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

Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury

INTRODUCTION

Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI.

PATIENTS AND METHODS

This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively.

RESULTS

Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort.

CONCLUSIONS

Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.

Multilevel omics for the discovery of biomarkers in pediatric sepsis

Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi-omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi-levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.

N6-methyladenosine modification: Regulatory mechanisms and therapeutic potential in sepsis

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and is characterized by multiple biological and clinical features. N6-methyladenosine (m6A) modification is the most common type of RNA modifications in eukaryotes and plays an important regulatory role in various biological processes. Recently, m6A modification has been found to be involved in the regulation of immune responses in sepsis. In addition, several studies have shown that m6A modification is involved in sepsis-induced multiple organ dysfunctions, including cardiovascular dysfunction, acute lung injury (ALI), acute kidney injury (AKI) and etc. Considering the complex pathogenesis of sepsis and the lack of specific therapeutic drugs, m6A modification may be the important bond in the pathophysiological process of sepsis and even therapeutic targets. This review systematically highlights the recent advances regarding the roles of m6A modification in sepsis and sheds light on their use as treatment targets for sepsis.

Organotypic heterogeneity in microvascular endothelial cell responses in sepsis-a molecular treasure trove and pharmacological Gordian knot

In the last decades, it has become evident that endothelial cells (ECs) in the microvasculature play an important role in the pathophysiology of sepsis-associated multiple organ dysfunction syndrome (MODS). Studies on how ECs orchestrate leukocyte recruitment, control microvascular integrity and permeability, and regulate the haemostatic balance have provided a wealth of knowledge and potential molecular targets that could be considered for pharmacological intervention in sepsis. Yet, this information has not been translated into effective treatments. As MODS affects specific vascular beds, (organotypic) endothelial heterogeneity may be an important contributing factor to this lack of success. On the other hand, given the involvement of ECs in sepsis, this heterogeneity could also be leveraged for therapeutic gain to target specific sites of the vasculature given its full accessibility to drugs. In this review, we describe current knowledge that defines heterogeneity of organ-specific microvascular ECs at the molecular level and elaborate on studies that have reported EC responses across organ systems in sepsis patients and animal models of sepsis. We discuss hypothesis-driven, single-molecule studies that have formed the basis of our understanding of endothelial cell engagement in sepsis pathophysiology, and include recent studies employing high-throughput technologies. The latter deliver comprehensive data sets to describe molecular signatures for organotypic ECs that could lead to new hypotheses and form the foundation for rational pharmacological intervention and biomarker panel development. Particularly results from single cell RNA sequencing and spatial transcriptomics studies are eagerly awaited as they are expected to unveil the full spatiotemporal signature of EC responses to sepsis. With increasing awareness of the existence of distinct sepsis subphenotypes, and the need to develop new drug regimen and companion diagnostics, a better understanding of the molecular pathways exploited by ECs in sepsis pathophysiology will be a cornerstone to halt the detrimental processes that lead to MODS.

Bayesian methods: a potential path forward for sepsis trials

BACKGROUND

Given the success of recent platform trials for COVID-19, Bayesian statistical methods have become an option for complex, heterogenous syndromes like sepsis. However, study design will require careful consideration of how statistical power varies using Bayesian methods across different choices for how historical data are incorporated through a prior distribution and how the analysis is ultimately conducted. Our objective with the current analysis is to assess how different uses of historical data through a prior distribution, and type of analysis influence results of a proposed trial that will be analyzed using Bayesian statistical methods.

METHODS

We conducted a simulation study incorporating historical data from a published multicenter, randomized clinical trial in the US and Canada of polymyxin B hemadsorption for treatment of endotoxemic septic shock. Historical data come from a 179-patient subgroup of the previous trial of adult critically ill patients with septic shock, multiple organ failure and an endotoxin activity of 0.60-0.89. The trial intervention consisted of two polymyxin B hemoadsorption treatments (2 h each) completed within 24 h of enrollment.

RESULTS

In our simulations for a new trial of 150 patients, a range of hypothetical results were observed. Across a range of baseline risks and treatment effects and four ways of including historical data, we demonstrate an increase in power with the use of clinically defensible incorporation of historical data. In one possible trial result, for example, with an observed reduction in risk of mortality from 44 to 37%, the probability of benefit is 96% with a fixed weight of 75% on prior data and 90% with a commensurate (adaptive-weighting) prior; the same data give an 80% probability of benefit if historical data are ignored.

CONCLUSIONS

Using Bayesian methods and a biologically justifiable use of historical data in a prior distribution yields a study design with higher power than a conventional design that ignores relevant historical data. Bayesian methods may be a viable option for trials in critical care medicine where beneficial treatments have been elusive.

Deep Learning for Epidemiologists: An Introduction to Neural Networks

Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.

A Prospective Cohort Protocol for the Remnant Investigation in Sepsis Study

BACKGROUND

Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown.

METHODS AND RESULTS

The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium-heparin, EDTA, sodium fluoride, and citrate tubes.

CONCLUSIONS

The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.

Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes

PURPOSE

The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies.

METHODS

This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, β, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles.

RESULTS

All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made.

CONCLUSION

Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

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.

Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care

BACKGROUND AND OBJECTIVE

Interpretable and real-time prediction of sepsis and risk factor analysis could enable timely treatment by clinicians and improve patient outcomes. To develop an interpretable machine-learning model for the prediction and risk factor analysis of sepsis and septic death.

