Subphenotypes in critical care: translation into clinical practice

Aiming for precision: personalized medicine through sepsis subtyping

According to the latest definition, sepsis is characterized by life-threatening organ dysfunction caused by a dysregulated host response to an infection. However, this definition fails to grasp the heterogeneous nature and the underlying dynamic pathophysiology of the syndrome. In response to this heterogeneity, efforts have been made to stratify sepsis patients into subtypes, either based on their clinical presentation or pathophysiological characteristics. Subtyping introduces the possibility of the implementation of personalized medicine, whereby each patient receives treatment tailored to their individual disease manifestation. This review explores the currently known subtypes, categorized by subphenotypes and endotypes, as well as the treatments that have been researched thus far in the context of sepsis subtypes and personalized medicine.

Molecular phenotypes of critical illness confer prognostic and biological enrichment in sub-Saharan Africa: a prospective cohort study from Uganda

The generalisability of critical illness molecular phenotypes to low- and middle-income countries (LMICs) is unknown. We show that molecular phenotypes derived in high-income countries (hyperinflammatory and hypoinflammatory, reactive and uninflamed) stratify sepsis patients in Uganda by physiological severity, mortality risk and dysregulation of key pathobiological domains. A classifier model including data available at the LMIC bedside modestly discriminated phenotype assignment (area under the receiver operating characteristic curve (AUROC) 0.80, 95% CI 0.71 to 0.90 for hyperinflammatory vs hypoinflammatory; AUROC 0.74, 95% CI 0.65 to 0.83 for reactive vs uninflamed). Our findings highlight the potential for a globally relevant, clinicomolecular classification of critical illness and may support the inclusion of diverse populations in phenotype-targeted critical care trials. Improved laboratory capacity and access to rapid biomarker assays are likely necessary to optimise phenotype stratification in LMIC settings.

Acute kidney injury subphenotyping and personalized medicine

PURPOSE OF REVIEW

This review discusses novel concepts of acute kidney injury (AKI), including subphenotyping, which may facilitate the development of target treatment strategies for specific subgroups of patients to achieve precision medicine.

RECENT FINDINGS

AKI is a multifaceted syndrome with a major impact on morbidity and mortality. As efforts to identify treatment strategies have largely failed, it is becoming increasingly apparent that there are different subphenotypes that require different treatment strategies. Various ways of subphenotyping AKI have been investigated, including the use of novel renal biomarkers, machine learning and artificial intelligence, some of which have already been implemented in the clinical setting. Thus, novel renal biomarkers have been recommended for inclusion in new definition criteria for AKI and for the use of biomarker bundled strategies for the prevention of AKI. Computational models have been explored and require future research.

SUMMARY

Subphenotyping of AKI may provide a new understanding of this syndrome and guide targeted treatment strategies in order to improve patient outcomes.

Identification of CRTH2 as a New PPARγ-Target Gene in T Cells Suggested CRTH2 Dependent Conversion of Th2 Cells as Therapeutic Concept in COVID-19 Infection

Background

COVID-19 is a serious viral infection, which is often associated with a lethal outcome. Therefore, understanding mechanisms, which affect the immune response during SARS-CoV2 infection, are important.

Methods

To address this, we determined the number of T cells in peripheral blood derived from intensive care COVID-19 patients. Based on our previous studies, evaluating PPARγ-dependent T cell apoptosis in sepsis patients, we monitored PPARγ expression. We performed a next generation sequencing approach to identify putative PPARγ-target genes in Jurkat T cells and used a PPARγ transactivation assay in HEK293T cells. Finally, we translated these data to primary T cells derived from healthy donors.

Results

A significantly reduced count of total CD3+ T lymphocytes and the CD4+ and CD8+ subpopulations was observed. Also, the numbers of anti-inflammatory, resolutive Th2 cells and FoxP3-positive regulatory T cells (Treg) were decreased. We observed an augmented PPARγ expression in CD4+ T cells of intensive care COVID-19 patients. Adapted from a next generation sequencing approach in Jurkat T cells, we found the chemoattractant receptor-homologous molecule expressed on T helper type 2 cells (CRTH2) as one gene regulated by PPARγ in T cells. This Th2 marker is a receptor for prostaglandin D and its metabolic degradation product 15-deoxy-∆12,14-prostaglandin J2 (15d-PGJ2), an established endogenous PPARγ agonist. In line, we observed an increased PPARγ transactivation in response to 15d-PGJ2 treatment in HEK293T cells overexpressing CRTH2. Translating these data to primary T cells, we found that Th2 differentiation was associated with an increased expression of CRTH2. Interestingly, these CRTH2+ T cells were prone to apoptosis.

Conclusion

These mechanistic data suggest an involvement of PPARγ in Th2 differentiation and T cell depletion in COVID-19 patients.

Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64-0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752-0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

Rapid and Robust Identification of Sepsis Using SeptiCyte RAPID in a Heterogeneous Patient Population

Background/Objective: SeptiCyte RAPID is a transcriptional host response assay that discriminates between sepsis and non-infectious systemic inflammation (SIRS) with a one-hour turnaround time. The overall performance of this test in a cohort of 419 patients has recently been described [Balk et al., J Clin Med 2024, 13, 1194]. In this study, we present the results from a detailed stratification analysis in which SeptiCyte RAPID performance was evaluated in the same cohort across patient groups and subgroups encompassing different demographics, comorbidities and disease, sources and types of pathogens, interventional treatments, and clinically defined phenotypes. The aims were to identify variables that might affect the ability of SeptiCyte RAPID to discriminate between sepsis and SIRS and to determine if any patient subgroups appeared to present a diagnostic challenge for the test. Methods: (1) Subgroup analysis, with subgroups defined by individual demographic or clinical variables, using conventional statistical comparison tests. (2) Principal component analysis and k-means clustering analysis to investigate phenotypic subgroups defined by unique combinations of demographic and clinical variables. Results: No significant differences in SeptiCyte RAPID performance were observed between most groups and subgroups. One notable exception involved an enhanced SeptiCyte RAPID performance for a phenotypic subgroup defined by a combination of clinical variables suggesting a septic shock response. Conclusions: We conclude that for this patient cohort, SeptiCyte RAPID performance was largely unaffected by key variables associated with heterogeneity in patients suspected of sepsis.

