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

Association Between Premorbid Beta-Blocker Exposure and Sepsis Outcomes-The Beta-Blockers in European and Australian/American Septic Patients (BEAST) Study

OBJECTIVES

To examine the effect of premorbid β-blocker exposure on mortality and organ dysfunction in sepsis.

DESIGN

Retrospective observational study.

SETTING

ICUs in Australia, the Czech Republic, and the United States.

PATIENTS

Total of 4,086 critical care patients above 18 years old with sepsis between January 2014 and December 2018.

INTERVENTION

Premorbid beta-blocker exposure.

MEASUREMENTS AND MAIN RESULTS

One thousand five hundred fifty-six patients (38%) with premorbid β-blocker exposure were identified. Overall ICU mortality rate was 15.1%. In adjusted models, premorbid β-blocker exposure was associated with decreased ICU (adjusted odds ratio, 0.80; 95% CI, 0.66-0.97; p = 0.025) and hospital (adjusted odds ratio, 0.83; 95% CI, 0.71-0.99; p = 0.033) mortality. The risk reduction in ICU mortality of 16% was significant (hazard ratio, 0.84, 95% CI, 0.71-0.99; p = 0.037). In particular, exposure to noncardioselective β-blocker before septic episode was associated with decreased mortality. Sequential Organ Failure Assessment score analysis showed that premorbid β-blocker exposure had potential benefits in reducing respiratory and neurologic dysfunction.

CONCLUSIONS

This study suggests that β-blocker exposure prior to sepsis, especially to noncardioselective β blockers, may be associated with better outcome. The findings suggest prospective evaluation of β-blocker use in the management of sepsis.

Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes

Background

Acute respiratory distress syndrome remains a heterogeneous syndrome for clinicians and researchers difficulting successful tailoring of interventions and trials. To this moment, phenotyping of this syndrome has been approached by means of inflammatory laboratory panels. Nevertheless, the systemic and inflammatory expression of acute respiratory distress syndrome might not reflect its respiratory mechanics and gas exchange.

Methods

Retrospective analysis of a prospective cohort of two hundred thirty-eight patients consecutively admitted patients under mechanical ventilation presenting with acute respiratory distress syndrome. All patients received standardized monitoring of clinical variables, respiratory mechanics and computed tomography scans at predefined PEEP levels. Employing latent class analysis, an unsupervised structural equation modelling method, on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes at a PEEP level of 5cmH₂O, distinct pulmonary phenotypes of acute respiratory distress syndrome were identified.

Results

Latent class analysis was applied to 54 respiratory mechanics, gas-exchange and CT-derived gas- and tissue-volume variables, and a two-class model identified as best fitting. Phenotype 1 (non-recruitable) presented lower respiratory system elastance, alveolar dead space and amount of potentially recruitable lung volume than phenotype 2 (recruitable). Phenotype 2 (recruitable) responded with an increase in ventilated lung tissue, compliance and PaO₂/FiO₂ ratio (p < 0.001), in addition to a decrease in alveolar dead space (p < 0.001), to a standardized recruitment manoeuvre. Patients belonging to phenotype 2 (recruitable) presented a higher intensive care mortality (hazard ratio 2.9, 95% confidence interval 1.7–2.7, p = 0.001).

Conclusions

The present study identifies two ARDS phenotypes based on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes. These phenotypes are characterized by distinctly diverse responses to a standardized recruitment manoeuvre and by a diverging mortality. Given multicentre validation, the simple and rapid identification of these pulmonary phenotypes could facilitate enrichment of future prospective clinical trials addressing mechanical ventilation strategies in ARDS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03578-6.

D-Dimer as Biomarker for Early Prediction of Clinical Outcomes in Patients With Severe Invasive Infections Due to Streptococcus Pneumoniae and Neisseria Meningitidis

