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

Clustering out‐of‐hospital cardiac arrest patients with non‐shockable rhythm by machine learning latent class analysis

Aim

We aimed to identify subphenotypes among patients with out‐of‐hospital cardiac arrest (OHCA) with initial non‐shockable rhythm by applying machine learning latent class analysis and examining the associations between subphenotypes and neurological outcomes.

Methods

This study was a retrospective analysis within a multi‐institutional prospective observational cohort study of OHCA patients in Osaka, Japan (the CRITICAL study). The data of adult OHCA patients with medical causes and initial non‐shockable rhythm presenting with OHCA between 2012 and 2016 were included in machine learning latent class analysis models, which identified subphenotypes, and patients who presented in 2017 were included in a dataset validating the subphenotypes. We investigated associations between subphenotypes and 30‐day neurological outcomes.

Results

Among the 12,594 patients in the CRITICAL study database, 4,849 were included in the dataset used to classify subphenotypes (median age: 75 years, 60.2% male), and 1,465 were included in the validation dataset (median age: 76 years, 59.0% male). Latent class analysis identified four subphenotypes. Odds ratios and 95% confidence intervals for a favorable 30‐day neurological outcome among patients with these subphenotypes, using group 4 for comparison, were as follows; group 1, 0.01 (0.001–0.046); group 2, 0.097 (0.051–0.171); and group 3, 0.175 (0.073–0.358). Associations between subphenotypes and 30‐day neurological outcomes were validated using the validation dataset.

Conclusion

We identified four subphenotypes of OHCA patients with initial non‐shockable rhythm. These patient subgroups presented with different characteristics associated with 30‐day survival and neurological outcomes.

Novel clinical risk scoring model for predicting mortality in patients with necrotizing fasciitis: The MNF scoring system

Necrotizing fasciitis (NF) is a life-threatening soft tissue infection that rapidly progresses and requires urgent surgery and medical therapy. If treatment is delayed, the likelihood of an unfavorable outcome, including death, is significantly increased. The goal of this study was to develop and validate a novel scoring model for predicting mortality in patients with NF. The proposed system is hereafter referred to as the Mortality in Necrotizing Fasciitis (MNF) scoring system. A total of 1503 patients with NF were recruited from 3 provincial hospitals in Thailand during January 2009 to December 2012. Patients were randomly allocated into either the derivation cohort (n = 1192) or the validation cohort (n = 311). Clinical risk factors used to develop the MNF scoring system were determined by logistic regression. Regression coefficients were transformed into item scores, the sum of which reflected the total MNF score. The following 6 clinical predictors were included: female gender; age > 60 years; white blood cell (WBC) ≤5000/mm3; WBC ≥ 35,000/mm3; creatinine ≥ 1.6 mg/dL, and pulse rate > 130/min. Area under the receiver operating characteristic curve (AuROC) analysis showed the MNF scoring system to have moderate power for predicting mortality in patients with NF (AuROC: 76.18%) with good calibration (Hosmer-Lemeshow χ2: 1.01; P = .798). The positive likelihood ratios of mortality in patients with low-risk scores (≤2.5) and high-risk scores (≥7) were 11.30 (95% confidence interval [CI]: 6.16-20.71) and 14.71 (95%CI: 7.39-29.28), sequentially. When used to the validation cohort, the MNF scoring system presented good performance with an AuROC of 74.25%. The proposed MNF scoring system, which includes 6 commonly available and easy-to-use parameters, was shown to be an effective tool for predicting mortality in patients with NF. This validated instrument will help clinicians identify at-risk patients so that early investigations and interventions can be performed that will reduce the mortality rate among patients with NF.

Monocyte Trajectories Endotypes Are Associated With Worsening in Septic Patients

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. The immune system plays a key role in sepsis onset and remains dysregulated over time in a heterogeneous manner. Here, we decipher the heterogeneity of the first week evolution of the monocyte HLA-DR (mHLA-DR) surface protein expression in septic patients, a key molecule for adaptive immunity onset. We found and verified four distinctive trajectories endotypes in a discovery (n = 276) and a verification cohort (n = 102). We highlight that 59% of septic patients exhibit low or decreasing mHLA-DR expression while in others mHLA-DR expression increased. This study depicts the first week behavior of mHLA-DR over time after sepsis onset and shows that initial and third day mHLA-DR expression measurements is sufficient for an early risk stratification of sepsis patients. These patients might benefit from immunomodulatory treatment to improve outcomes. Going further, our study introduces a way of deciphering heterogeneity of immune system after sepsis onset which is a first step to reach a more comprehensive landscape of sepsis.

Current practice and evolving concepts in septic shock resuscitation

Clinical and pathophysiological understanding of septic shock has progressed exponentially in the previous decades, translating into a steady decrease in septic shock-related morbidity and mortality. Even though large randomized, controlled trials have addressed fundamental aspects of septic shock resuscitation, many questions still exist. In this review, we will describe the current standards of septic shock resuscitation, but the emphasis will be placed on evolving concepts in different domains such as clinical resuscitation targets, adequate use of fluids and vasoactive drugs, refractory shock, and the use of extracorporeal therapies. Multiple research opportunities remain open, and collaborative endeavors should be performed to fill in these gaps.

Is artificial intelligence ready to solve mechanical ventilation? Computer says blow

Artificial intelligence (AI) has the potential to identify treatable phenotypes, optimise ventilation strategies, and provide clinical decision support for patients who require mechanical ventilation. Gallifant and colleagues performed a systematic review to identify studies using AI to solve a diverse range of clinical problems in the ventilated patient. They identify 95 studies, the majority of which were reported in the last 5 yr. Their findings indicate that the majority of studies have significant methodological bias and are a long way from deployment.

Association of persistent acute kidney injury and renal recovery with mortality in hospitalised patients

Objectives

Acute kidney injury (AKI) affects up to one-quarter of hospitalised patients and 60% of patients in the intensive care unit (ICU). We aim to understand the baseline characteristics of patients who will develop distinct AKI trajectories, determine the impact of persistent AKI and renal non-recovery on clinical outcomes, resource use, and assess the relative importance of AKI severity, duration and recovery on survival.