Highlights from the Respiratory Failure and Mechanical Ventilation Conference 2024

The Respiratory Intensive Care Assembly of the European Respiratory Society gathered in Berlin to organise the third Respiratory Failure and Mechanical Ventilation Conference in February 2024. The conference covered key points of acute and chronic respiratory failure in adults. During the 3-day conference ventilatory strategies, patient selection, diagnostic approaches, treatment and health-related quality of life topics were addressed by a panel of international experts. In this article, lectures delivered during the event have been summarised by early career members of the Assembly and take-home messages highlighted.

Sepsis phenotypes, subphenotypes, and endotypes: are they ready for bedside care?

PURPOSE OF REVIEW

Sepsis remains a leading global cause of morbidity and mortality, and despite decades of research, no effective therapies have emerged. The lack of progress in sepsis outcomes is related in part to the significant heterogeneity of sepsis populations. This review seeks to highlight recent literature regarding sepsis phenotypes and the potential for further research and therapeutic intervention.

RECENT FINDINGS

Numerous recent studies have elucidated various phenotypes, subphenotypes, and endotypes in sepsis. Clinical parameters including vital sign trajectories and microbial factors, biomarker investigation, and genomic, transcriptomic, proteomic, and metabolomic studies have illustrated numerous differences in sepsis populations with implications for prediction, diagnosis, treatment, and prognosis of sepsis.

SUMMARY

Sepsis therapies including care bundles, fluid resuscitation, and source control procedures may be better guided by validated phenotypes than universal application. Novel biomarkers may improve upon the sensitivity and specificity of existing markers and identify complications and sequelae of sepsis. Multiomics have demonstrated significant differences in sepsis populations, most notably expanding our understanding of immunosuppressed sepsis phenotypes. Despite progress, these findings may be limited by modest reproducibility and logistical barriers to clinical implementation. Further studies may translate recent findings into bedside care.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation

BACKGROUND

Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown.

OBJECTIVE

We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level.

METHODS

In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model.

RESULTS

The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients.

CONCLUSIONS

The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

Subphenotypes in Acute Respiratory Distress Syndrome: Universal Steps Toward Treatable Traits

Patients with acute respiratory distress syndrome (ARDS) have severe respiratory impairment requiring mechanical ventilation resulting in high mortality. Despite extensive research, no effective pharmacological interventions have been identified in unselected ARDS, which has been attributed to the considerable heterogeneity. The identification of more homogeneous subgroups through phenotyping has provided a novel method to improve our pathophysiological understanding, trial design, and, most importantly, patient care through targeted interventions. The objective of this article is to outline a structured, stepwise approach toward identifying and classifying heterogeneity within ARDS and subsequently derive, validate, and integrate targeted treatment options. We present a 6-step roadmap toward the identification of effective phenotype-targeted treatments: development of distinct and reproducible subphenotypes, derivation of a possible parsimonious bedside classification method, identification of possible interventions, prospective validation of subphenotype classification, testing of subphenotype-targeted intervention prospectively in randomized clinical trial (RCT), and finally implementation of subphenotype classification and intervention in guidelines and clinical practice. Based on this framework, the current literature was reviewed. Respiratory physiology, lung morphology, and systemic inflammatory biology subphenotypes were identified. Currently, lung morphology and systemic inflammatory biology subphenotypes are being tested prospectively in RCTs.

Biomarkers of acute kidney injury: From discovery to the future of clinical practice

Purpose of this review Acute kidney injury (AKI) is a complex syndrome whose development is associated with an increased morbidity and mortality. Recent studies show that this syndrome is a common complication in critically ill and surgical patients the trajectory of which may differ. As AKI can be induced by different triggers, it is complex and therefore challenging to manage patients with AKI. This review strives to provide a brief historical perspective on AKI, elucidate recent developments in diagnosing and managing AKI, and show the current usage of novel biomarkers in both clinical routine and research. In addition, we provide a perspective on potential future developments and their impact of AKI understanding and management. Recent findings/developments Recent studies show the merits of stress and damage biomarkers, highlighting limitations of the current KDIGO definition that only uses the functional biomarkers serum creatinine and urine output. The use of novel biomarkers led to the introduction of the concept of "subclinical AKI". This new classification may allow a more distinct management of affected or at risk patients. Ongoing studies, such as BigpAK-2 and PrevProgAKI, investigate the implementation of biomarker-guided interventions in clinical practice and may demonstrate an improvement in patients' outcome. Summary The ongoing scientific efforts surrounding AKI have deepened our understanding of the syndrome prompting an expansion of existing concepts. A future integration of stress and damage biomarkers in AKI management, may lead to an individualized therapy in this area.

