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Background Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI. Methods Secondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk. Results A total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients ( n  = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr. Conclusions Endothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children. Graphical abstract

Neonatal sepsis remains a leading cause of global childhood mortality, yet treatment options are limited. Clinical and biological heterogeneity hinders the development of targeted therapies. Gene-expression profiling offers a potential strategy to identify neonatal sepsis subtypes and guide targeted intervention. We performed secondary analyses of publicly available gene-expression datasets. Differential gene expression analysis and T-distributed Stochastic Neighbor Embedding (t-SNE) identified biologically relevant patient clusters. Mortality and organ dysfunction were compared across clusters to determine clinical relevance. We identified three endotypes of neonatal sepsis based on the 100 gene expression mortality signature, distinguishing five non-survivors from 72 survivors across datasets. Compared with other endotypes, Endotype A was associated with high mortality (22% vs. 0%, p=0.003) and cardiac dysfunction (61% vs. 31%, p=0.025). Pathobiology among endotype A patients was primarily driven by neutrophil progenitors. Gene-expression profiling can be used to disentangle neonatal sepsis heterogeneity. Dysregulated hyperinflammatory response with emergency granulopoiesis was pathognomonic of high-risk endotype A. Pending further validation, gene-expression-based subclassification may be used to identify at-risk neonates and inform the selection of targeted sepsis therapies.

Background Multiple organ dysfunction syndrome (MODS) is a critical driver of sepsis morbidity and mortality in children. Early identification of those at risk of death and persistent organ dysfunctions is necessary to enrich patients for future trials of sepsis therapeutics. Here, we sought to integrate endothelial and PERSEVERE biomarkers to estimate the composite risk of death or organ dysfunctions on day 7 of septic shock. Methods We measured endothelial dysfunction markers from day 1 serum among those with existing PERSEVERE data. TreeNet® classification model was derived incorporating 22 clinical and biological variables to estimate risk. Based on relative variable importance, a simplified 6-biomarker model was developed thereafter. Results Among 502 patients, 49 patients died before day 7 and 124 patients had persistence of MODS on day 7 of septic shock. Area under the receiver operator characteristic curve (AUROC) for the newly derived PERSEVEREnce model to predict death or day 7 MODS was 0.93 (0.91–0.95) with a summary AUROC of 0.80 (0.76–0.84) upon tenfold cross-validation. The simplified model, based on IL-8, HSP70, ICAM-1, Angpt2/Tie2, Angpt2/Angpt1, and Thrombomodulin, performed similarly. Interaction between variables—ICAM-1 with IL-8 and Thrombomodulin with Angpt2/Angpt1—contributed to the models’ predictive capabilities. Model performance varied when estimating risk of individual organ dysfunctions with AUROCS ranging from 0.91 to 0.97 and 0.68 to 0.89 in training and test sets, respectively. Conclusions The newly derived PERSEVEREnce biomarker model reliably estimates risk of death or persistent organ dysfunctions on day 7 of septic shock. If validated, this tool can be used for prognostic enrichment in future pediatric trials of sepsis therapeutics. Graphical abstract

Sepsis, a syndrome of life-threatening organ dysfunction caused by dysregulated host responses to infection, exhibits profound pathobiological heterogeneity, hindering the development of effective therapies. Current subtyping approaches, often reliant on single-omics data or unsupervised clustering, yield poorly reproducible and therapeutically misaligned classifications. Here, we introduce a goal-directed subgroup identification (GD-SI) framework that optimizes patient stratification for differential treatment responses, integrating longitudinal multi-omics data (transcriptomic, proteomic, metabolomic, phenomic) from 1327 subjects across 43 hospitals. While supervised multi-omics integration frameworks (e.g., DIABLO) effectively capture shared biological signals, our approach anchors subgroup discovery directly to treatment-effect optimization. This strategy achieves substantial cross-omic concordance and, crucially, generalizes to predict differential treatment response across international critical care databases. Patients stratified by GD-SI-derived benefit scores for restrictive versus liberal fluid resuscitation exhibited marked survival differences, with similar advantages observed for ulinastatin immunomodulation. External validations in MIMIC-IV and ZiGongDB confirm prognostic generalizability. This framework reconciles biological heterogeneity with clinical actionability, offering a scalable infrastructure for precision trial design and personalized sepsis management. Our findings underscore the translational potential of omics-driven, goal-directed stratification to overcome decades of therapeutic stagnation in critical care. In this work, authors present their goal-directed subgroup identification (GD-SI) framework, reconciling biological heterogeneity with clinical actionability, and offering a scalable infrastructure for precision trial design and personalized sepsis management.