METHODS

This is a retrospective observational cohort study based on the Medical Information Mart for Intensive Care (MIMIC-IV) dataset; 69,619 patients from the database were screened. The two outcomes include patients diagnosed with sepsis and the death of septic patients. Clinical variables from ICU admission to outcomes were analyzed: demographic data, vital signs, Glasgow Coma Scale scores, laboratory test results, and results for arterial blood gasses (ABGs). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were based on the Shapley additive explanations (SHAP), and the clustered analysis was based on the combination of K-means and dimensionality reduction algorithms of t-SNE and PCA.

RESULTS

For the analysis of sepsis and septic death, 47,185 and 2480 patients were enrolled, respectively. The XGBoost model achieved a predictive value of area under the curve (AUC): 0.745 [0.731-0.759] for sepsis prediction and 0.8 [0.77, 0.828] for septic death prediction. The real-time prediction model was trained to predict by day and visualize the individual or combined risk factor effects on the outcomes based on SHAP values. Clustered analysis separated the two phenotypes with distinct risk factors among patients with septic death.

CONCLUSION

The proposed real-time, clustered prediction model for sepsis and septic death exhibited superior performance in predicting the outcomes and visualizing the risk factors in a real-time and interpretable manner to distinguish and mitigate patient risks, thus promising immense potential in effective clinical decision making and comprehensive understanding of complex diseases such as sepsis.

Distinct clinical phenotypes in paediatric cancer patients with sepsis are associated with different outcomes-an international multicentre retrospective study

Background

Identifying phenotypes in sepsis patients may enable precision medicine approaches. However, the generalisability of these phenotypes to specific patient populations is unclear. Given that paediatric cancer patients with sepsis have different host response and pathogen profiles and higher mortality rates when compared to non-cancer patients, we determined whether unique, reproducible, and clinically-relevant sepsis phenotypes exist in this specific patient population.

Methods

We studied patients with underlying malignancies admitted with sepsis to one of 25 paediatric intensive care units (PICUs) participating in two large, multi-centre, observational cohorts from the European SCOTER study (n = 383 patients; study period between January 1, 2018 and January 1, 2020) and the U.S. Novel Data-Driven Sepsis Phenotypes in Children study (n = 1898 patients; study period between January 1, 2012 and January 1, 2018). We independently used latent class analysis (LCA) in both cohorts to identify phenotypes using demographic, clinical, and laboratory data from the first 24 h of PICU admission. We then tested the association of the phenotypes with clinical outcomes in both cohorts.

Findings

LCA identified two distinct phenotypes that were comparable across both cohorts. Phenotype 1 was characterised by lower serum bicarbonate and albumin, markedly increased lactate and hepatic, renal, and coagulation abnormalities when compared to phenotype 2. Patients with phenotype 1 had a higher 90-day mortality (European cohort 29.2% versus 13.4%, U.S. cohort 27.3% versus 11.4%, p < 0.001) and received more vasopressor and renal replacement therapy than patients with phenotype 2. After adjusting for severity of organ dysfunction, haematological cancer, prior stem cell transplantation and age, phenotype 1 was associated with an adjusted OR of death at 90-day of 1.9 (1.04-3.34) in the European cohort and 1.6 (1.2-2.2) in the U.S. cohort.

Interpretation

We identified two clinically-relevant sepsis phenotypes in paediatric cancer patients that are reproducible across two international, multicentre cohorts with prognostic implications. These results may guide further research regarding therapeutic approaches for these specific phenotypes.

Funding

Part of this study is funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Bio-Inspired Fractal Robust Hydrogel Catheter for Intra-Abdominal Sepsis Management

To deal with intra-abdominal sepsis, one of the major global causes of death in hospitalized patients, efficient abscess drainage is crucial. Despite decades of advances, traditional catheters have demonstrated poor drainage and absorption properties due to their simple tubular structures and their dense nonporous surface. Herein, inspired by porous sponges and fractal roots, a multifaceted hydrogel catheter with effective drainage, absorptive, and robust properties, is presented. Its unique fractal structures provide extensive internal branching and a high specific surface area for effective drainage, while the hierarchical porous structures provide a wide range of absorption capabilities. Additionally, its distinctive multi-interpenetration network maintains robust and appropriate mechanical properties, even after absorption multiple times of liquid and mechanical disturbance, allowing for intact removal from the abdominal cavity without harm to the animal in vivo. Besides, the loaded antimicrobial peptides are capable of being released in situ to inhibit the potential for infections. In vivo experiments have demonstrated that this hydrogel catheter efficiently removes lethal abscesses and improves survival. It is believed that this innovative and practical catheter will create a future precedent for hydrogel drainage devices for more effective management of intra-abdominal sepsis.

Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity

PURPOSE

Various studies have analyzed sepsis subtypes, yet the reproducibility of such results remains unclear. This study aimed to determine the reproducibility of sepsis subtypes across multiple cohorts.

METHODS

The study examined 63,547 sepsis patients from six distinct cohorts who had similar sepsis-related characteristics (vital signs, lactate, sequential organ failure assessment score, bilirubin, serum, urine output, and Glasgow coma scale). Identical cluster analysis techniques were used, employing 27 clustering schemes, and normalized mutual information (NMI), a metric ranging from 0 to 1 with higher values indicating better concordance, was employed to quantify the clustering solutions' reproducibility. Principal component analysis (PCA) was utilized to obtain the disease axis, and its uniformity across cohorts was evaluated through patterns of feature loading and correlation.

RESULTS

The reproducibility of sepsis clustering subtypes across the various studies was modest (median NMI ranging from 0.08 to 0.54). The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). The reproducibility was greater when the transfer solution was performed within United States (US) cohorts. The PCA analysis revealed that the correlation pattern between variables was consistent across all cohorts, and the first two disease axes were the "shock axis" and "systemic inflammatory response syndrome (SIRS) axis."