Potential Impact of Subphenotyping in Pharmacologic Management of Acute Respiratory Distress Syndrome

Background: Acute respiratory distress syndrome (ARDS) is an acute inflammatory process in the lungs associated with high morbidity and mortality. Previous research has studied both nonpharmacologic and pharmacologic interventions aimed at targeting this inflammatory process and improving ventilation. Hypothesis: To date, only nonpharmacologic interventions including lung protective ventilation, prone positioning, and high positive end-expiratory pressure ventilation strategies have resulted in significant improvements in patient outcomes. Given the high mortality associated with ARDS despite these advancements, interest in subphenotyping has grown, aiming to improve diagnosis and develop personalized treatment approaches. Data Collection: Previous trials evaluating pharmacologic therapies in heterogeneous populations have primarily demonstrated no positive effect, but hope to show benefit when targeting specific subphenotypes, thus increasing their efficacy, while simultaneously decreasing adverse effects. Results: Although most studies evaluating pharmacologic therapies for ARDS have not demonstrated a mortality benefit, there is limited data evaluating pharmacologic therapies in ARDS subphenotypes, which have found promising results. Neuromuscular blocking agents, corticosteroids, and simvastatin have resulted in a mortality benefit when used in patients with the hyper-inflammatory ARDS subphenotype. Therapeutic Opinion: The use of subphenotyping could revolutionize the way ARDS therapies are applied and therefore improve outcomes while also limiting the adverse effects associated with their ineffective use. Future studies should evaluate ARDS subphenotypes and their response to pharmacologic intervention to advance this area of precision medicine.

Biomarkers in pursuit of precision medicine for acute kidney injury: hard to get rid of customs

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI's pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.

Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan

BACKGROUND

In patients who experience out-of-hospital cardiac arrest (OHCA), it is important to assess the association of sub-phenotypes identified by latent class analysis (LCA) using pre-hospital prognostic factors and factors measurable immediately after hospital arrival with neurological outcomes at 30 days, which would aid in making treatment decisions.

METHODS

This study retrospectively analyzed data obtained from the Japanese OHCA registry between June 2014 and December 2019. The registry included a complete set of data on adult patients with OHCA, which was used in the LCA. The association between the sub-phenotypes and 30-day survival with favorable neurological outcomes was investigated. Furthermore, adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by multivariate logistic regression analysis using in-hospital data as covariates.

RESULTS

A total of, 22,261 adult patients who experienced OHCA were classified into three sub-phenotypes. The factor with the highest discriminative power upon patient's arrival was Glasgow Coma Scale followed by partial pressure of oxygen. Thirty-day survival with favorable neurological outcome as the primary outcome was evident in 66.0% participants in Group 1, 5.2% in Group 2, and 0.5% in Group 3. The 30-day survival rates were 80.6%, 11.8%, and 1.3% in groups 1, 2, and 3, respectively. Logistic regression analysis revealed that the ORs (95% CI) for 30-day survival with favorable neurological outcomes were 137.1 (99.4-192.2) for Group 1 and 4.59 (3.46-6.23) for Group 2 in comparison to Group 3. For 30-day survival, the ORs (95%CI) were 161.7 (124.2-212.1) for Group 1 and 5.78 (4.78-7.04) for Group 2, compared to Group 3.

CONCLUSIONS

This study identified three sub-phenotypes based on the prognostic factors available immediately after hospital arrival that could predict neurological outcomes and be useful in determining the treatment strategy of patients experiencing OHCA upon their arrival at the hospital.

Evaluation of interleukin-1 and interleukin-6 receptor antagonists in a murine model of acute lung injury

The acute exudative phase of acute respiratory distress syndrome (ARDS), a severe form of respiratory failure, is characterized by alveolar damage, pulmonary oedema, and an exacerbated inflammatory response. There is no effective treatment for this condition, but based on the major contribution of inflammation, anti-inflammatory strategies have been evaluated in animal models and clinical trials, with conflicting results. In COVID-19 ARDS patients, interleukin (IL)-1 and IL-6 receptor antagonists (IL-1Ra and IL-6Ra, kineret and tocilizumab, respectively) have shown some efficacy. Moreover, we have previously developed novel peptides modulating IL-1R and IL-6R activity (rytvela and HSJ633, respectively) while preserving immune vigilance and cytoprotective pathways. We aimed to assess the efficacy of these novel IL-1Ra and IL-6Ra, compared to commercially available drugs (kineret, tocilizumab) during the exudative phase (day 7) of bleomycin-induced acute lung injury (ALI) in mice. Our results first showed that none of the IL-1Ra and IL-6Ra compounds attenuated bleomycin-induced weight loss and venousP C O 2 ${P_{{\mathrm{C}}{{\mathrm{O}}_{\mathrm{2}}}}}$ increase. Histological analyses and lung water content measurements also showed that these drugs did not improve lung injury scores or pulmonary oedema, after the bleomycin challenge. Finally, IL-1Ra and IL-6Ra failed to alleviate the inflammatory status of the mice, as indicated by cytokine levels and alveolar neutrophil infiltration. Altogether, these results indicate a lack of beneficial effects of IL-1R and IL-6R antagonists on key parameters of ALI in the bleomycin mouse model.

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

Do Precision and Personalised Nutrition Interventions Improve Risk Factors in Adults with Prediabetes or Metabolic Syndrome? A Systematic Review of Randomised Controlled Trials

This review aimed to synthesise existing literature on the efficacy of personalised or precision nutrition (PPN) interventions, including medical nutrition therapy (MNT), in improving outcomes related to glycaemic control (HbA1c, post-prandial glucose [PPG], and fasting blood glucose), anthropometry (weight, BMI, and waist circumference [WC]), blood lipids, blood pressure (BP), and dietary intake among adults with prediabetes or metabolic syndrome (MetS). Six databases were systematically searched (Scopus, Medline, Embase, CINAHL, PsycINFO, and Cochrane) for randomised controlled trials (RCTs) published from January 2000 to 16 April 2023. The Academy of Nutrition and Dietetics Quality Criteria were used to assess the risk of bias. Seven RCTs (n = 873), comprising five PPN and two MNT interventions, lasting 3-24 months were included. Consistent and significant improvements favouring PPN and MNT interventions were reported across studies that examined outcomes like HbA1c, PPG, and waist circumference. Results for other measures, including fasting blood glucose, HOMA-IR, blood lipids, BP, and diet, were inconsistent. Longer, more frequent interventions yielded greater improvements, especially for HbA1c and WC. However, more research in studies with larger sample sizes and standardised PPN definitions is needed. Future studies should also investigate combining MNT with contemporary PPN factors, including genetic, epigenetic, metabolomic, and metagenomic data.

Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study

BACKGROUND

Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome.

METHODS

A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied.

RESULTS

Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1.

CONCLUSIONS

Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.

Assessment and report of individual symptoms in studies of delirium in postoperative populations: a systematic review

OBJECTIVES

Delirium is most often reported as present or absent. Patients with symptoms falling short of the diagnostic criteria for delirium fall into 'no delirium' or 'control' groups. This binary classification neglects individual symptoms and may be hindering identification of the pathophysiology underlying delirium. This systematic review investigates which individual symptoms of delirium are reported by studies of postoperative delirium in adults.

METHODS

Medline, EMBASE and Web of Science databases were searched on 03 June 2021 and 06 April 2023. Two reviewers independently examined titles and abstracts. Each paper was screened in duplicate and conflicting decisions settled by consensus discussion. Data were extracted, qualitatively synthesised and narratively reported. All included studies were quality assessed.

RESULTS

These searches yielded 4,367 results. After title and abstract screening, 694 full-text studies were reviewed, and 62 deemed eligible for inclusion. This review details 11,377 patients including 2,049 patients with delirium. In total, 78 differently described delirium symptoms were reported. The most reported symptoms were inattention (N = 29), disorientation (N = 27), psychomotor agitation/retardation (N = 22), hallucination (N = 22) and memory impairment (N = 18). Notably, psychomotor agitation and hallucinations are not listed in the current Diagnostic and Statistical Manual for Mental Disorders-5-Text Revision delirium definition.

CONCLUSIONS

The 78 symptoms reported in this systematic review cover domains of attention, awareness, disorientation and other cognitive changes. There is a lack of standardisation of terms, and many recorded symptoms are synonyms of each other. This systematic review provides a library of individual delirium symptoms, which may be used to inform future reporting.

Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials

Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials.

Bloodstream infection clusters for critically ill patients: analysis of two-center retrospective cohorts

BACKGROUND

Bloodstream infections (BSI) are highly prevalent in hospitalized patients requiring intensive care. They are among the most serious infections and are highly associated with sepsis or septic shock, which can lead to prolonged hospital stays and high healthcare costs. This study aimed at establishing an easy-to-use nomogram for predicting the prognosis of patients with BSI.

METHODS

In retrospective study, records of patients with BSI admitted to the intensive care unit (ICU) over the period from Jan 1st 2016 to Dec 31st 2021 were included. We used data from two different China hospitals as development cohort and validation cohort respectively. The demographic and clinical data of patients were collected. Based on all baseline data, k-means algorithm was applied to discover the groups of BSI phenotypes with different prognostic outcomes, which was confirmed by Kaplan-Meier analysis and compared using log-rank tests. Univariate Cox regression analyses were used to estimate the risk of clusters. Random forest was used to identified discriminative predictors in clusters, which were utilized to construct nomogram based on multivariable logistic regression in the discovery cohort. For easy clinical applications, we developed a bloodstream infections clustering (BSIC) score according to the nomogram. The results were validated in the validation cohort over a similar period.

RESULTS

A total of 360 patients in the discovery cohort and 310 patients in the validation cohort were included in statistical analyses. Based on baseline variables, two distinct clusters with differing prognostic outcomes were identified in the discovery cohort. Population in cluster 1 was 211 with a ICU mortality of 17.1%, while population in cluster 2 was 149 with an ICU mortality of 41.6% (p < 0.001). The survival analysis also revealed a higher risk of death for cluster 2 when compared with cluster 1 (hazard ratio: 2.31 [95% CI, 1.53 to 3.51], p < 0.001), which was confirmed in validation cohort. Four independent predictors (vasoconstrictor use before BSI, mechanical ventilation (MV) before BSI, Deep vein catheterization (DVC) before BSI, and antibiotic use before BSI) were identified and used to develop a nomogram. The nomogram and BSIC score showed good discrimination with AUC of 0.96.

CONCLUSION

The developed score has potential applications in the identification of high-risk critically ill BSI patients.

The Rise of Adaptive Platform Trials in Critical Care

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

Derivation of Coagulation Phenotypes and the Association with Prognosis in Traumatic Brain Injury: A Cluster Analysis of Nationwide Multicenter Study

BACKGROUND

The pathogenesis and pathophysiology of traumatic coagulopathy during traumatic brain injury is not well understood, and the appropriate treatment strategy for this condition has not been established. This study aimed to evaluate the coagulation phenotypes and their effect on prognosis in patients with isolated traumatic brain injury.

METHODS

In this multicenter cohort study, we retrospectively analyzed data from the Japan Neurotrauma Data Bank. Adults with isolated traumatic brain injury (head abbreviated injury scale > 2; abbreviated injury scale of any other trauma < 3) who were registered in the Japan Neurotrauma Data Bank were included in this study. The primary outcome was the association of coagulation phenotypes with in-hospital mortality. Coagulation phenotypes were derived using k-means clustering with coagulation markers, including prothrombin time international normalized ratio (PT-INR), activated partial thromboplastin time (APTT), fibrinogen (FBG), and D-dimer (DD) on arrival at the hospital. Multivariable logistic regression analyses were conducted to calculate the adjusted odds ratios of coagulation phenotypes with their 95% confidence intervals (CIs) for in-hospital mortality.