Background Multiple organ dysfunction syndrome (MODS) is an important cause of post-operative morbidity and mortality for children undergoing cardiac surgery requiring cardiopulmonary bypass (CPB). Dysregulated inflammation is widely regarded as a key contributor to bypass-related MODS pathobiology, with considerable overlap of pathways associated with septic shock. The pediatric sepsis biomarker risk model (PERSEVERE) is comprised of seven protein biomarkers of inflammation and reliably predicts baseline risk of mortality and organ dysfunction among critically ill children with septic shock. We aimed to determine if PERSEVERE biomarkers and clinical data could be combined to derive a new model to assess the risk of persistent CPB-related MODS in the early post-operative period. Methods This study included 306 patients < 18 years old admitted to a pediatric cardiac ICU after surgery requiring cardiopulmonary bypass (CPB) for congenital heart disease. Persistent MODS, defined as dysfunction of two or more organ systems on postoperative day 5, was the primary outcome. PERSEVERE biomarkers were collected 4 and 12 h after CPB. Classification and regression tree methodology were used to derive a model to assess the risk of persistent MODS. Results The optimal model containing interleukin-8 (IL-8), chemokine ligand 3 (CCL3), and age as predictor variables had an area under the receiver operating characteristic curve (AUROC) of 0.86 (0.81–0.91) for differentiating those with or without persistent MODS and a negative predictive value of 99% (95–100). Ten-fold cross-validation of the model yielded a corrected AUROC of 0.75 (0.68–0.84). Conclusions We present a novel risk prediction model to assess the risk for development of multiple organ dysfunction after pediatric cardiac surgery requiring CPB. Pending prospective validation, our model may facilitate identification of a high-risk cohort to direct interventions and studies aimed at improving outcomes via mitigation of post-operative organ dysfunction.

Background Sepsis is associated with significant mortality. Yet, there are no efficacious therapies beyond antibiotics. PCSK9 loss-of-function (LOF) and inhibition, through enhanced low-density lipoprotein receptor (LDLR) mediated endotoxin clearance, holds promise as a potential therapeutic approach among adults. In contrast, we have previously demonstrated higher mortality in the juvenile host. Given the potential pleiotropic effects of PCSK9 on the endothelium, beyond canonical effects on serum lipoproteins, both of which may influence sepsis outcomes, we sought to test the influence of PCSK9 LOF genotype on endothelial dysfunction. Methods Secondary analyses of a prospective observational cohort of pediatric septic shock. Genetic variants of PCSK9 and LDLR genes, serum PCSK9, and lipoprotein concentrations were determined previously. Endothelial dysfunction markers were measured in day 1 serum. We conducted multivariable linear regression to test the influence of PCSK9 LOF genotype on endothelial markers, adjusted for age, complicated course, and low- and high-density lipoproteins (LDL and HDL). Causal mediation analyses to test impact of select endothelial markers on the association between PCSK9 LOF genotype and mortality. Juvenile Pcsk9 null and wildtype mice were subject to cecal slurry sepsis and endothelial markers were quantified. Results A total of 474 patients were included. PCSK9 LOF was associated with several markers of endothelial dysfunction, with strengthening of associations after exclusion of those homozygous for the rs688 LDLR variant that renders it insensitive to PCSK9. Serum PCSK9 was not correlated with endothelial dysfunction. PCSK9 LOF influenced concentrations of Angiopoietin-1 (Angpt-1) upon adjusting for potential confounders including lipoprotein concentrations, with false discovery adjusted p value of 0.042 and 0.013 for models that included LDL and HDL, respectively. Causal mediation analysis demonstrated that the effect of PCSK9 LOF on mortality was mediated by Angpt-1 ( p  = 0.0008). Murine data corroborated these results with lower Angpt-1 and higher soluble thrombomodulin among knockout mice with sepsis relative to the wildtype. Conclusions We present genetic and biomarker association data that suggest a potential direct role of the PCSK9-LDLR pathway on Angpt-1 in the developing host with septic shock and warrant external validation. Further, mechanistic studies on the role of PCSK9-LDLR pathway on vascular homeostasis may lead to the development of pediatric-specific sepsis therapies. Graphical abstract