Methods

In this retrospective, longitudinal cohort study, 156 699 patients admitted to a quaternary care hospital between January 2012 and August 2019 were staged and classified (no AKI, rapidly reversed AKI, persistent AKI with and without renal recovery). Clinical outcomes, resource use and short-term and long-term survival adjusting for AKI severity were compared among AKI trajectories in all cohort and subcohorts with and without ICU admission.

Results

Fifty-eight per cent (31 500/54 212) had AKI that rapidly reversed within 48 hours; among patients with persistent AKI, two-thirds (14 122/22 712) did not have renal recovery by discharge. One-year mortality was significantly higher among patients with persistent AKI (35%, 7856/22 712) than patients with rapidly reversed AKI (15%, 4714/31 500) and no AKI (7%, 22 117/301 466). Persistent AKI without renal recovery was associated with approximately fivefold increased hazard rates compared with no AKI in all cohort and ICU and non-ICU subcohorts, independent of AKI severity.

Discussion

Among hospitalised, ICU and non-ICU patients, persistent AKI and the absence of renal recovery are associated with reduced long-term survival, independent of AKI severity.

Conclusions

It is essential to identify patients at risk of developing persistent AKI and no renal recovery to guide treatment-related decisions.

Overlapping but Disparate Inflammatory and Immunosuppressive Responses to SARS-CoV-2 and Bacterial Sepsis: An Immunological Time Course Analysis

Both severe SARS-CoV-2 infections and bacterial sepsis exhibit an immunological dyscrasia and propensity for secondary infections. The nature of the immunological dyscrasias for these differing etiologies and their time course remain unclear. In this study, thirty hospitalized patients with SARS-CoV-2 infection were compared with ten critically ill patients with bacterial sepsis over 21 days, as well as ten healthy control subjects. Blood was sampled between days 1 and 21 after admission for targeted plasma biomarker analysis, cellular phenotyping, and leukocyte functional analysis via enzyme-linked immunospot assay. We found that circulating inflammatory markers were significantly higher early after bacterial sepsis compared with SARS-CoV-2. Both cohorts exhibited profound immune suppression through 21 days (suppressed HLA-DR expression, reduced mononuclear cell IFN-gamma production), and expanded numbers of myeloid-derived suppressor cells (MDSCs). In addition, MDSC expansion and ex vivo production of IFN-gamma and TNF-alpha were resolving over time in bacterial sepsis, whereas in SARS-CoV-2, immunosuppression and inflammation were accelerating. Despite less severe initial physiologic derangement, SARS-CoV-2 patients had similar incidence of secondary infections (23% vs 30%) as bacterial sepsis patients. Finally, COVID patients who developed secondary bacterial infections exhibited profound immunosuppression evident by elevated sPD-L1 and depressed HLA-DR. Although both bacterial sepsis and SARS-CoV-2 are associated with inflammation and immune suppression, their immune dyscrasia temporal patterns and clinical outcomes are different. SARS-CoV-2 patients had less severe early inflammation and organ dysfunction but had persistent inflammation and immunosuppression and suffered worse clinical outcomes, especially when SARS-CoV-2 infection was followed by secondary bacterial infection.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications

Unsupervised learning, the task of clustering observations in such a way that observations within cluster are more similar than those assigned to other clusters is one the central tasks of data science. Its exploratory and descriptive nature make it one of the most underused and underappreciated methods. In the present chapter we describe its core function with applied examples, explore different approaches, and discuss meaningful applications of the approach for the practicing researcher.

Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

Background

Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected.

Methods

Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable.

Findings

No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms.

Interpretation

Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation.

Funding

NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)

Identification of Endotypes of Hospitalized COVID-19 Patients

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated.

Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients.

Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020.

Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers.

Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

Randomised clinical trials in critical care: past, present and future

Randomised clinical trials (RCTs) are the gold standard for providing unbiased evidence of intervention effects. Here, we provide an overview of the history of RCTs and discuss the major challenges and limitations of current critical care RCTs, including overly optimistic effect sizes; unnuanced conclusions based on dichotomization of results; limited focus on patient-centred outcomes other than mortality; lack of flexibility and ability to adapt, increasing the risk of inconclusive results and limiting knowledge gains before trial completion; and inefficiency due to lack of re-use of trial infrastructure. We discuss recent developments in critical care RCTs and novel methods that may provide solutions to some of these challenges, including a research programme approach (consecutive, complementary studies of multiple types rather than individual, independent studies), and novel design and analysis methods. These include standardization of trial protocols; alternative outcome choices and use of core outcome sets; increased acceptance of uncertainty, probabilistic interpretations and use of Bayesian statistics; novel approaches to assessing heterogeneity of treatment effects; adaptation and platform trials; and increased integration between clinical trials and clinical practice. We outline the advantages and discuss the potential methodological and practical disadvantages with these approaches. With this review, we aim to inform clinicians and researchers about conventional and novel RCTs, including the rationale for choosing one or the other methodological approach based on a thorough discussion of pros and cons. Importantly, the most central feature remains the randomisation, which provides unparalleled restriction of confounding compared to non-randomised designs by reducing confounding to chance.

Current opinion in management of septic shock due to Gram-negative bacteria

PURPOSE OF REVIEW

The COVID-19 pandemic has caused multiple challenges to ICUs, including an increased rate of secondary infections, mostly caused by Gram-negative micro-organisms. Worrying trends of resistance acquisition complicate this picture. We provide a review of the latest evidence to guide management of patients with septic shock because of Gram-negative bacteria.

RECENT FINDINGS

New laboratory techniques to detect pathogens and specific resistance patterns from the initial culture are available. Those may assist decreasing the time to adequate antimicrobial therapy and avoid unnecessary broad-spectrum antibiotic overuse. New antimicrobials, including β-lactam/β-lactamase inhibitor combinations, such as ceftolozane-tazobactam, imipenem-relebactam or meropenem-vaborbactam and cephalosporins, such as cefiderocol targeted to specific pathogens and resistance patterns are available for use in the clinical setting. Optimization of antibiotic dosing and delivery should follow pharmacokinetic and pharmacodynamic principles and wherever available therapeutic drug monitoring. Management of sepsis has brought capillary refill time back to the spotlight along with more reasoned fluid resuscitation and a moderate approach to timing of dialysis initiation.