RESULTS

In total, 556 patients were enrolled and five coagulation phenotypes were identified. The median (interquartile range) score for the Glasgow Coma Scale was 6 (4-9). Cluster A (n = 129) had the closest to normal coagulation values; cluster B (n = 323) had a mild high DD phenotype; cluster C (n = 30) had a prolonged PT-INR phenotype with a higher frequency of antithrombotic medication in elderly patients than in younger patients; cluster D (n = 45) had a low amount of FBG, high DD, and prolonged APTT phenotype with a high incidence of skull fracture; and cluster E (n = 29) had a low amount of FBG and extremely high DD phenotype with high energy trauma and a high incidence of skull fracture. In the multivariable logistic regression analysis, the association of clusters B, C, D, and E with in-hospital mortality yielded the corresponding adjusted odds ratios of 2.17 (95% CI 1.22-3.86), 2.61 (95% CI 1.01-6.72), 10.0 (95% CI 4.00-25.2), and 24.1 (95% CI 7.12-81.3), respectively, relative to cluster A.

CONCLUSIONS

This multicenter, observational study identified five different coagulation phenotypes of traumatic brain injury and showed associations of these phenotypes with in-hospital mortality.

Identification of molecular subphenotypes in two cohorts of paediatric ARDS

BACKGROUND

Two subphenotypes of acute respiratory distress syndrome (ARDS), hypoinflammatory and hyperinflammatory, have been reported in adults and in a single paediatric cohort. The relevance of these subphenotypes in paediatrics requires further investigation. We aimed to identify subphenotypes in two large observational cohorts of paediatric ARDS and assess their congruence with prior descriptions.

METHODS

We performed latent class analysis (LCA) separately on two cohorts using biomarkers as inputs. Subphenotypes were compared on clinical characteristics and outcomes. Finally, we assessed overlap with adult cohorts using parsimonious classifiers.

FINDINGS

In two cohorts from the Children's Hospital of Philadelphia (n=333) and from a multicentre study based at the University of California San Francisco (n=293), LCA identified two subphenotypes defined by differential elevation of biomarkers reflecting inflammation and endotheliopathy. In both cohorts, hyperinflammatory subjects had greater illness severity, more sepsis and higher mortality (41% and 28% in hyperinflammatory vs 11% and 7% in hypoinflammatory). Both cohorts demonstrated overlap with adult subphenotypes when assessed using parsimonious classifiers.

INTERPRETATION

We identified hypoinflammatory and hyperinflammatory subphenotypes of paediatric ARDS from two separate cohorts with utility for prognostic and potentially predictive, enrichment. Future paediatric ARDS trials should identify and leverage biomarker-defined subphenotypes in their analysis.

Association between urea trajectory and protein dose in critically ill adults: a secondary exploratory analysis of the effort protein trial (RE-EFFORT)

BACKGROUND

Delivering higher doses of protein to mechanically ventilated critically ill patients did not improve patient outcomes and may have caused harm. Longitudinal urea measurements could provide additional information about the treatment effect of higher protein doses. We hypothesised that higher urea values over time could explain the potential harmful treatment effects of higher doses of protein.

METHODS

We conducted a reanalysis of a randomised controlled trial of higher protein doses in critical illness (EFFORT Protein). We applied Bayesian joint models to estimate the strength of association of urea with 30-day survival and understand the treatment effect of higher protein doses.

RESULTS

Of the 1301 patients included in EFFORT Protein, 1277 were included in this analysis. There were 344 deaths at 30 days post-randomisation. By day 6, median urea was 2.1 mmol/L higher in the high protein group (95% CI 1.1-3.2), increasing to 3.0 mmol/L (95% CI 1.3-4.7) by day 12. A twofold rise in urea was associated with an increased risk of death at 30 days (hazard ratio 1.34, 95% credible interval 1.21-1.48), following adjustment of baseline characteristics including age, illness severity, renal replacement therapy, and presence of AKI. This association persisted over the duration of 30-day follow-up and in models adjusting for evolution of organ failure over time.

CONCLUSIONS

The increased risk of death in patients randomised to a higher protein dose in the EFFORT Protein trial was estimated to be mediated by increased urea cycle activity, of which serum urea is a biological signature. Serum urea should be taken into consideration when initiating and continuing protein delivery in critically ill patients.

CLINICALTRIALS

gov Identifier: NCT03160547 (2017-05-17).

Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts

We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model "X-DEC". We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.

Advancing specificity in delirium: The delirium subtyping initiative

BACKGROUND

Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology.

METHODS

The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts.

RESULTS

Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations.

DISCUSSION

The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes.

HIGHLIGHTS

Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

Personalized acute kidney injury treatment

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a complex syndrome that might be induced by different causes and is associated with an increased morbidity and mortality. Therefore, it is a very heterogeneous syndrome and establishing a "one size fits all" treatment approach might not work. This review aims to examine the potential of personalized treatment strategies for AKI.

RECENT FINDINGS

The traditional diagnosis of AKI is based on changes of serum creatinine and urine output, but these two functional biomarkers have several limitations. Recent research identified different AKI phenotypes based on clinical features, biomarkers, and pathophysiological pathways. Biomarkers, such as Cystatin C, NGAL, TIMP2∗IGFBP7, CCL14, and DKK-3, have shown promise in predicting AKI development, renal recovery, and prognosis. Biomarker-guided interventions, such as the implementation of the KDIGO bundle, have demonstrated an improvement in renal outcomes in specific patient groups.

SUMMARY

A personalized approach to AKI treatment as well as research is becoming increasingly important as it allows the identification of distinct AKI phenotypes and the potential for targeted interventions. By utilizing biomarkers and clinical features, physicians might be able to stratify patients into subphenotypes, enabling more individualized treatment strategies. This review highlights the potential of personalized AKI treatment, emphasizing the need for further research and large-scale clinical trials to validate the efficacy of these approaches.