CONCLUSIONS

Cluster analysis of sepsis patients across various cohorts showed modest reproducibility. Sepsis heterogeneity is better characterized through continuous disease axes that coexist to varying degrees within the same individual instead of mutually exclusive subtypes.

The role of endotoxin in septic shock

Septic shock can be caused by a variety of mechanisms including direct effects of bacterial toxins such as endotoxin. Annually, approximately 5-7 million patients worldwide develop sepsis with very high endotoxin activity in the blood and more than half die. The term endotoxic septic shock has been used for these patients but it is important to emphasize that endotoxin may be a factor in all forms of septic shock including non-bacterial etiologies like COVID-19 since translocation of bacterial products is a common feature of septic shock. A pattern of organ failure including hepatic dysfunction, acute kidney injury and various forms of endothelial dysfunction ranging from disseminated intravascular coagulation to thrombotic microangiopathy characterize endotoxic septic shock. However, while characteristic, the clinical phenotype is not unique to patients with high endotoxin, and the diagnosis relies on the measurement of endotoxin activity in addition to clinical assessment. Therapies for endotoxic septic shock are limited with immune modulating therapies under investigation and extracorporeal blood purification still controversial in many parts of the world.

Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit

BACKGROUND

Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output.

SUMMARY

Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI.

KEY MESSAGES

Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

BACKGROUND

There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to "hyperinflammation" associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes.

OBJECTIVE

The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management.

METHODS

This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission.

RESULTS

A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit-level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83.

CONCLUSIONS

Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

Substantial heterogeneity of inflammatory cytokine production and its inhibition by a triple cocktail of toll-like receptor blockers in early sepsis

Introduction

Early sepsis is a life-threatening immune dysregulation believed to feature a "cytokine storm" due to activation of pattern recognition receptors by pathogen and danger associated molecular patterns. However, treatments with single toll-like receptor (TLR) blockers have shown no clinical benefit. We speculated that sepsis patients at the time of diagnosis are heterogeneous in relation to their cytokine production and its potential inhibition by a triple cocktail of TLR blockers. Accordingly, we analyzed inflammatory cytokine production in whole blood assays from early sepsis patients and determined the effects of triple TLR-blockade.

Methods

Whole blood of 51 intensive care patients sampled within 24h of meeting Sepsis-3 criteria was incubated for 6h without or with specific TLR2, 4, and 7/8 stimuli or suspensions of heat-killed S. aureus or E. coli bacteria as pan-TLR challenges, and also with a combination of monoclonal antibodies against TLR2 and 4 and chloroquine (endosomal TLR inhibition), subsequent to dose optimization. Concentrations of tumor necrosis factor (TNF), Interleukin(IL)-6, IL-8, IL-10, IL-1α and IL-1β were measured (multiplex ELISA) before and after incubation. Samples from 11 sex and age-matched healthy volunteers served as controls and for dose-finding studies.

Results

Only a fraction of sepsis patient samples revealed ongoing cytokine production ex vivo despite sampling within 24 h of first meeting Sepsis-3 criteria. In dose finding studies, inhibition of TLR2, 4 and endosomal TLRs reliably suppressed cytokine production to specific TLR agonists and added bacteria. However, inflammatory cytokine production ex vivo was only suppressed in the high cytokine producing samples but not in the majority. The suppressive response to TLR-blockade correlated both with intraassay inflammatory cytokine production (r=0.29-0.68; p<0.0001-0.04) and cytokine baseline concentrations (r=0.55; p<0.0001).

Discussion

Upon meeting Sepsis-3 criteria for less than 24 h, a mere quarter of patient samples exhibits a strong inflammatory phenotype, as characterized by increased baseline inflammatory cytokine concentrations and a stark TLR-dependent increase upon further ex vivo incubation. Thus, early sepsis patient cohorts as defined by Sepsis-3 criteria are very heterogeneous in regard to inflammation. Accordingly, proper ex vivo assays may be useful in septic individuals before embarking on immunomodulatory treatments.

The Impact of Positive Fluid Balance on Sepsis Subtypes: A Causal Inference Study

Introduction

Sepsis, the leading cause of death in hospitalized patients globally, was investigated in this study, examining the varying effects of positive fluid balance on sepsis subtypes through causal inference.

Methods

In this study, data from the eICU database were utilized, extracting 35 features from sepsis patients. Fluid balance during ICU stay was the treatment, and ICU mortality was the primary outcome. Data preprocessing ensured linear assumptions for logistic regression. Binarized positive fluid balance with mortality was examined using DoWhy's logistic regression, while continuous data were analyzed with random forest T-learner. ATE served as the primary metric.

Results

Results revealed that septic patients with higher fluid balance had worse mortality outcomes, with an ATE of 0.042 (95% CI: (0.034, 0.047)) using logistic regression and an ATE of 0.0340 (95% CI: (0.028-0.040)) using T-learner. In the pulmonary sepsis subtype, higher mortality was associated with increased fluid balance, showing an ATE of 0.047 (95% CI: (0.037, 0.055)) using logistic regression and an ATE of 0.28 (95% CI: (0.22, 0.34)) with T-learner. Conversely, urinary sepsis patients had improved mortality with higher fluid balance, presenting an ATE of -0.135 (95% CI: (-0.024, -0.0035)) using logistic regression and an ATE of -0.28 (95% CI: (-0.34, -0.22)) with T-learner.

