2) subset Derivation (MIMIC-III v1.4 'CareVue', US) cohort (2001-2008); (3) Validation I (eICU-CRD, US) cohort (2014-2015); (4) Validation II (AmsterdamUMCdb/AUMC, Euro) cohort (2003-2016); (5) Validation III (Jinling, CN) cohort (2017-2021). Patients who meet the criteria of CCI in their first ICU admission period were included in this study. Patients with age over 89 or under 18 years old were excluded. Three unsupervised clustering algorithms were employed independently for phenotypes derivation and validation. Extreme Gradient Boosting (XGBoost) was used for phenotype classifier construction. A parametric G-formula model was applied to estimate the cumulative risk under different daily fluid management strategies in different subphenotypes of ICU mortality.

Findings

We identified four subphenotypes as Phenotype A, B, C, and D in a total of 8145 patients from three countries. Phenotype A is the mildest and youngest subgroup; Phenotype B is the most common group, of whom patients showed the oldest age, significant acid-base abnormality, and low white blood cell count; Patients with Phenotype C have hypernatremia, hyperchloremia, and hypercatabolic status; and in Phenotype D, patients accompany with the most severe multiple organ failure. An easy-to-use classifier showed good effectiveness. Phenotype characteristics showed robustness across all cohorts. The beneficial fluid balance threshold intervals of subphenotypes were different.

Interpretation

We identified four novel phenotypes that revealed the different patterns and significant heterogeneous treatment effects of fluid therapy within patients with CCI. A prospective study is needed to validate our findings, which could inform clinical practice and guide future research on individualized care.

Funding

This study was funded by 333 High Level Talents Training Project of Jiangsu Province (BRA2019011), General Program of Medical Research from the Jiangsu Commission of Health (M2020052), and Key Research and Development Program of Jiangsu Province (BE2022823).

Biomarkers of neuropsychiatric dysfunction in intensive care unit survivors: a prospective cohort study

OBJECTIVE

To assess factors associated with long-term neuropsychiatric outcomes, including biomarkers measured after discharge from the intensive care unit.

METHODS

A prospective cohort study was performed with 65 intensive care unit survivors. The cognitive evaluation was performed through the Mini-Mental State Examination, the symptoms of anxiety and depression were evaluated using the Hospital Anxiety and Depression Scale, and posttraumatic stress disorder was evaluated using the Impact of Event Scale-6. Plasma levels of amyloid-beta (1-42) [Aβ (1-42)], Aβ (1-40), interleukin (IL)-10, IL-6, IL-33, IL-4, IL-5, tumor necrosis factor alpha, C-reactive protein, and brain-derived neurotrophic factor were measured at intensive care unit discharge.

RESULTS

Of the variables associated with intensive care, only delirium was independently related to the occurrence of long-term cognitive impairment. In addition, higher levels of IL-10 and IL-6 were associated with cognitive dysfunction. Only IL-6 was independently associated with depression. Mechanical ventilation, IL-33 levels, and C-reactive protein levels were independently associated with anxiety. No variables were independently associated with posttraumatic stress disorder.

CONCLUSION

Cognitive dysfunction, as well as symptoms of depression, anxiety, and posttraumatic stress disorder, are present in patients who survive a critical illness, and some of these outcomes are associated with the levels of inflammatory biomarkers measured at discharge from the intensive care unit.

Increased ten-eleven translocation methylcytosine dioxygenase one in dorsal root ganglion contributes to inflammatory pain in CFA rats

DNA hydroxylation catalyzed by Tet dioxygenases occurs abundantly in neurons in mammals. However, effects of ten-eleven translocation methylcytosine dioxygenase 1 (TET1) expression and hydroxymethylation status on neuron injury remain unclear. This study was designed to explore the effects of TET1 and TET2 expression in the inflammatory pain of rats induced by complete Freund's adjuvant (CFA). Mechanical paw withdrawal threshold (PWT) and thermal withdrawal latency (TWL) were detected to assess pain behavior. The expression of TET1 and TET2 were measured in the dorsal root ganglion (DRG) with western blotting analysis. Immunofluorescence staining is employed to detect the expression and co-location of TRPV1 with TET1. Intrathecal administration of Bobcat339 was used to inhibit TET1 function in dorsal root ganglion. The paw withdrawal threshold and thermal withdrawal latency of rats were significantly reduced after CFA Injection. Western blot results showed that the expression of TET1 was significantly increased at 3 days after CFA injection, but TET2 had no statistical difference. Immunofluorescence results showed that TET1 was co-localized with TRPV1. Intrathecal administration of Bobcat339 improved mechanical and thermal pain threshold in CFA rats. Our findings highlight the role of TET1 in chronic inflammatory pain model. The expression of TET1 was increased in CFA rats, and suppression of TET1 will ameliorate inflammatory pain.