Associations of dipeptidyl-peptidase 3 with short-term outcome in a mixed admission ICU-cohort

PURPOSE

Biomarkers independently associated with outcome of intensive care unit (ICU) patients can improve risk assessment. The cytosolic protease dipeptidyl-peptidase 3 (DPP3) is released into the circulation upon cell necrosis. We aimed to investigate the prognostic properties of cDPP3 in a mixed-admission ICU cohort.

MATERIALS AND METHODS

Prospective observational study in 650 adult ICU patients. cDPP3 concentrations were measured at ICU admission (day 1), and on days 2 and 3.

RESULTS

cDPP3 concentrations on days 1 and 2, but not on day 3 were associated with 28-day mortality; HR 1.36 (95%CI 1.01-1.83, p = 0.043) and HR 1.49 (95%CI 1.16-1.93, p = 0.002) for days 1 and 2, respectively. cDPP3 was also associated with acute kidney injury (AKI), with OR's of 1.31 (95%CI 1.05-1.64, p = 0.016), 1.87 (95%CI 1.51-2.34, p < 0.001) and 1.49 (95%CI 1.16-1.92, p = 0.002) for measurements performed on days 1, 2, and 3, respectively. In multivariate analyses including SOFA or APACHE-II scores, cDPP3 assessed at day 2 of admission remained an independent predictor of mortality and all-stage AKI.

CONCLUSIONS

In a mixed-ICU cohort, cDPP3 concentrations after start of initial treatment were independently associated with both mortality and development of AKI. Therefore, measurement of cDPP3 can improve risk-stratification provided by established disease severity scores.

Precision fluid and vasoactive drug therapy for critically ill patients

There are several clinical practice guidelines concerning the use of fluid and vasoactive drug therapies in critically ill adult patients, but the recommendations in these guidelines are often based on low-quality evidence. Further, some were compiled prior to the publication of landmark clinical trials, particularly in the comparison of balanced crystalloid and normal saline. An important consideration in the treatment of critically ill patients is the application of precision medicine to provide the most effective care to groups of patients most likely to benefit from the therapy. Although not currently widely integrated into these practice guidelines, the utility of precision medicine in critical illness is a recognized research priority for fluid and vasoactive therapy management. The purpose of this narrative review was to illustrate the evaluation and challenges of providing precision fluid and vasoactive therapies to adult critically ill patients. The review includes a discussion of important investigations published after the release of currently available clinical practice guidelines to provide insight into how recommendations and research priorities may change future guidelines and bedside care for critically ill patients.

Identifying molecular phenotypes in sepsis: an analysis of two prospective observational cohorts and secondary analysis of two randomised controlled trials

BACKGROUND

In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials.

METHODS

We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect.

FINDINGS

A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72).

INTERPRETATION

Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis.

FUNDING

US National Institutes of Health.

Application of anti-inflammatory treatment in two different ovine Acute Respiratory Distress Syndrome injury models: a preclinical randomized intervention study

Whilst the presence of 2 subphenotypes among the heterogenous Acute Respiratory Distress Syndrome (ARDS) population is becoming clinically accepted, subphenotype-specific treatment efficacy has yet to be prospectively tested. We investigated anti-inflammatory treatment in different ARDS models in sheep, previously shown similarities to human ARDS subphenotypes, in a preclinical, randomized, blinded study. Thirty anesthetized sheep were studied up to 48 h and randomized into: (a) OA: oleic acid (n = 15) and (b) OA-LPS: oleic acid and subsequent lipopolysaccharide (n = 15) to achieve a PaO2/FiO2 ratio of < 150 mmHg. Then, animals were randomly allocated to receive treatment with methylprednisolone or erythromycin or none. Assessed outcomes were oxygenation, pulmonary mechanics, hemodynamics and survival. All animals reached ARDS. Treatment with methylprednisolone, but not erythromycin, provided the highest therapeutic benefit in Ph2 animals, leading to a significant increase in PaO2/FiO2 ratio by reducing pulmonary edema, dead space ventilation and shunt fraction. Animals treated with methylprednisolone displayed a higher survival up to 48 h than all others. In animals treated with erythromycin, there was no treatment benefit regarding assessed physiological parameters and survival in both phenotypes. Treatment with methylprednisolone improves oxygenation and survival, more so in ovine phenotype 2 which resembles the human hyperinflammatory subphenotype.

Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further?

PURPOSE OF REVIEW

Care and outcomes of critically ill patients with cancer have improved over the past decade. This selective review will discuss recent updates in sepsis and acute respiratory failure among patients with cancer, with particular focus on important opportunities to improve outcomes further through attention to phenotyping, predictive analytics, and improved outcome measures.

RECENT FINDINGS

The prevalence of cancer diagnoses in intensive care units (ICUs) is nontrivial and increasing. Sepsis and acute respiratory failure remain the most common critical illness syndromes affecting these patients, although other complications are also frequent. Recent research in oncologic sepsis has described outcome variation - including ICU, hospital, and 28-day mortality - across different types of cancer (e.g., solid vs. hematologic malignancies) and different sepsis definitions (e.g., Sepsis-3 vs. prior definitions). Research in acute respiratory failure in oncology patients has highlighted continued uncertainty in the value of diagnostic bronchoscopy for some patients and in the optimal respiratory support strategy. For both of these syndromes, specific challenges include multifactorial heterogeneity (e.g. in etiology and/or underlying cancer), delayed recognition of clinical deterioration, and complex outcomes measurement.