Conclusion

Our research implies that fluid balance impact on ICU mortality differs among sepsis subtypes. Positive fluid balance raises mortality in sepsis and pulmonary sepsis but may protect against urinary sepsis. Further trials are needed to confirm these findings.

The application of artificial intelligence in the management of sepsis

Sepsis is a complex and heterogeneous syndrome that remains a serious challenge to healthcare worldwide. Patients afflicted by severe sepsis or septic shock are customarily placed under intensive care unit (ICU) supervision, where a multitude of apparatus is poised to produce high-granularity data. This reservoir of high-quality data forms the cornerstone for the integration of AI into clinical practice. However, existing reviews currently lack the inclusion of the latest advancements. This review examines the evolving integration of artificial intelligence (AI) in sepsis management. Applications of artificial intelligence include early detection, subtyping analysis, precise treatment and prognosis assessment. AI-driven early warning systems provide enhanced recognition and intervention capabilities, while profiling analyzes elucidate distinct sepsis manifestations for targeted therapy. Precision medicine harnesses the potential of artificial intelligence for pathogen identification, antibiotic selection, and fluid optimization. In conclusion, the seamless amalgamation of artificial intelligence into the domain of sepsis management heralds a transformative shift, ushering in novel prospects to elevate diagnostic precision, therapeutic efficacy, and prognostic acumen. As AI technologies develop, their impact on shaping the future of sepsis care warrants ongoing research and thoughtful implementation.

Derivation, Validation, and Clinical Relevance of a Pediatric Sepsis Phenotype With Persistent Hypoxemia, Encephalopathy, and Shock

OBJECTIVES

Untangling the heterogeneity of sepsis in children and identifying clinically relevant phenotypes could lead to the development of targeted therapies. Our aim was to analyze the organ dysfunction trajectories of children with sepsis-associated multiple organ dysfunction syndrome (MODS) to identify reproducible and clinically relevant sepsis phenotypes and determine if they are associated with heterogeneity of treatment effect (HTE) to common therapies.

DESIGN

Multicenter observational cohort study.

SETTING

Thirteen PICUs in the United States.

PATIENTS

Patients admitted with suspected infections to the PICU between 2012 and 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used subgraph-augmented nonnegative matrix factorization to identify candidate trajectory-based phenotypes based on the type, severity, and progression of organ dysfunction in the first 72 hours. We analyzed the candidate phenotypes to determine reproducibility as well as prognostic, therapeutic, and biological relevance. Overall, 38,732 children had suspected infection, of which 15,246 (39.4%) had sepsis-associated MODS with an in-hospital mortality of 10.1%. We identified an organ dysfunction trajectory-based phenotype (which we termed persistent hypoxemia, encephalopathy, and shock) that was highly reproducible, had features of systemic inflammation and coagulopathy, and was independently associated with higher mortality. In a propensity score-matched analysis, patients with persistent hypoxemia, encephalopathy, and shock phenotype appeared to have HTE and benefit from adjuvant therapy with hydrocortisone and albumin. When compared with other high-risk clinical syndromes, the persistent hypoxemia, encephalopathy, and shock phenotype only overlapped with 50%-60% of patients with septic shock, moderate-to-severe pediatric acute respiratory distress syndrome, or those in the top tier of organ dysfunction burden, suggesting that it represents a nonsynonymous clinical phenotype of sepsis-associated MODS.

CONCLUSIONS

We derived and validated the persistent hypoxemia, encephalopathy, and shock phenotype, which is highly reproducible, clinically relevant, and associated with HTE to common adjuvant therapies in children with sepsis.

The Role of Data Science in Closing the Implementation Gap

Data science has the potential to greatly enhance efforts to translate evidence into practice in critical care. The intensive care unit is a data-rich environment enabling insight into both patient-level care patterns and clinician-level treatment patterns. By applying artificial intelligence to these novel data sources, implementation strategies can be tailored to individual patients, individual clinicians, and individual situations, revealing when evidence-based practices are missed and facilitating context-sensitive clinical decision support. To achieve these goals, technology developers should work closely with clinicians to create unbiased applications that are integrated into the clinical workflow.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

Clinical Course of Patients in Cardiogenic Shock Stratified by Phenotype

BACKGROUND

Cardiogenic shock (CS) patients remain at 30% to 60% in-hospital mortality despite therapeutic innovations. Heterogeneity of CS has complicated clinical trial design. Recently, 3 distinct CS phenotypes were identified in the CSWG (Cardiogenic Shock Working Group) registry version 1 (V1) and external cohorts: I, "noncongested;" II, "cardiorenal;" and III, "cardiometabolic" shock.

OBJECTIVES

The aim was to confirm the external reproducibility of machine learning-based CS phenotypes and to define their clinical course.

METHODS

The authors included 1,890 all-cause CS patients from the CSWG registry version 2. CS phenotypes were identified using the nearest centroids of the initially reported clusters.

RESULTS

Phenotypes were retrospectively identified in 796 patients in version 2. In-hospital mortality rates in phenotypes I, II, III were 23%, 41%, 52%, respectively, comparable to the initially reported 21%, 45%, and 55% in V1. Phenotype-related demographic, hemodynamic, and metabolic features resembled those in V1. In addition, 58.8%, 45.7%, and 51.9% of patients in phenotypes I, II, and III received mechanical circulatory support, respectively (P = 0.013). Receiving mechanical circulatory support was associated with increased mortality in cardiorenal (OR: 1.82 [95% CI: 1.16-2.84]; P = 0.008) but not in noncongested or cardiometabolic CS (OR: 1.26 [95% CI: 0.64-2.47]; P = 0.51 and OR: 1.39 [95% CI: 0.86-2.25]; P = 0.18, respectively). Admission phenotypes II and III and admission Society for Cardiovascular Angiography and Interventions stage E were independently associated with increased mortality in multivariable logistic regression compared to noncongested "stage C" CS (P < 0.001).