SUMMARY

Novel rapid diagnostic tests and antimicrobials specifically targeted to Gram-negative pathogens are available and should be used within the principles of antimicrobial stewardship including de-escalation and short duration of antimicrobial therapy.

Higher levels of IgA and IgG at sepsis onset are associated with higher mortality: results from the Albumin Italian Outcome Sepsis (ALBIOS) trial

Background

The role of intravenous immunoglobulins (IVIG) during sepsis is controversial, as different trials on IVIG have observed inconsistent survival benefits. We aimed to elucidate the possible association and clinical significance between circulating levels of immunoglobulins.

Methods

In a subset of 956 patients with severe sepsis and septic shock of the multicentre, open-label RCT ALBIOS, venous blood samples were serially collected 1, 2, and 7 days after enrolment (or at ICU discharge, whichever came first). IgA, IgG and IgM concentrations were assayed in all patients on day 1 and in a subgroup of 150 patients on days 2 and 7. Ig concentrations were measured employing a turbidimetric assay, OSR61171 system.

Results

IgA on day 1 had a significant predictive value for both 28-day and 90-day mortality (28-day mortality, HR: 1.50 (95% CI 1.18–1.92); 90-day mortality, HR: 1.54 (95% CI 1.25–1.91)). IgG, but not IgM, on day 1 showed similar results for 28-day (HR 1.83 (95% CI 1.33–2.51) and 90-day mortality HR: 1.66 (95% CI 1.23–2.25)). In addition, lower levels of IgG but not of IgA and IgM, at day 1 were associated with significantly higher risk of secondary infections (533 [406–772] vs 600 [452–842] mg/dL, median [Q1–Q3], p = 0.007).

Conclusions

In the largest cohort study of patients with severe sepsis or septic shock, we found that high levels of IgA and IgG on the first day of diagnosis were associated with a decreased 90-day survival. No association was found between IgM levels and survival. As such, the assessment of endogenous immunoglobulins could be a useful tool to identify septic patients at high risk of mortality.

Trial registration #NCT00707122, Clinicaltrial.gov, registered 30 June 2008

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00952-z.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

The immunology of sepsis

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. This recently implemented definition does not capture the heterogeneity or the underlying pathophysiology of the syndrome, which is characterized by concurrent unbalanced hyperinflammation and immune suppression. Here, we review current knowledge of aberrant immune responses during sepsis and recent initiatives to stratify patients with sepsis into subgroups that are more alike from a clinical and/or pathobiological perspective, which could be key for identification of patients who are more likely to benefit from specific immune interventions.

Revising Host Phenotypes of Sepsis Using Microbiology

Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown. Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables ("host model") and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, "host-pathogen model"). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves. Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780). Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.

Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm - Machine Learning-Based Unsupervised Cluster Analysis

BACKGROUND

The hypothesis of this study is that latent class analysis could identify the subphenotypes of out-of-hospital cardiac arrest (OHCA) patients associated with the outcomes and allow us to explore heterogeneity in the effects of extracorporeal cardiopulmonary resuscitation (ECPR).Methods and Results:This study was a retrospective analysis of a multicenter prospective observational study (CRITICAL study) of OHCA patients. It included adult OHCA patients with initial shockable rhythm. Patients from 2012 to 2016 (development dataset) were included in the latent class analysis, and those from 2017 (validation dataset) were included for evaluation. The association between subphenotypes and outcomes was investigated. Further, the heterogeneity of the association between ECPR implementation and outcomes was explored. In the study results, a total of 920 patients were included for latent class analysis. Three subphenotypes (Groups 1, 2, and 3) were identified, mainly characterized by the distribution of partial pressure of O₂(PO₂), partial pressure of CO₂(PCO₂) value of blood gas assessment, cardiac rhythm on hospital arrival, and estimated glomerular filtration rate. The 30-day survival outcomes were varied across the groups: 15.7% in Group 1; 30.7% in Group 2; and 85.9% in Group 3. Further, the association between ECPR and 30-day survival outcomes by subphenotype groups in the development dataset was as varied. These results were validated using the validation dataset.

CONCLUSIONS

The latent class analysis identified 3 subphenotypes with different survival outcomes and potential heterogeneity in the effects of ECPR.

A Comparative Analysis of the Respiratory Subscore of the Sequential Organ Failure Assessment Scoring System

Rationale: The Sequential Organ Failure Assessment (SOFA) tool is a commonly used measure of illness severity. Calculation of the respiratory subscore of SOFA is frequently limited by missing arterial oxygen pressure (PaO2) data. Although missing PaO2 data are commonly replaced with normal values, the performance of different methods of substituting PaO2 for SOFA calculation is unclear. Objectives: The study objective was to compare the performance of different substitution strategies for missing PaO2 data for SOFA score calculation. Methods: This retrospective cohort study was performed using the Weill Cornell Critical Care Database for Advanced Research from a tertiary care hospital in the United States. All adult patients admitted to an intensive care unit (ICU) from 2011 to 2019 with an available respiratory SOFA score were included. We analyzed the availability of the PaO2/fraction of inspired oxygen (FiO2) ratio on the first day of ICU admission. In those without a PaO2/FiO2 ratio available, the ratio of oxygen saturation as measured by pulse oximetry to FiO2 was used to calculate a respiratory SOFA subscore according to four methods (linear substitution [Rice], nonlinear substitution [Severinghaus], modified respiratory SOFA, and multiple imputation by chained equations [MICE]) as well as the missing-as-normal technique. We then compared how well the different total SOFA scores discriminated in-hospital mortality. We performed several subgroup and sensitivity analyses. Results: We identified 35,260 unique visits, of which 9,172 included predominant respiratory failure. PaO2 data were available for 14,939 (47%). The area under the receiver operating characteristic curve for each substitution technique for discriminating in-hospital mortality was higher than that for the missing-as-normal technique (0.78 [0.77-0.79]) in all analyses (modified, 0.80 [0.79-0.81]; Rice, 0.80 [0.79-0.81]; Severinghaus, 0.80 [0.79-0.81]; and MICE, 0.80 [0.79-0.81]) (P < 0.01). Each substitution method had a higher accuracy for discriminating in-hospital mortality (MICE, 0.67; Rice, 0.67; modified, 0.66; and Severinghaus, 0.66) than the missing-as-normal technique. Model calibration for in-hospital mortality was less precise for the missing-as-normal technique than for the other substitution techniques at the lower range of SOFA and among the subgroups. Conclusions: Using physiologic and statistical substitution methods improved the total SOFA score's ability to discriminate mortality compared with the missing-as-normal technique. Treating missing data as normal may result in underreporting the severity of illness compared with using substitution. The simplicity of a direct oxygen saturation as measured by pulse oximetry/FiO2 ratio-modified SOFA technique makes it an attractive choice for electronic health record-based research. This knowledge can inform comparisons of severity of illness across studies that used different techniques.