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection; no current clinical measure adequately reflects the concept of dysregulated response. Coagulation plays a pivotal role in the normal response to pathogens (immunothrombosis), thus the evolution toward sepsis-induced coagulopathy could be individuate through coagulation/fibrinolysis-related biomarkers. We focused on the role of D-dimer assessed within 24 h after admission in predicting clinical outcomes in a cohort of 270 patients hospitalized in a 79 months period for meningitis and/or bloodstream infections due to Streptococcus pneumoniae (n = 162) or Neisseria meningitidis (n = 108). Comparisons were performed with unpaired t-test, Mann-Whitney-test or chi-squared-test with continuity correction, as appropriate, and multivariable logistic regression analysis was performed with Bayesian model averaging. In-hospital mortality was 14.8% for the overall population, significantly higher in S. pneumoniae than in N. meningitidis patients: 19.1 vs. 8.3%, respectively (p = 0.014). At univariable logistic regression analysis the following variables were significantly associated with in-hospital mortality: pneumococcal etiology, female sex, age, ICU admission, SOFA score, septic shock, MODS, and D-dimer levels. At multivariable analysis D-dimer showed an effect only in N. meningitidis subgroup: as 500 ng/mL of D-dimer increased, the probability of unfavorable outcome increased on average by 4%. Median D-dimer was significantly higher in N. meningitidis than in S. pneumoniae patients (1,314 vs. 1,055 ng/mL, p = 0.009). For N. meningitidis in-hospital mortality was 0% for D-dimer <500 ng/mL, very low (3.5%) for D-dimer <7,000 ng/mL, and increased to 26.1% for D-dimer >7,000 ng/mL. Kaplan-Meier analysis of in-hospital mortality showed for N. meningitidis infections a statistically significant difference for D-dimer >7,000 ng/mL compared to values <500 ng/mL (p = 0.021) and 500-3,000 ng/mL (p = 0.002). For S. pneumoniae the mortality risk resulted always high, over 10%, irrespective by D-dimer values. In conclusion, D-dimer is rapid to be obtained, at low cost and available everywhere, and can help stratify the risk of in-hospital mortality and complications in patients with invasive infections due to N. meningitidis: D-dimer <500 ng/mL excludes any further complications, and a cut-off of 7,000 ng/mL seems able to predict a significantly increased mortality risk from much <10% to over 25%.

Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data

Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system. Compared to an existing gold standard of manually collected data at our institution, CEDAR was statistically similar in most measures, including patient demographics and sepsis-related organ failure assessment (SOFA) scores. Additionally, CEDAR automated data extraction obviated the need for manual collection of 550 variables. Critically, during the spring 2020 COVID-19 surge in New York City, a modified version of CEDAR supported pandemic response efforts, including clinical operations and research. Other academic medical centers may find value in using the CEDAR method to automate data extraction from EHR systems to support ICU activities.

Lipid and Lipoprotein Dysregulation in Sepsis: Clinical and Mechanistic Insights into Chronic Critical Illness

In addition to their well-characterized roles in metabolism, lipids and lipoproteins have pleiotropic effects on the innate immune system. These undergo clinically relevant alterations during sepsis and acute inflammatory responses. High-density lipoprotein (HDL) plays an important role in regulating the immune response by clearing bacterial toxins, supporting corticosteroid release, decreasing platelet aggregation, inhibiting endothelial cell apoptosis, reducing the monocyte inflammatory response, and inhibiting expression of endothelial cell adhesion molecules. It undergoes quantitative as well as qualitative changes which can be measured using the HDL inflammatory index (HII). Pro-inflammatory, or dysfunctional HDL (dysHDL) lacks the ability to perform these functions, and we have also found it to independently predict adverse outcomes and organ failure in sepsis. Another important class of lipids known as specialized pro-resolving mediators (SPMs) positively affect the escalation and resolution of inflammation in a temporal fashion. These undergo phenotypic changes in sepsis and differ significantly between survivors and non-survivors. Certain subsets of sepsis survivors go on to have perilous post-hospitalization courses where this inflammation continues in a low grade fashion. This is associated with immunosuppression in a syndrome of persistent inflammation, immunosuppression, and catabolism syndrome (PICS). The continuous release of tissue damage-related patterns and viral reactivation secondary to immunosuppression feed this chronic cycle of inflammation. Animal data indicate that dysregulation of endogenous lipids and SPMs play important roles in this process. Lipids and their associated pathways have been the target of many clinical trials in recent years which have not shown mortality benefit. These results are limited by patient heterogeneity and poor animal models. Considerations of sepsis phenotypes and novel biomarkers in future trials are important factors to be considered in future research. Further characterization of lipid dysregulation and chronic inflammation during sepsis will aid mortality risk stratification, detection of sepsis, and inform individualized pharmacologic therapies.