CONCLUSIONS

The findings support the universal applicability of these phenotypes using supervised machine learning. CS phenotypes may inform the design of future clinical trials and enable management algorithms tailored to a specific CS phenotype.

Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape

Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. Individual hospital practice patterns may limit external generalizability. Data missingness is another barrier to optimal algorithm performance and various strategies exist to mitigate this. Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.

Sepsis heterogeneity

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, with extremely high mortality. Notably, sepsis is a heterogeneous syndrome characterized by a vast, multidimensional array of clinical and biologic features, which has hindered advances in the therapeutic field beyond the current standards.

DATA SOURCES

We used PubMed to search the subject-related medical literature by searching for the following single and/or combination keywords: sepsis, heterogeneity, personalized treatment, host response, infection, epidemiology, mortality, incidence, age, children, sex, comorbidities, gene susceptibility, infection sites, bacteria, fungi, virus, host response, organ dysfunction and management.

RESULTS

We found that host factors (age, biological sex, comorbidities, and genetics), infection etiology, host response dysregulation and multiple organ dysfunctions can all result in different disease manifestations, progression, and response to treatment, which make it difficult to effectively treat and manage sepsis patients.

CONCLUSIONS

Herein, we have summarized contributing factors to sepsis heterogeneity, including host factors, infection etiology, host response dysregulation, and multiple organ dysfunctions, from the key elements of pathogenesis of sepsis. An in-depth understanding of the factors that contribute to the heterogeneity of sepsis will help clinicians understand the complexity of sepsis and enable researchers to conduct more personalized clinical studies for homogenous patients.

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.

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.

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.

Molecular subtypes based on N6-methyladenosine RNA methylation demonstrate the heterogeneity of immune and biological functions in pediatric septic shock

Introduction

Septic shock in children is a highly heterogeneous syndrome involving different immune states and biological processes. We used a bioinformatics approach to explore the relationship between N6-methyladenosine (m6A) methylation and septic shock in children.

Methods

A gene expression dataset including information on 98 children with septic shock was selected. To construct and evaluate a risk prediction model, machine learning was used to screen marker m6A regulators. Based on differentially expressed m6A regulators, molecular subtypes for paediatric septic shock were constructed. Subsequently, the differences in the m6Ascore, heterogeneity of immune cell infiltration, and heterogeneity of biological functions between the different subtypes were analyzed. Finally, real-time quantitative PCR (RT-qPCR) was performed to validate the expression of the marker m6A regulators.

Results

Fifteen differentially expressed m6A regulators were identified. Six marker m6A regulators, including LRPPRC, ELAVL1, RBM15, CBLL1, FTO, and RBM15B, were screened using the random forest method. The risk prediction model for paediatric septic shock constructed using m6A markers had strong consistency and high clinical practicability. Two subtypes of paediatric septic shock have been identified based on the differential expression pattern of m6A regulators. Significant differences were observed in RNA epigenetics, immune statuses, and biological processes between the two m6A subtypes. Differentially expressed genes between the two subtypes were enriched in cell number homeostasis, redox responses, and innate immune system responses. Finally, the six marker m6A regulators were verified in additional samples.

Conclusions

Based on the heterogeneity of m6A methylation-regulated genes, two different subtypes of septic shock in children with different RNA epigenetics, immune statuses, and biological processes were identified, revealing the heterogeneity of the disease largely attributable to differential m6A methylation. The findings will help explore and establish appropriate individualized treatments.

Understanding Post-Sepsis Syndrome: How Can Clinicians Help?

Sepsis is a global health challenge, with over 49 million cases annually. Recent medical advancements have increased in-hospital survival rates to approximately 80%, but the escalating incidence of sepsis, owing to an ageing population, rise in chronic diseases, and antibiotic resistance, have also increased the number of sepsis survivors. Subsequently, there is a growing prevalence of "post-sepsis syndrome" (PSS). This syndrome includes long-term physical, medical, cognitive, and psychological issues after recovering from sepsis. PSS puts survivors at risk for hospital readmission and is associated with a reduction in health- and life span, both at short and long term, after hospital discharge. Comprehensive understanding of PSS symptoms and causative factors is vital for developing optimal care for sepsis survivors, a task of prime importance for clinicians. This review aims to elucidate our current knowledge of PSS and its relevance in enhancing post-sepsis care provided by clinicians.

Assessment of implementation methods in sepsis: study protocol for a cluster-randomized hybrid type 2 trial

BACKGROUND

Sepsis is the leading cause of intensive care unit (ICU) admission and ICU death. In recognition of the burden of sepsis, the Surviving Sepsis Campaign (SSC) and the Institute for Healthcare Improvement developed sepsis "bundles" (goals to accomplish over a specific time period) to facilitate SSC guideline implementation in clinical practice. Using the SSC 3-h bundle as a base, the Centers for Medicare and Medicaid Services developed a 3-h sepsis bundle that has become the national standard for early management of sepsis. Emerging observational data, from an analysis conducted for the AIMS grant application, suggest there may be additional mortality benefit from even earlier implementation of the 3-h bundle, i.e., the 1-h bundle.

METHOD

The primary aims of this randomized controlled trial are to: (1) examine the effect on clinical outcomes of Emergency Department initiation of the elements of the 3-h bundle within the traditional 3 h versus initiating within 1 h of sepsis recognition and (2) examine the extent to which a rigorous implementation strategy will improve implementation and compliance with both the 1-h bundle and the 3-h bundle. This study will be entirely conducted in the Emergency Department at 18 sites. A secondary aim is to identify clinical sepsis phenotypes and their impact on treatment outcomes.