Diagnostic Value of Thromboelastography (TEG) for the Diagnosis of Death in Infected Patients

In this study, we want to investigate the clinical value of each index of thromboelastography (TEG) on the prognosis of infected patients.The clinical baseline data and TEG test results of 431 infected patients in our hospital's emergency department between January 2018 and December 2018 were selected. And the patients were divided into death and survival groups to analyze the predictive value of each index of TEG and the joint model on the death of infected patients.In the correlation study of C-reactive protein (CRP) and procalcitonin (PCT) with each TEG parameter, CRP was positively correlated with maximum amplitude (MA, r  =  0.145, P  =  .003) and elasticity constants (E, r  =  0.098, P  =  .043), respectively. PCT was positively correlated with coagulation reaction time (R, r  =  0.124, P  =  .010) and time to MA (TMA) (r  =  0.165, P  =  .001), respectively; PCT was negatively correlated with α-Angle (r  =  0.124, P  =  .010) and coagulation index (CI, r  =  -0.108, P  =  .026), respectively. Multifactorial regression analysis showed that granulocytes, thrombocytes, platelet distribution width (PDW), and infection site were independent influences on infected patients' death. Diagnostic data showed that all eight TEG indicators had good specificity for predicting death, but all had poor sensitivity; thrombodynamic potential index (TPI) had the best diagnostic value (area under the curve, AUC  =  0.609, P  =  .002). The eight-indicator modeling of TEG showed that the TEG model combined with PCT and CRP, respectively, had lower diagnostic efficacy than PCT (AUC  =  0.756, P < .001); however, TEG had better specificity (82.73%) when diagnosed independently.The granulocytes, thrombocytes, PDW, and infection site are independent influencing factors of death in infected patients. Each index of TEG has better specificity in the diagnosis of death in infected patients.

A Pilot Double-Blind Placebo-Controlled Randomized Clinical Trial to Investigate the Effects of Early Enteral Nutrients in Sepsis

Supplemental Digital Content is available in the text.

Preclinical studies from our laboratory demonstrated therapeutic effects of enteral dextrose administration in the acute phase of sepsis, mediated by the intestine-derived incretin hormone glucose-dependent insulinotropic peptide. The current study investigated the effects of an early enteral dextrose infusion on systemic inflammation and glucose metabolism in critically ill septic patients.

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non-critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4) further development of the tools necessary for the real-time application of precision medicine approaches; 5) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts

While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87–99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

Targeted delivery of a phosphoinositide 3-kinase γ inhibitor to restore organ function in sepsis

Jaundice, the clinical hallmark of infection-associated liver dysfunction, reflects altered membrane organization of the canalicular pole of hepatocytes and portends poor outcomes. Mice lacking phosphoinositide 3-kinase-γ (PI3Kγ) are protected against membrane disintegration and hepatic excretory dysfunction. However, they exhibit a severe immune defect that hinders neutrophil recruitment to sites of infection. To exploit the therapeutic potential of PI3Kγ inhibition in sepsis, a targeted approach to deliver drugs to hepatic parenchymal cells without compromising other cells, in particular immune cells, seems warranted. Here, we demonstrate that nanocarriers functionalized through DY-635, a fluorescent polymethine dye, and a ligand of organic anion transporters can selectively deliver therapeutics to hepatic parenchymal cells. Applying this strategy to a murine model of sepsis, we observed the PI3Kγ-dependent restoration of biliary canalicular architecture, maintained excretory liver function, and improved survival without impairing host defense mechanisms. This strategy carries the potential to expand targeted nanomedicines to disease entities with systemic inflammation and concomitantly impaired barrier functionality.

Towards Understanding the Effective Use of Antibiotics for Sepsis

BACKGROUND

The benefits of early antibiotics for sepsis have recently been questioned. Evidence for this mainly comes from observational studies. The only randomized trial on this subject, the Prehospital Antibiotics Against Sepsis (PHANTASi) trial, did not find significant mortality benefits from early antibiotics. That subgroups of patients benefit from this practice is still plausible, given the heterogeneous nature of sepsis.

RESEARCH QUESTION

Do subgroups of sepsis patients experience 28-day mortality benefits from early administration of antibiotics in a prehospital setting? And what key traits drive these benefits?

STUDY DESIGN AND METHODS

We used machine learning to conduct exploratory partitioning cluster analysis to identify possible subgroups of sepsis patients who may benefit from early antibiotics. We further tested the influence of several traits within these subgroups, using a logistic regression model.

RESULTS

We found a significant interaction between age and benefits of early antibiotics (P = .03). When we adjusted for this interaction and several other confounders, there was a significant benefit of early antibiotic treatment (OR, 0.07; 95% CI, 0.01-0.79; P = .03).

INTERPRETATION

An interaction between age and benefits of early antibiotics for sepsis has not been reported before. When validated, it can have major implications for clinical practice. This new insight into benefits of early antibiotic treatment for younger sepsis patients may enable more effective care.