SUMMARY

Improving outcomes in oncologic critical care requires attention to the heterogeneity of cancer diagnoses, timely recognition and management of critical illness, and defining appropriate ICU outcomes.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Precision management of acute kidney injury in the intensive care unit: current state of the art

Acute kidney injury (AKI) is a prototypical example of a common syndrome in critical illness defined by consensus. The consensus definition for AKI, traditionally defined using only serum creatinine and urine output, was needed to standardize the description for epidemiology and to harmonize eligibility for clinical trials. However, AKI is not a simple disease, but rather a complex and multi-factorial syndrome characterized by a wide spectrum of pathobiology. AKI is now recognized to be comprised of numerous sub-phenotypes that can be discriminated through shared features such as etiology, prognosis, or common pathobiological mechanisms of injury and damage. The characterization of sub-phenotypes can serve to enable prognostic enrichment (i.e., identify subsets of patients more likely to share an outcome of interest) and predictive enrichment (identify subsets of patients more likely to respond favorably to a given therapy). Existing and emerging biomarkers will aid in discriminating sub-phenotypes of AKI, facilitate expansion of diagnostic criteria, and be leveraged to realize personalized approaches to management, particularly for recognizing treatment-responsive mechanisms (i.e., endotypes) and targets for intervention (i.e., treatable traits). Specific biomarkers (e.g., serum renin; olfactomedin 4 (OLFM4); interleukin (IL)-9) may further enable identification of pathobiological mechanisms to serve as treatment targets. However, even non-specific biomarkers of kidney injury (e.g., neutrophil gelatinase-associated lipocalin, NGAL; [tissue inhibitor of metalloproteinases 2, TIMP2]·[insulin like growth factor binding protein 7, IGFBP7]; kidney injury molecule 1, KIM-1) can direct greater precision management for specific sub-phenotypes of AKI. This review will summarize these evolving concepts and recent innovations in precision medicine approaches to the syndrome of AKI in critical illness, along with providing examples of how they can be leveraged to guide patient care.

Subphenotypes of Pediatric Acute Kidney Injury

Acute kidney injury (AKI) is seen frequently in hospitalized patients and is associated with increased risk of mortality and adverse short- and long-term renal and systemic complications. Emerging data suggest that AKI is a heterogenous syndrome with a variety of underlying causes, predisposing illnesses, and range of clinical trajectories and outcomes. This mini-review aims to discuss emerging AKI subphenotype classifications as our understanding of the heterogeneity and underlying pathophysiology has improved.

Distinct host-response signatures in circulatory shock: a narrative review

Circulatory shock is defined syndromically as hypotension associated with tissue hypoperfusion and often subcategorized according to hemodynamic profile (e.g., distributive, cardiogenic, hypovolemic) and etiology (e.g., infection, myocardial infarction, trauma, among others). These shock subgroups are generally considered homogeneous entities in research and clinical practice. This current definition fails to consider the complex pathophysiology of shock and the influence of patient heterogeneity. Recent translational evidence highlights previously under-appreciated heterogeneity regarding the underlying pathways with distinct host-response patterns in circulatory shock syndromes. This heterogeneity may confound the interpretation of trial results as a given treatment may preferentially impact distinct subgroups. Re-analyzing results of major 'neutral' treatment trials from the perspective of biological mechanisms (i.e., host-response signatures) may reveal treatment effects in subgroups of patients that share treatable traits (i.e., specific biological signatures that portend a predictable response to a given treatment). In this review, we discuss the emerging literature suggesting the existence of distinct biomarker-based host-response patterns of circulatory shock syndrome independent of etiology or hemodynamic profile. We further review responses to newly prescribed treatments in the intensive care unit designed to personalize treatments (biomarker-driven or endotype-driven patient selection in support of future clinical trials).

Ebola Virus Disease Features Hemophagocytic Lymphohistiocytosis/Macrophage Activation Syndrome in the Rhesus Macaque Model

BACKGROUND

Ebola virus (EBOV) disease (EVD) is one of the most severe and fatal viral hemorrhagic fevers and appears to mimic many clinical and laboratory manifestations of hemophagocytic lymphohistiocytosis syndrome (HLS), also known as macrophage activation syndrome. However, a clear association is yet to be firmly established for effective host-targeted, immunomodulatory therapeutic approaches to improve outcomes in patients with severe EVD.

METHODS

Twenty-four rhesus monkeys were exposed intramuscularly to the EBOV Kikwit isolate and euthanized at prescheduled time points or when they reached the end-stage disease criteria. Three additional monkeys were mock-exposed and used as uninfected controls.

RESULTS

EBOV-exposed monkeys presented with clinicopathologic features of HLS, including fever, multiple organomegaly, pancytopenia, hemophagocytosis, hyperfibrinogenemia with disseminated intravascular coagulation, hypertriglyceridemia, hypercytokinemia, increased concentrations of soluble CD163 and CD25 in serum, and the loss of activated natural killer cells.

CONCLUSIONS

Our data suggest that EVD in the rhesus macaque model mimics pathophysiologic features of HLS/macrophage activation syndrome. Hence, regulating inflammation and immune function might provide an effective treatment for controlling the pathogenesis of acute EVD.

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).

Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis

Sepsis arises from diverse and incompletely understood dysregulated host response processes following infection that leads to life-threatening organ dysfunction. Here we showed that neutrophils and emergency granulopoiesis drove a maladaptive response during sepsis. We generated a whole-blood single-cell multiomic atlas (272,993 cells, n = 39 individuals) of the sepsis immune response that identified populations of immunosuppressive mature and immature neutrophils. In co-culture, CD66b⁺ sepsis neutrophils inhibited proliferation and activation of CD4⁺ T cells. Single-cell multiomic mapping of circulating hematopoietic stem and progenitor cells (HSPCs) (29,366 cells, n = 27) indicated altered granulopoiesis in patients with sepsis. These features were enriched in a patient subset with poor outcome and a specific sepsis response signature that displayed higher frequencies of IL1R2⁺ immature neutrophils, epigenetic and transcriptomic signatures of emergency granulopoiesis in HSPCs and STAT3-mediated gene regulation across different infectious etiologies and syndromes. Our findings offer potential therapeutic targets and opportunities for stratified medicine in severe infection.