DISCUSSION

This cluster-randomized trial, employing implementation science methodology, is timely and important to the field. The hybrid effectiveness-implementation design is likely to have an impact on clinical practice in sepsis management by providing a rigorous evaluation of the 1- and 3-h bundles.

FUNDING

NHLBI R01HL162954.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05491941. Registered on August 8, 2022.

The impact of hypertonic saline on damage control laparotomy after penetrating abdominal trauma

PURPOSE

The inability to achieve primary fascial closure (PFC) after emergency laparotomy increases the rates of adverse outcomes including fistula formation, incisional hernia, and intraabdominal infection. Hypertonic saline (HTS) infusion improves early PFC rates and decreases time to PFC in patients undergoing damage control laparotomy (DCL) after injury. We hypothesized that in patients undergoing DCL after penetrating abdominal injury, HTS infusion would decrease the time to fascial closure as well as the volume of crystalloid required for resuscitation without inducing clinically relevant acute kidney injury (AKI) or electrolyte derangements.

METHODS

We retrospectively analyzed all penetrating abdominal injury patients undergoing DCL within the University of Pennsylvania Health System (January 2015-December 2018). We compared patients who received 3% HTS at 30 mL/h (HTS) to those receiving isotonic fluid (ISO) for resuscitation while the abdominal fascia remained open. Primary outcomes were the rate of early PFC (PFC within 72 h) and time to PFC; secondary outcomes included acute kidney injury, sodium derangement, ventilator-free days, hospital length of stay (LOS), and ICU LOS. Intergroup comparisons occurred by ANOVA and Tukey's comparison, and student's t, and Fischer's exact tests, as appropriate. A Shapiro-Wilk test was performed to determine normality of distribution.

RESULTS

Fifty-seven patients underwent DCL after penetrating abdominal injury (ISO n = 41, HTS n = 16). There were no significant intergroup differences in baseline characteristics or injury severity score. Mean time to fascial closure was significantly shorter in HTS (36.37 h ± 14.21 vs 59.05 h ± 50.75, p = 0.02), and the PFC rate was significantly higher in HTS (100% vs 73%, p = 0.01). Mean 24-h fluid and 48-h fluid totals were significantly less in HTS versus ISO (24 h: 5.2L ± 1.7 vs 8.6L ± 2.2, p = 0.01; 48 h: 1.3L ± 1.1 vs 2.6L ± 2.2, p = 0.008). During the first 72 h, peak sodium (Na) concentration (146.2 mEq/L ± 2.94 vs 142.8 mEq/L ± 3.67, p = 0.0017) as well as change in Na from ICU admission (5.1 mEq/L vs 2.3, p = 0.016) were significantly higher in HTS compared to ISO. Patients in the HTS group received significantly more blood in the trauma bay compared to ISO. There were no intergroup differences in intraoperative blood transfusion volume, AKI incidence, change in chloride concentration (△Cl) from ICU admit, Na to Cl gradient (Na:Cl), initial serum creatinine (Cr), peak post-operative Cr, change in creatinine concentration (△Cr) from ICU admission, creatinine clearance (CrCl), initial serum potassium (K), peak ICU K, change in K from ICU admission, initial pH, highest or lowest post-operative pH, mean hospital LOS, ICU LOS, and ventilator-free days.

CONCLUSIONS

HTS infusion in patients undergoing DCL after penetrating abdominal injury decreases the time to fascial closure and led to 100% early PFC. HTS infusion also decreased resuscitative fluid volume without causing significant AKI or electrolyte derangement. HTS appears to offer a safe and effective fluid management approach in patients who sustain penetrating abdominal injury and DCL to support early PFC without inducing measurable harm.

LEVEL OF EVIDENCE

Level III.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Comprehensive single-cell analysis reveals novel anergic antigen-presenting cell subtypes in human sepsis

Background

Sepsis is a life-threatening condition with high mortality. A few studies have emerged utilizing single-cell RNA sequencing (scRNA-seq) to analyze gene expression at the single-cell resolution in sepsis, but a comprehensive high-resolution analysis of blood antigen-presenting cells has not been conducted.

Methods

All published human scRNA-seq data were downloaded from the single cell portal database. After manually curating the dataset, we extracted all antigen-presenting cells, including dendritic cells (DCs) and monocytes, for identification of cell subpopulations and their gene profiling and intercellular interactions between septic patients and healthy controls. Finally, we further validated the findings by performing deconvolution analysis on bulk RNA sequencing (RNA-seq) data and flow cytometry.

Results

Within the traditional DC populations, we discovered novel anergic DC subtypes characterized by low major histocompatibility complex class II expression. Notably, these anergic DC subtypes showed a significant increase in septic patients. Additionally, we found that a previously reported immunosuppressive monocyte subtype, Mono1, exhibited a similar gene expression profile to these anergic DCs. The consistency of our findings was confirmed through validation using bulk RNA-seq and flow cytometry, ensuring accurate identification of cell subtypes and gene expression patterns.

Conclusions

This study represents the first comprehensive single-cell analysis of antigen-presenting cells in human sepsis, revealing novel disease-associated anergic DC subtypes. These findings provide new insights into the cellular mechanisms of immune dysregulation in bacterial sepsis.