Characterizing preclinical sub-phenotypic models of acute respiratory distress syndrome: An experimental ovine study

The acute respiratory distress syndrome (ARDS) describes a heterogenous population of patients with acute severe respiratory failure. However, contemporary advances have begun to identify distinct sub-phenotypes that exist within its broader envelope. These sub-phenotypes have varied outcomes and respond differently to several previously studied interventions. A more precise understanding of their pathobiology and an ability to prospectively identify them, may allow for the development of precision therapies in ARDS. Historically, animal models have played a key role in translational research, although few studies have so far assessed either the ability of animal models to replicate these sub-phenotypes or investigated the presence of sub-phenotypes within animal models. Here, in three ovine models of ARDS, using combinations of oleic acid and intravenous, or intratracheal lipopolysaccharide, we investigated the presence of sub-phenotypes which qualitatively resemble those found in clinical cohorts. Principal Component Analysis and partitional clustering identified two clusters, differentiated by markers of shock, inflammation, and lung injury. This study provides a first exploration of ARDS phenotypes in preclinical models and suggests a methodology for investigating this phenomenon in future studies.

Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery

OBJECTIVES

Early antibiotic administration is a central component of sepsis guidelines, and delays may increase mortality. However, prior studies have examined the delay to first antibiotic administration as a single time period even though it contains two distinct processes: antibiotic ordering and antibiotic delivery, which can each be targeted for improvement through different interventions. The objective of this study was to characterize and compare patients who experienced order or delivery delays, investigate the association of each delay type with mortality, and identify novel patient subphenotypes with elevated risk of harm from delays.

DESIGN

Retrospective analysis of multicenter inpatient data.

SETTING

Two tertiary care medical centers (2008-2018, 2006-2017) and four community-based hospitals (2008-2017).

PATIENTS

All patients admitted through the emergency department who met clinical criteria for infection.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patient demographics, vitals, laboratory values, medication order and administration times, and in-hospital survival data were obtained from the electronic health record. Order and delivery delays were calculated for each admission. Adjusted logistic regression models were used to examine the relationship between each delay and in-hospital mortality. Causal forests, a machine learning method, was used to identify a high-risk subgroup. A total of 60,817 admissions were included, and delays occurred in 58% of patients. Each additional hour of order delay (odds ratio, 1.04; 95% CI, 1.03-1.05) and delivery delay (odds ratio, 1.05; 95% CI, 1.02-1.08) was associated with increased mortality. A patient subgroup identified by causal forests with higher comorbidity burden, greater organ dysfunction, and abnormal initial lactate measurements had a higher risk of death associated with delays (odds ratio, 1.07; 95% CI, 1.06-1.09 vs odds ratio, 1.02; 95% CI, 1.01-1.03).

CONCLUSIONS

Delays in antibiotic ordering and drug delivery are both associated with a similar increase in mortality. A distinct subgroup of high-risk patients exist who could be targeted for more timely therapy.

Serum Biomarkers of Regeneration and Plasticity are Associated with Functional Outcome in Pediatric Neurocritical Illness: An Exploratory Study

BACKGROUND/OBJECTIVE

Pediatric neurocritical care survivorship is frequently accompanied by functional impairments. Lack of prognostic biomarkers is a barrier to early identification and management of impairment. We explored the association between blood biomarkers and functional impairment in children with acute acquired brain injury.

METHODS

This study is a secondary analysis of a randomized control trial evaluating early versus usual care rehabilitation in the pediatric intensive care unit (PICU). Forty-four children (17 [39%] female, median age 11 [interquartile range 6-13] years) with acute acquired brain injury admitted to the PICU were studied. A single center obtained serum samples on admission days 0, 1, 3, 5, and the day closest to hospital discharge. Biomarkers relevant to brain injury (neuron specific enolase [NSE], S100b), inflammation (interleukin [IL-6], C-reactive protein), and regeneration (brain-derived neurotrophic factor [BDNF], vascular endothelial growth factor [VEGF]) were collected. Biomarkers were analyzed using a Luminex® bioassay. Functional status scale (FSS) scores were abstracted from the medical record. New functional impairment was defined as a (worse) FSS score at hospital discharge compared to pre-PICU (baseline). Individual biomarker fluorescence index (FI) values for each sample collection day were correlated with new functional impairment using Spearman rank correlation coefficient (ρ). Trends in repeated measures of biomarker FI over time were explored graphically, and the association between repeated measures of biomarker FI and new functional impairment was analyzed using covariate adjusted linear mixed-effect models.

RESULTS

Functional impairment was inversely correlated with markers of regeneration and plasticity including BDNF at day 3 (ρ =  - 0.404, p = .015), day 5 (ρ =  - 0.549, p = 0.005) and hospital discharge (ρ =  - 0.420, p = 0.026) and VEGF at day 1 (ρ =  - 0.282, p = 0.008) and hospital discharge (ρ =  - 0.378, p = 0.047), such that lower levels of both markers at each time point were associated with greater impairment. Similarly, repeated measures of BDNF and VEGF were inversely correlated with new functional impairment (B =  - 0.001, p = 0.001 and B =  - 0.001, p = 0.003, respectively). NSE, a biomarker of acute brain injury, showed a positive correlation between day 0 levels and new functional impairment (ρ = 0.320, p = 0.044).

CONCLUSIONS

Blood-based biomarkers of regeneration and plasticity may hold prognostic utility for functional impairment among pediatric patients with neurocritical illness and warrant further investigation.

Machine learning in gastrointestinal surgery

Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.

Longitudinal analysis of symptom-based clustering in patients with primary Sjogren's syndrome: a prospective cohort study with a 5-year follow-up period

BACKGROUND

Sjogren's syndrome (SS) is a heterogenous disease with various phenotypes. We aimed to provide a relevant subclassification based on symptom-based clustering for patients with primary (p) SS.

METHODS

Data from patients in a prospective pSS cohort in Korea were analysed. Latent class analysis (LCA) was performed using patient reported outcomes, including pain, fatigue, dryness, and anxiety/depression. Clinical and laboratory differences between the classes were analysed. Latent transition analysis (LTA) was applied to the longitudinal data (annually for up to 5 years) to assess temporal stability of the classifications.

RESULTS

LCA identified three classes among 341 patients with pSS (i.e., 'high symptom burden', 'dryness dominant', 'low symptom burden'). Each group had distinct laboratory and clinical phenotypes. LTA revealed that class membership remained stable over time. Baseline class predicted future salivary gland function and damage accrual represented by a Sjogren's syndrome disease damage index.