Characterisation of the pro-inflammatory cytokine signature in severe COVID-19

Clinical outcomes from infection with SARS-CoV-2, the cause of the COVID-19 pandemic, are remarkably variable ranging from asymptomatic infection to severe pneumonia and death. One of the key drivers of this variability is differing trajectories in the immune response to SARS-CoV-2 infection. Many studies have noted markedly elevated cytokine levels in severe COVID-19, although results vary by cohort, cytokine studied and sensitivity of assay used. We assessed the immune response in acute COVID-19 by measuring 20 inflammatory markers in 118 unvaccinated patients with acute COVID-19 (median age: 70, IQR: 58-79 years; 48.3% female) recruited during the first year of the pandemic and 44 SARS-CoV-2 naïve healthy controls. Acute COVID-19 was associated with marked elevations in nearly all pro-inflammatory markers, whilst eleven markers (namely IL-1β, IL-2, IL-6, IL-10, IL-18, IL-23, IL-33, TNF-α, IP-10, G-CSF and YKL-40) were associated with disease severity. We observed significant correlations between nearly all markers elevated in those infected with SARS-CoV-2 consistent with widespread immune dysregulation. Principal component analysis highlighted a pro-inflammatory cytokine signature (with strongest contributions from IL-1β, IL-2, IL-6, IL-10, IL-33, G-CSF, TNF-α and IP-10) which was independently associated with severe COVID-19 (aOR: 1.40, 1.11-1.76, p=0.005), invasive mechanical ventilation (aOR: 1.61, 1.19-2.20, p=0.001) and mortality (aOR 1.57, 1.06-2.32, p = 0.02). Our findings demonstrate elevated cytokines and widespread immune dysregulation in severe COVID-19, adding further evidence for the role of a pro-inflammatory cytokine signature in severe and critical COVID-19.

THE EUROPEAN SHOCK SOCIETY MEETS THE IMMUNOSEP CONSORTIUM FOR PERSONALIZED SEPSIS TREATMENT

ABSTRACT

The unacceptable high mortality of severe infections and sepsis led over the years to understand the need for adjunctive immunotherapy to modulate the dysregulated host response of the host. However, not all patients should receive the same type of treatment. The immune function may largely differ from one patient to the other. The principles of precision medicine require that some biomarker is used to capture the immune function of the host and guide the best candidate therapy. This is the approach of the ImmunoSep randomized clinical trial (NCT04990232) where patients are allocated to treatment with anakinra or recombinant interferon gamma tailored to immune signs of macrophage activation-like syndrome and immunoparalysis respectively. ImmunoSep is a first-in-class paradigm of precision medicine for sepsis. Other approaches need to consider classification by sepsis endotypes, targeting T cell and application of stem cells. Basic principle for any trial to be successful is the delivery of appropriate antimicrobial therapy as standard-of-care taking into consideration not just the likelihood for resistant pathogens but also the pharmacokinetic/pharmacodynamic mode of action of the administered antimicrobial.

Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning

To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: "inflamed," characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and "respiratory acidosis," characterized by low pH and high pCO2 levels.    Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: "inflamed" and "respiratory acidosis." By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: • Treatment of PDA in preterm infants has been controversial. • Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: • Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. • The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.

Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome

Paediatric acute respiratory distress syndrome (PARDS) is a heterogeneous clinical syndrome that is associated with high rates of mortality and long-term morbidity. Factors that distinguish PARDS from adult acute respiratory distress syndrome (ARDS) include changes in developmental stage and lung maturation with age, precipitating factors, and comorbidities. No specific treatment is available for PARDS and management is largely supportive, but methods to identify patients who would benefit from specific ventilation strategies or ancillary treatments, such as prone positioning, are needed. Understanding of the clinical and biological heterogeneity of PARDS, and of differences in clinical features and clinical course, pathobiology, response to treatment, and outcomes between PARDS and adult ARDS, will be key to the development of novel preventive and therapeutic strategies and a precision medicine approach to care. Studies in which clinical, biomarker, and transcriptomic data, as well as informatics, are used to unpack the biological and phenotypic heterogeneity of PARDS, and implementation of methods to better identify patients with PARDS, including methods to rapidly identify subphenotypes and endotypes at the point of care, will drive progress on the path to precision medicine.

Pathobiology, Severity, and Risk Stratification of Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference

OBJECTIVES

To review the literature for studies published in children on the pathobiology, severity, and risk stratification of pediatric acute respiratory distress syndrome (PARDS) with the intent of guiding current medical practice and identifying important areas for future research related to severity and risk stratification.

DATA SOURCES

Electronic searches of PubMed and Embase were conducted from 2013 to March 2022 by using a combination of medical subject heading terms and text words to capture the pathobiology, severity, and comorbidities of PARDS.

STUDY SELECTION

We included studies of critically ill patients with PARDS that related to the severity and risk stratification of PARDS using characteristics other than the oxygenation defect. Studies using animal models, adult only, and studies with 10 or fewer children were excluded from our review.

DATA EXTRACTION

Title/abstract review, full-text review, and data extraction using a standardized data collection form.

DATA SYNTHESIS

The Grading of Recommendations Assessment, Development, and Evaluation approach was used to identify and summarize relevant evidence and develop recommendations for clinical practice. There were 192 studies identified for full-text extraction to address the relevant Patient/Intervention/Comparator/Outcome questions. One clinical recommendation was generated related to the use of dead space fraction for risk stratification. In addition, six research statements were generated about the impact of age on acute respiratory distress syndrome pathobiology and outcomes, addressing PARDS heterogeneity using biomarkers to identify subphenotypes and endotypes, and use of standardized ventilator, physiologic, and nonpulmonary organ failure measurements for future research.

CONCLUSIONS

Based on an extensive literature review, we propose clinical management and research recommendations related to characterization and risk stratification of PARDS severity.