Effect of high-dose intravenous ascorbic acid on microcirculation and endothelial glycocalyx during sepsis and septic shock: a double-blind, randomized, placebo-controlled study

Previous studies indicate supplemental vitamin C improves microcirculation and reduces glycocalyx shedding in septic animals. Our randomized, double-blind, placebo-controlled trial aimed to investigate whether a high dose of intravenous ascorbic acid (AA) might improve microcirculation and affect glycocalyx in septic patients. In our study, 23 septic patients were supplemented with a high dose (50 mg/kg every 6 h) of intravenous AA or placebo for 96 h. Sublingual microcirculation was examined using a handheld Cytocam-incident dark field (IDF) video microscope. A sidestream dark field video microscope (SDF), connected to the GlycoCheck software (GlycoCheck ICU®; Maastricht University Medical Center, Maastricht, the Netherlands), was employed to observe glycocalyx. We found a significantly higher proportion of perfused small vessels (PPV) 6 h after the beginning of the trial in the experimental group compared with placebo. As an indicator of glycocalyx thickness, the perfused boundary region was lower in capillaries of the 5-9 μm diameter in the AA group than placebo after the first dose of AA. Our data suggest that high-dose parenteral AA tends to improve microcirculation and glycocalyx in the early period of septic shock. The study was retrospectively registered in the clinicaltrials.gov database on 26/02/2021 (registration number NCT04773717).

Predicting sepsis using a combination of clinical information and molecular immune markers sampled in the ambulance

Sepsis is a time dependent condition. Screening tools based on clinical parameters have been shown to increase the identification of sepsis. The aim of current study was to evaluate the additional predictive value of immunological molecular markers to our previously developed prehospital screening tools. This is a prospective cohort study of 551 adult patients with suspected infection in the ambulance setting of Stockholm, Sweden between 2017 and 2018. Initially, 74 molecules and 15 genes related to inflammation were evaluated in a screening cohort of 46 patients with outcome sepsis and 50 patients with outcome infection no sepsis. Next, 12 selected molecules, as potentially synergistic predictors, were evaluated in combination with our previously developed screening tools based on clinical parameters in a prediction cohort (n = 455). Seven different algorithms with nested cross-validation were used in the machine learning of the prediction models. Model performances were compared using posterior distributions of average area under the receiver operating characteristic (ROC) curve (AUC) and difference in AUCs. Model variable importance was assessed by permutation of variable values, scoring loss of classification as metric and with model-specific weights when applicable. When comparing the screening tools with and without added molecular variables, and their interactions, the molecules per se did not increase the predictive values. Prediction models based on the molecular variables alone showed a performance in terms of AUCs between 0.65 and 0.70. Among the molecular variables, IL-1Ra, IL-17A, CCL19, CX3CL1 and TNF were significantly higher in septic patients compared to the infection non-sepsis group. Combing immunological molecular markers with clinical parameters did not increase the predictive values of the screening tools, most likely due to the high multicollinearity of temperature and some of the markers. A group of sepsis patients was consistently miss-classified in our prediction models, due to milder symptoms as well as lower expression levels of the investigated immune mediators. This indicates a need of stratifying septic patients with a priori knowledge of certain clinical and molecular parameters in order to improve prediction for early sepsis diagnosis.Trial registration: NCT03249597. Registered 15 August 2017.

Trans-Omics analysis of post injury thrombo-inflammation identifies endotypes and trajectories in trauma patients

Understanding and managing the complexity of trauma-induced thrombo-inflammation necessitates an innovative, data-driven approach. This study leveraged a trans-omics analysis of longitudinal samples from trauma patients to illuminate molecular endotypes and trajectories that underpin patient outcomes, transcending traditional demographic and physiological characterizations. We hypothesize that trans-omics profiling reveals underlying clinical differences in severely injured patients that may present with similar clinical characteristics but ultimately have very different responses to treatment and clinical outcomes. Here we used proteomics and metabolomics to profile 759 of longitudinal plasma samples from 118 patients at 11 time points and 97 control subjects. Results were used to define distinct patient states through data reduction techniques. The patient groups were stratified based on their shock severity and injury severity score, revealing a spectrum of responses to trauma and treatment that are fundamentally tied to their unique underlying biology. Ensemble models were then employed, demonstrating the predictive power of these molecular signatures with area under the receiver operating curves of 80 to 94% for key outcomes such as INR, ICU-free days, ventilator-free days, acute lung injury, massive transfusion, and death. The molecularly defined endotypes and trajectories provide an unprecedented lens to understand and potentially guide trauma patient management, opening a path towards precision medicine. This strategy presents a transformative framework that aligns with our understanding that trauma patients, despite similar clinical presentations, might harbor vastly different biological responses and outcomes.

Management of Sepsis and Septic Shock: What Have We Learned in the Last Two Decades?

Sepsis is a clinical syndrome encompassing physiologic and biological abnormalities caused by a dysregulated host response to infection. Sepsis progression into septic shock is associated with a dramatic increase in mortality, hence the importance of early identification and treatment. Over the last two decades, the definition of sepsis has evolved to improve early sepsis recognition and screening, standardize the terms used to describe sepsis and highlight its association with organ dysfunction and higher mortality. The early 2000s witnessed the birth of early goal-directed therapy (EGDT), which showed a dramatic reduction in mortality leading to its wide adoption, and the surviving sepsis campaign (SSC), which has been instrumental in developing and updating sepsis guidelines over the last 20 years. Outside of early fluid resuscitation and antibiotic therapy, sepsis management has transitioned to a less aggressive approach over the last few years, shying away from routine mixed venous oxygen saturation and central venous pressure monitoring and excessive fluids resuscitation, inotropes use, and red blood cell transfusions. Peripheral vasopressor use was deemed safe and is rising, and resuscitation with balanced crystalloids and a restrictive fluid strategy was explored. This review will address some of sepsis management's most important yet controversial components and summarize the available evidence from the last two decades.