CONCLUSION

Symptom-based clustering of heterogenous patients with primary Sjogren's syndrome provided a relevant classification supported by temporal stability over time and distinct phenotypes between the classes. This clustering strategy may provide more homogenous groups of pSS patients for novel treatment development and predict future phenotypic evolvement.

The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care?

There is ongoing demographic ageing and increasing longevity of the population, with previously devastating and often-fatal diseases now transformed into chronic conditions. This is turning multi-morbidity into a major challenge in the world of critical care. After many years of research and innovation, mainly in geriatric care, the concept of multi-morbidity now requires fine-tuning to support decision-making for patients along their whole trajectory in healthcare, including in the intensive care unit (ICU). This article will discuss current challenges and present approaches to adapt critical care services to the needs of these patients.

Understanding the Host in the Management of Pneumonia. An Official American Thoracic Society Workshop Report

Pneumonia causes a significant burden of disease worldwide. Although all populations are at risk of pneumonia, those at extremes of age and those with immunosuppressive disorders, underlying respiratory disease, and critical illness are particularly vulnerable. Although clinical practice guidelines addressing the management and treatment of pneumonia exist, few of the supporting studies focus on the crucial contributions of the host in pneumonia pathogenesis and recovery. Such essential considerations include the host risk factors that lead to susceptibility to lung infections; biomarkers reflecting the host response and the means to pursue host-directed pneumonia therapy; systemic effects of pneumonia on the host; and long-term health outcomes after pneumonia. To address these gaps, the Pneumonia Working Group of the Assembly on Pulmonary Infection and Tuberculosis led a workshop held at the American Thoracic Society meeting in May 2018 with overarching objectives to foster attention, stimulate research, and promote funding for short-term and long-term investigations into the host contributions to pneumonia. The workshop involved participants from various disciplines with expertise in lung infection, pneumonia, sepsis, immunocompromised patients, translational biology, data science, genomics, systems biology, and clinical trials. This workshop report summarizes the presentations and discussions and important recommendations for future clinical pneumonia studies. These recommendations include establishing consensus disease and outcome definitions, improved phenotyping, development of clinical study networks, standardized data and biospecimen collection and protocols, and development of innovative trial designs.

Computerized Mortality Prediction for Community-acquired Pneumonia at 117 Veterans Affairs Medical Centers

Rationale: Computerized severity assessment for community-acquired pneumonia could improve consistency and reduce clinician burden. Objectives: To develop and compare 30-day mortality-prediction models using electronic health record data, including a computerized score with all variables from the original Pneumonia Severity Index (PSI) except confusion and pleural effusion ("ePSI score") versus models with additional variables. Methods: Among adults with community-acquired pneumonia presenting to emergency departments at 117 Veterans Affairs Medical Centers between January 1, 2006, and December 31, 2016, we compared an ePSI score with 10 novel models employing logistic regression, spline, and machine learning methods using PSI variables, age, sex and 26 physiologic variables as well as all 69 PSI variables. Models were trained using encounters before January 1, 2015; tested on encounters during and after January 1, 2015; and compared using the areas under the receiver operating characteristic curve, confidence intervals, and patient event rates at a threshold PSI score of 970. Results: Among 297,498 encounters, 7% resulted in death within 30 days. When compared using the ePSI score (confidence interval [CI] for the area under the receiver operating characteristic curve, 0.77-0.78), performance increased with model complexity (CI for the logistic regression PSI model, 0.79-0.80; CI for the boosted decision-tree algorithm machine learning PSI model using the Extreme Gradient Boosting algorithm [mlPSI] with the 19 original PSI factors, 0.83-0.85) and the number of variables (CI for the logistic regression PSI model using all 69 variables, 0.84-085; CI for the mlPSI with all 69 variables, 0.86-0.87). Models limited to age, sex, and physiologic variables also demonstrated high performance (CI for the mlPSI with age, sex, and 26 physiologic factors, 0.84-0.85). At an ePSI score of 970 and a mortality-risk cutoff of <2.7%, the ePSI score identified 31% of all patients as being at "low risk"; the mlPSI with age, sex, and 26 physiologic factors identified 53% of all patients as being at low risk; and the mlPSI with all 69 variables identified 56% of all patients as being at low risk, with similar rates of mortality, hospitalization, and 7-day secondary hospitalization being determined. Conclusions: Computerized versions of the PSI accurately identified patients with pneumonia who were at low risk of death. More complex models classified more patients as being at low risk of death and as having similar adverse outcomes.

Dynamic monitoring of kidney injury status over 3 days in the intensive care unit as a sepsis phenotype associated with hospital mortality and hyperinflammation

Background

Sepsis-associated acute kidney injury (AKI) often worsens with the deterioration of a patient's condition. Therefore, we hypothesized that monitoring AKI dynamically from day 1 to day 3 was potential to predict hospital mortality. Specifically, we explored whether monitoring AKI dynamically in the intensive care unit (ICU) could be a sepsis phenotype predictive of mortality. A new classification was established based on the change in the AKI stage from admission day 1 and day 3. We compared the hospital mortality, cytokines, and immune response pattern between each group.

Methods

We retrospectively enrolled 523 patients with sepsis, and we calculated the AKI stages on day 1 and day 3 admission to ICUs.

Among these 523 people, 388 of them were assigned to normal, improved, and deteriorated groups according to the changes in the AKI stages. 263 of which did not develop AKI on day 1 and day 3 (normal group). The AKI stage improved in 68 patients (improved group) and worsened in 57 (deteriorated group). We compared the mortality rates between the groups, and identified the relationship between the dynamic AKI status, immune response patterns, and cytokine levels.

Results

The hospital mortality rate in the deteriorated group was higher than that in the non-deteriorated group (combination of normal and improved group) (p = 0.004). Additionally, according to the Kaplan–Meier analysis, the non-deteriorated group had a distinct hospital survival curve (p = 0.004). Furthermore, both the overexpression of tumor necrosis factor-α and decreased monocyte expression of human leukocyte antigen-DR were present in the deteriorated group.