Timely and accurate diagnosis of influenza remains a challenge but is critical for patients who may benefit from antiviral therapy. This study determined the test characteristics of provider diagnosis of influenza, final ED electronic medical record (EMR) diagnosis of influenza, and influenza-like illness (ILI) in patients recommended to receive antiviral treatment according to Centers for Disease Control and Prevention (CDC) guidelines. In addition, we evaluated the compliance with CDC antiviral guidelines. A prospective cohort of adults presenting to a tertiary care ED with an acute respiratory illness who met CDC criteria for recommended antiviral treatment were enrolled and tested for influenza. A clinical diagnosis of influenza was assessed by asking the clinician: “Do you think this patient has influenza?” Influenza-like illness was defined according to current CDC criteria. In this cohort of 270 subjects, 42 (16%; 95% confidence interval [CI], 11%-20%) had influenza. Clinician diagnosis had a sensitivity of 36% (95% CI, 22%-52%) and specificity of 78% (95% CI, 72%-83%); EMR final ED diagnosis had a sensitivity of 26% (95% CI, 14%-42%) and specificity of 97% (95% CI, 94%-99%); ILI had a sensitivity of 31% (95% CI, 18%-47%) and specificity of 88% (95% CI, 83%-92%). Only 15 influenza-positive patients (36%) received antiviral treatment. Clinician diagnosis, final ED EMR diagnosis, and ILI have low sensitivity for diagnosing influenza, and there is overall poor compliance with CDC antiviral treatment recommendations. Improved methods of influenza diagnosis are needed to help guide management in the clinical setting.

Pediatric sepsis is a leading cause of global mortality, particularly among children, with limited therapeutic options beyond antibiotics and organ support. The Pediatric Sepsis Biomarker Risk Model (PERSEVERE-II) stratifies mortality risk in pediatric septic shock, yet the molecular mechanisms underlying high mortality risk remain incompletely understood. We analyzed whole blood transcriptomes collected from 81 children with septic shock on day 1 of meeting study criteria. Patients were stratified into high- and low-mortality risk groups according to the PERSEVERE-II biomarker risk model. Using weighted gene co-expression network analysis (WGCNA) and differential gene expression analyses, we identified molecular pathways and transcription factors (TFs) associated with mortality risk. Cell type differences were inferred using CIBERSORTx and using a reference single-cell dataset inclusive of neutrophils and their subsets. We identified distinct molecular profiles with high-risk patients displaying significant overexpression of genes related to neutrophil degranulation and innate immunity, alongside suppressed adaptive immune responses. The predominance of developing neutrophils underscored a major role of emergency granulopoiesis. Key TFs identified, including , , , and , were linked to high-risk gene expression signatures. Our findings indicate a pathological shift toward a dysregulated neutrophil-driven hyperinflammation and adaptive immune suppressive state, which together are associated with adverse outcomes. Our results suggest that neutrophil dysregulation underpins the high mortality risk conferred by the PERSEVERE-II model. The identified transcriptional regulators may provide potential targets to mitigate neutrophil dysregulation and improve outcomes among high-risk patients.

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. Graphical abstract