A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach

Individuals developing stroke have varying clinical characteristics, demographic, and biochemical profiles. This heterogeneity in phenotypic characteristics can impact on cardiovascular disease (CVD) morbidity and mortality outcomes. This study uses a novel clustering approach to stratify individuals with incident stroke into phenotypic clusters and evaluates the differential burden of recurrent stroke and other cardiovascular outcomes. We used linked clinical data from primary care, hospitalisations, and death records in the UK. A data-driven clustering analysis (kamila algorithm) was used in 48,114 patients aged ≥ 18 years with incident stroke, from 1-Jan-1998 to 31-Dec-2017 and no prior history of serious vascular events. Cox proportional hazards regression was used to estimate hazard ratios (HRs) for subsequent adverse outcomes, for each of the generated clusters. Adverse outcomes included coronary heart disease (CHD), recurrent stroke, peripheral vascular disease (PVD), heart failure, CVD-related and all-cause mortality. Four distinct phenotypes with varying underlying clinical characteristics were identified in patients with incident stroke. Compared with cluster 1 (n = 5,201, 10.8%), the risk of composite recurrent stroke and CVD-related mortality was higher in the other 3 clusters (cluster 2 [n = 18,655, 38.8%]: hazard ratio [HR], 1.07; 95% CI, 1.02-1.12; cluster 3 [n = 10,244, 21.3%]: HR, 1.20; 95% CI, 1.14-1.26; and cluster 4 [n = 14,014, 29.1%]: HR, 1.44; 95% CI: 1.37-1.50). Similar trends in risk were observed for composite recurrent stroke and all-cause mortality outcome, and subsequent recurrent stroke outcome. However, results were not consistent for subsequent risk in CHD, PVD, heart failure, CVD-related mortality, and all-cause mortality. In this proof of principle study, we demonstrated how a heterogenous population of patients with incident stroke can be stratified into four relatively homogenous phenotypes with differential risk of recurrent and major cardiovascular outcomes. This offers an opportunity to revisit the stratification of care for patients with incident stroke to improve patient outcomes.

Identifying clusters of coexisting conditions and outcomes among adults admitted to hospital with community-acquired pneumonia: a multicentre cohort study

BACKGROUND

Little is known about patterns of coexisting conditions and their influence on clinical care or outcomes in adults admitted to hospital for community-acquired pneumonia (CAP). We sought to evaluate how coexisting conditions cluster in this population to advance understanding of how multimorbidity affects CAP.

METHODS

We studied 11 085 adults admitted to hospital with CAP at 7 hospitals in Ontario, Canada. Using cluster analysis, we identified patient subgroups based on clustering of comorbidities in the Charlson Comorbidity Index. We derived and replicated cluster analyses in independent cohorts (derivation sample 2010-2015, replication sample 2015-2017), then combined these into a total cohort for final cluster analyses. We described differences in medications, imaging and outcomes.

RESULTS

Patients clustered into 7 subgroups. The low comorbidity subgroup (n = 3052, 27.5%) had no comorbidities. The DM-HF-Pulm subgroup had prevalent diabetes, heart failure and chronic lung disease (n = 1710, 15.4%). One disease category defined each remaining subgroup, as follows: pulmonary (n = 1621, 14.6%), diabetes (n = 1281, 11.6%), heart failure (n = 1370, 12.4%), dementia (n = 1038, 9.4%) and cancer (n = 1013, 9.1%). Corticosteroid use ranged from 11.5% to 64.9% in the dementia and pulmonary subgroups, respectively. Piperacillin-tazobactam use ranged from 9.1% to 28.0% in the pulmonary and cancer subgroups, respectively. The use of thoracic computed tomography ranged from 5.7% to 36.3% in the dementia and cancer subgroups, respectively. Adjusting for patient factors, the risk of in-hospital death was greater in the cancer (adjusted odds ratio [OR] 3.12, 95% confidence interval [CI] 2.44-3.99), dementia (adjusted OR 1.57, 95% CI 1.05-2.35), heart failure (adjusted OR 1.66, 95% CI 1.35-2.03) and DM-HF-Pulm subgroups (adjusted OR 1.35, 95% CI 1.12-1.61), and lower in the diabetes subgroup (adjusted OR 0.67, 95% CI 0.50-0.89), compared with the low comorbidity group.

INTERPRETATION

Patients admitted to hospital with CAP cluster into clinically recognizable subgroups based on coexisting conditions. Clinical care and outcomes vary among these subgroups with little evidence to guide decision-making, highlighting opportunities for research to personalize care.

Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND

Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.

METHODS

In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.

FINDINGS

Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.

INTERPRETATION

Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.

FUNDING

Deutsche Forschungsgemeinschaft.

Nutrition dose in the early acute phase of critical illness: Finding the sweet spot and heeding the lessons from the NUTRIREA trials

The landmark NUTRIREA-2 and NUTRIREA-3 trials compared the route and dose of nutrition, respectively, in critically ill patients with circulatory shock. The results of both trials support a "less-is-more" paradigm shift in the early acute phase of critical illness. In this review, the authors outline and appraise the results of the NUTRIREA-2 and NUTRIREA-3 trials, introduce the concept of identifying the "sweet spot" for nutrition dose based on severity of illness/nutrition risk and nutrition dose, and identify the unintended consequences of delivering full-dose nutrition in sicker critically ill patients during the early acute phase of critical illness.

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.