Conclusions

The deteriorated group was associated with a higher hospital mortality rate, potentially resulting from an abnormal inflammatory response. Worsening AKI in the first 3 days of ICU admission may be a sepsis phenotype predictive of hospital mortality.

A prospective, randomised clinical study comparing triple therapy regimen to hydrocortisone monotherapy in reducing mortality in septic shock patients

OBJECTIVES

This prospective, comparative and randomised clinical study evaluated the effectiveness of triple therapy regimen (hydrocortisone, thiamine and vitamin C) versus hydrocortisone alone in reducing the mortality rate and preventing progressive organ dysfunction in septic shock patients.

METHODS

A total of 94 patients were randomly assigned to one of two groups: the first group received hydrocortisone 50 mg/6-h IV for 7 days or till intensive care unit (ICU) discharge, if sooner, followed by tapering. The second group received hydrocortisone 50 mg/6-h IV for 7 days or ICU discharge followed by tapering, vitamin C 1.5 g/6-h IV for 4 days or till ICU discharge and thiamine 200 mg/12-h IV for 4 days or till ICU discharge.

RESULTS

The triple therapy regimen showed a non-significant reduction in 28-day mortality compared to hydrocortisone alone (17 [36.2%] vs. 21 [44.7%]; P = .4005), but it was significantly lower than the control group regarding shock time and the duration of vasopressor use in days (4.000 [3.000-7.000]; 5.000 [4.000-8.000], [P = .0100]). The patients in the control group were likely to get 0.59 more in SCr level than those in the intervention group by a linear regression model which was significant (P < .05). Also, the number of patients who developed a fever after 216 hours was significantly higher in the control group (P value = .0299).

CONCLUSION

Vitamin C, thiamine, and hydrocortisone regimen for septic shock management showed non-significant efficacy in decreasing 28-day mortality when compared to hydrocortisone monotherapy. On the other hand, it showed significant efficacy in decreasing the shock time and duration on vasopressors.

Limits of the Glasgow Coma Scale When Assessing for Sepsis in Allogeneic Hematopoietic Cell Transplant Recipients

BACKGROUND

The well-documented association between acute mental status changes and sepsis development and progression makes acute mental status an attractive factor for sepsis screening tools. However, the usefulness of acute mental status within these criteria is limited to the frequency and accuracy of its capture. The Glasgow Coma Scale (GCS) score-the acute mental status indicator in many clinical sepsis criteria-is infrequently captured among allogeneic hematopoietic cell transplant recipients with suspected infections, and its ability to serve as an indicator of acute mental status among this high-risk population is unknown.

OBJECTIVE

We evaluated the GCS score as an indicator of acute mental status during the 24 hours after suspected infection onset among allogeneic hematopoietic cell transplant recipients.

METHODS

Using data from the first 100 days posttransplant for patients transplanted at a single center between September 2010 and July 2017, we evaluated the GCS score as an indicator of documented acute mental status during the 24 hours after suspected infection onset. From all inpatients with suspected infections, we randomly selected a cohort based on previously published estimates of GCS score frequency among hematopoietic cell transplant recipients with suspected infections and performed chart review to ascertain documentation of clinical acute mental status within the 24 hours after suspected infection onset.

RESULTS

A total of 773 patients had ≥1 suspected infections and experienced 1,655 suspected infections during follow-up-625 of which had an accompanying GCS score. Among the randomly selected cohort of 100 persons with suspected infection, 28 were accompanied with documented acute mental status, including 18 without a recorded GCS. In relation to documented acute mental status, the GCS had moderate to high sensitivity and high specificity.

DISCUSSION

These data indicate that, among allogeneic hematopoietic cell transplant recipients with suspected infections, the GCS scores are infrequently collected and have a moderate sensitivity. If sepsis screening tools inclusive of acute mental status changes are to be used, nursing teams need to increase measurement of GCS scores among high sepsis risk patients or identify a standard alternative indicator.

Identification of distinct clinical phenotypes of acute respiratory distress syndrome with differential responses to treatment

Background

Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome, and the identification of homogeneous subgroups and phenotypes is the first step toward precision critical care. We aimed to explore whether ARDS phenotypes can be identified using clinical data, are reproducible and are associated with clinical outcomes and treatment response.

Methods

This study is based on a retrospective analysis of data from the telehealth intensive care unit (eICU) collaborative research database and three ARDS randomized controlled trials (RCTs) (ALVEOLI, FACTT and SAILS trials). We derived phenotypes in the eICU by cluster analysis based on clinical data and compared the clinical characteristics and outcomes of each phenotype. The reproducibility of the derived phenotypes was tested using the data from three RCTs, and treatment effects were evaluated.

Results

Three clinical phenotypes were identified in the training cohort of 3875 ARDS patients. Of the three phenotypes identified, phenotype I (n = 1565; 40%) was associated with fewer laboratory abnormalities, less organ dysfunction and the lowest in-hospital mortality rate (8%). Phenotype II (n = 1232; 32%) was correlated with more inflammation and shock and had a higher mortality rate (18%). Phenotype III (n = 1078; 28%) was strongly correlated with renal dysfunction and acidosis and had the highest mortality rate (22%). These results were validated using the data from the validation cohort (n = 3670) and three RCTs (n = 2289) and had reproducibility. Patients with these ARDS phenotypes had different treatment responses to randomized interventions. Specifically, in the ALVEOLI cohort, the effects of ventilation strategy (high PEEP vs low PEEP) on ventilator-free days differed by phenotype (p = 0.001); in the FACTT cohort, there was a significant interaction between phenotype and fluid-management strategy for 60-day mortality (p = 0.01). The fluid-conservative strategy was associated with improved mortality in phenotype II but had the opposite effect in phenotype III.

Conclusion

Three clinical phenotypes of ARDS were identified and had different clinical characteristics and outcomes. The analysis shows evidence of a phenotype-specific treatment benefit in the ALVEOLI and FACTT trials. These findings may improve the identification of distinct subsets of ARDS patients for exploration in future RCTs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03734-y.