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques

BACKGROUND

Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, and to construct a reliable prediction model for patients who had suffered orthopedic trauma using machine learning techniques.

METHODS

This study enrolled 1257 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Lymphocytes were tracked from ICU admission to more than 20 days following admission to examine the dynamic changes over time. Over 40 possible variables were gathered for investigation. Patients were split 80:20 at random into a training cohort (n=1035) and an internal validation cohort (n=222). Four machine learning algorithms, including random forest, gradient boosting machine, decision tree, and support vector machine, and a logistic regression technique were utilized to train and optimize models using data from the training cohort. Patients in the internal validation cohort were used to validate models, and the optimal one was chosen. Patients from two large teaching hospitals were used for external validation (n=113). The key metrics that used to assess the prediction performance of models mainly included discrimination, calibration, and clinical usefulness. To encourage clinical application based on the optimal machine learning-based model, a web-based calculator was developed.

RESULTS

16.0% (201/1257) of all patients had PerCI in the MIMIC-III database. The means of lymphocytes (%) were consistently below the normal reference range across the time among PerCI patients (around 10.0%), whereas in patients without PerCI, the number of lymphocytes continued to increase and began to be in normal range on day 10 following ICU admission. Subgroup analysis demonstrated that patients with PerCI were in a more serious health condition at admission since those patients had worse nutritional status, more electrolyte imbalance and infection-related comorbidities, and more severe illness scores. Eight variables, including albumin, serum calcium, red cell volume distributing width (RDW), blood pH, heart rate, respiratory failure, pneumonia, and the Sepsis-related Organ Failure Assessment (SOFA) score, were significantly associated with PerCI, according to the least absolute shrinkage and selection operator (LASSO) logistic regression model combined with the 10-fold cross-validation. These variables were all included in the modelling. In comparison to other algorithms, the random forest had the optimal prediction ability with the highest area under receiver operating characteristic (AUROC) (0.823, 95% CI: 0.757-0.889), highest Youden index (1.571), and lowest Brier score (0.107). The AUROC in the external validation cohort was also up to 0.800 (95% CI: 0.688-0.912). Based on the risk stratification system, patients in the high-risk group had a 10.0-time greater chance of developing PerCI than those in the low-risk group. A web-based calculator was available at https://starxueshu-perci-prediction-main-9k8eof.streamlitapp.com/.

CONCLUSIONS

Patients with PerCI typically remain in an immunosuppressive status, but those without PerCI gradually regain normal immunity. The dynamic changes of lymphocytes can be a reliable biomarker for PerCI. This work developed a reliable model that may be helpful in improving early diagnosis and targeted intervention of PerCI. Beneficial interventions, such as improving nutritional status and immunity, maintaining electrolyte and acid-base balance, curbing infection, and promoting respiratory recovery, are early warranted to prevent the onset of PerCI, especially among patients in the high-risk group and those with a continuously low level of lymphocytes.

Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors

Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors manifest unique blood leukocyte transcriptomes in late sepsis that differ from transcriptomes among sepsis survivors with rapid recovery. In a prospective cohort study of surgical ICU patients, genome-wide expression analysis was conducted on total leukocytes in human whole blood collected on days 1 and 14 from sepsis survivors who rapidly recovered or developed CCI, defined as ICU length of stay ≥ 14 days with persistent organ dysfunction. Both sepsis patients who developed CCI and those who rapidly recovered exhibited marked changes in genome-wide expression at day 1 which remained abnormal through day 14. Although summary changes in gene expression were similar between CCI patients and subjects who rapidly recovered, CCI patients exhibited differential expression of 185 unique genes compared with rapid recovery patients at day 14 (p < 0.001). The transcriptomic patterns in sepsis survivors reveal an ongoing immune dyscrasia at the level of the blood leukocyte transcriptome, consistent with persistent inflammation and immune suppression. Furthermore, the findings highlight important genes that could compose a prognostic transcriptomic metric or serve as therapeutic targets among sepsis patients that develop CCI.