Acute myocardial injury secondary to severe acute liver failure: A retrospective analysis supported by animal data

To investigate whether acute liver failure (ALF) leads to secondary acute myocardial injury, 100 ALF patients that were retrospectively identified in a single center based on ICD 10 codes and 8 rats from an experimental study that died early after bile duct ligation (BDL) were examined. Creatine kinase (CK), creatine kinase-MB isoenzyme (CKMB) and cardiac troponin-I (cTnI) were analyzed as markers of myocardial injury. For histological analysis, hematoxylin-eosin (HE), elastic Van Gieson (EVG), CD41 and myeloperoxidase were used to stain rat hearts. Major adverse cardiac events (MACEs) were a critical factor for mortality (p = 0.037) in human ALF. Deceased patients exhibited higher levels of CKMB than survivors (p = 0.023). CKMB was a predictor of mortality in ALF (p = 0.013). Animals that died early after BDL exhibited increased cTnI, CKMB, tumor necrosis factor α (TNFα) and interleukin-6 (IL-6) levels compared to controls (cTnI: p = 0.011, CKMB: p = 0.008, TNFα: p = 0.003, IL-6: p = 0.006). These animals showed perivascular lesions and wavy fibers, microthrombi and neutrophilic infiltration in the heart. MACEs are decisive for mortality in human ALF, and elevated CKMB values indicate that this might be due to structural myocardial damage. Accordingly, CKMB was found to have predictive value for mortality in ALF. The results are substantiated by data from a rat BDL model demonstrating diffuse myocardial injury.

Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

This study illustrated that lipoprotein and lipid metabolism might play a significant role in patients with sepsis and that complement activation was significantly enriched in patients with sepsis-associated encephalopathy.

Sepsis is defined as an organ dysfunction syndrome and it has high mortality worldwide. This study analysed the proteome of serum from patients with sepsis to characterize the pathological mechanism and pathways involved in sepsis. A total of 59 patients with sepsis were enrolled for quantitative proteomic analysis. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network specific to sepsis. Key regulatory modules that were detected were highly correlated with sepsis patients and related to multiple functional groups, including plasma lipoprotein particle remodeling, inflammatory response, and wound healing. Complement activation was significantly associated with sepsis-associated encephalopathy. Triglyceride/cholesterol homeostasis was found to be related to sepsis-associated acute kidney injury. Twelve hub proteins were identified, which might be predictive biomarkers of sepsis. External validation of the hub proteins showed their significantly differential expression in sepsis patients. This study identified that plasma lipoprotein processes played a crucial role in sepsis patients, that complement activation contributed to sepsis-associated encephalopathy, and that triglyceride/cholesterol homeostasis was associated with sepsis-associated acute kidney injury.

Association between sepsis survivorship and long-term cardiovascular outcomes in adults: a systematic review and meta-analysis

PURPOSE

We aimed to determine the association between sepsis and long-term cardiovascular events.

METHODS

We conducted a systematic review of observational studies evaluating post-sepsis cardiovascular outcomes in adult sepsis survivors. MEDLINE, Embase, and the Cochrane Controlled Trials Register and Database of Systematic Reviews were searched from inception until April 21st, 2021. Two reviewers independently extracted individual study data and evaluated risk of bias. Random-effects models estimated the pooled crude cumulative incidence and adjusted hazard ratios (aHRs) of cardiovascular events compared to either non-septic hospital survivors or population controls. Primary outcomes included myocardial infarction, stroke, and congestive heart failure; outcomes were analysed at maximum reported follow-up (from 30 days to beyond 5 years post-discharge).

RESULTS

Of 12,649 screened citations, 27 studies (25 cohort studies, 2 case-crossover studies) were included with a median of 4,289 (IQR 502-68,125) sepsis survivors and 18,399 (IQR 4,028-83,506) controls per study. The pooled cumulative incidence of myocardial infarction, stroke, and heart failure in sepsis survivors ranged from 3 to 9% at longest reported follow-up. Sepsis was associated with a higher long-term risk of myocardial infarction (aHR 1.77 [95% CI 1.26 to 2.48]; low certainty), stroke (aHR 1.67 [95% CI 1.37 to 2.05]; low certainty), and congestive heart failure (aHR 1.65 [95% CI 1.46 to 1.86]; very low certainty) compared to non-sepsis controls.

CONCLUSIONS

Surviving sepsis may be associated with a long-term, excess hazard of late cardiovascular events which may persist for at least 5 years following hospital discharge.

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

Clinical and biological clusters of sepsis patients using hierarchical clustering

Background

Heterogeneity in sepsis expression is multidimensional, including highly disparate data such as the underlying disorders, infection source, causative micro-organismsand organ failures. The aim of the study is to identify clusters of patients based on clinical and biological characteristic available at patients’ admission.

Methods

All patients included in a national prospective multicenter ICU cohort OUTCOMEREA and admitted for sepsis or septic shock (Sepsis 3.0 definition) were retrospectively analyzed. A hierarchical clustering was performed in a training set of patients to build clusters based on a comprehensive set of clinical and biological characteristics available at ICU admission. Clusters were described, and the 28-day, 90-day, and one-year mortality were compared with log-rank rates. Risks of mortality were also compared after adjustment on SOFA score and year of ICU admission.

Results

Of the 6,046 patients with sepsis in the cohort, 4,050 (67%) were randomly allocated to the training set. Six distinct clusters were identified: young patients without any comorbidities, admitted in ICU for community-acquired pneumonia (n = 1,603 (40%)); young patients without any comorbidities, admitted in ICU for meningitis or encephalitis (n = 149 (4%)); elderly patients with COPD, admitted in ICU for bronchial infection with few organ failures (n = 243 (6%)); elderly patients, with several comorbidities and organ failures (n = 1,094 (27%)); patients admitted after surgery, with a nosocomial infection (n = 623 (15%)); young patients with immunosuppressive conditions (e.g., AIDS, chronic steroid therapy or hematological malignancy) (n = 338 (8%)). Clusters differed significantly in early or late mortality (p < .001), even after adjustment on severity of organ dysfunctions (SOFA) and year of ICU admission.

Conclusions

Clinical and biological features commonly available at ICU admission of patients with sepsis or septic shock enabled to set up six clusters of patients, with very distinct outcomes. Considering these clusters may improve the care management and the homogeneity of patients in future studies.