Pattern of inflammatory immune response determines the clinical course and outcome of COVID-19: unbiased clustering analysis

The objective of the study was to identify distinct patterns in inflammatory immune responses of COVID-19 patients and to investigate their association with clinical course and outcome. Data from hospitalized COVID-19 patients were retrieved from electronic medical record. Supervised k-means clustering of serial C-reactive protein levels (CRP), absolute neutrophil counts (ANC), and absolute lymphocyte counts (ALC) was used to assign immune responses to one of three groups. Then, relationships between patterns of inflammatory responses and clinical course and outcome of COVID-19 were assessed in a discovery and validation cohort. Unbiased clustering analysis grouped 105 patients of a discovery cohort into three distinct clusters. Cluster 1 (hyper-inflammatory immune response) was characterized by high CRP levels, high ANC, and low ALC, whereas Cluster 3 (hypo-inflammatory immune response) was associated with low CRP levels and normal ANC and ALC. Cluster 2 showed an intermediate pattern. All patients in Cluster 1 required oxygen support whilst 61% patients in Cluster 2 and no patient in Cluster 3 required supplementary oxygen. Two (13.3%) patients in Cluster 1 died, whereas no patient in Clusters 2 and 3 died. The results were confirmed in an independent validation cohort of 116 patients. We identified three different patterns of inflammatory immune response to COVID-19. Hyper-inflammatory immune responses with elevated CRP, neutrophilia, and lymphopenia are associated with a severe disease and a worse outcome. Therefore, targeting the hyper-inflammatory response might improve the clinical outcome of COVID-19.

Unsupervised phenotyping of sepsis using nonnegative matrix factorization of temporal trends from a multivariate panel of physiological measurements

BACKGROUND

Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care.

METHODS

Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications.

RESULTS

We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characterized by low age (mean age, 60 years), a balanced gender ratio (male-to-female ratio, 50-to-50), the least complicated conditions, and a uniquely high incidence of neurologic disease. These phenotypes were validated to be prognostic factors of mortality for sepsis patients.

CONCLUSIONS

Our results suggest that these phenotypes can be used to develop targeted therapies based on phenotypic heterogeneity and algorithms designed for monitoring, validating and intervening clinical decisions for sepsis patients.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

The Range of Cardiogenic Shock Survival by Clinical Stage: Data From the Critical Care Cardiology Trials Network Registry

OBJECTIVES

Cardiogenic shock presents with variable severity. Categorizing cardiogenic shock into clinical stages may improve risk stratification and patient selection for therapies. We sought to determine whether a structured implementation of the 2019 Society for Cardiovascular Angiography and Interventions clinical cardiogenic shock staging criteria that is ascertainable in clinical registries discriminates mortality in a contemporary population with or at-risk for cardiogenic shock.

DESIGN

We developed a pragmatic application of the Society for Cardiovascular Angiography and Interventions cardiogenic shock staging criteria-A (at-risk), B (beginning), C (classic cardiogenic shock), D (deteriorating), or E (extremis)-and examined outcomes by stage.

SETTING

The Critical Care Cardiology Trials Network is an investigator-initiated multicenter research collaboration coordinated by the TIMI Study Group (Boston, MA). Consecutive admissions with or at-risk for cardiogenic shock during two annual 2-month collection periods (2017-2019) were analyzed.

PATIENTS

Patients with or at-risk for cardiogenic shock.

MEASUREMENTS AND MAIN RESULTS

Of 8,240 CICU admissions reviewed, 1,991 (24%) had or were at-risk for cardiogenic shock. Distributions across the five stages were as follows: A: 33%; B: 7%; C: 16%; D: 23%; and E: 21%. Overall in-hospital mortality among patients with established cardiogenic shock was 39%; however, mortality varied from only 15.8% to 32.1% to 62.5% across stages C, D, and E (Cochran-Armitage ptrend < 0.0001). The Society for Cardiovascular Angiography and Interventions stages improved mortality prediction beyond the Sequential Organ Failure Assessment and Intra-Aortic Balloon Pumpin Cardiogenic Shock II scores.

CONCLUSIONS

Although overall mortality in cardiogenic shock remains high, it varies considerably based on clinical stage, identifying stage C as relatively lower risk. We demonstrate a pragmatic adaptation of the Society for Cardiovascular Angiography and Interventions cardiogenic shock stages that effectively stratifies mortality risk and could be leveraged for future clinical research.

Safety-driven design of machine learning for sepsis treatment

Machine learning (ML) has the potential to bring significant clinical benefits. However, there are patient safety challenges in introducing ML in complex healthcare settings and in assuring the technology to the satisfaction of the different regulators. The work presented in this paper tackles the urgent problem of proactively assuring ML in its clinical context as a step towards enabling the safe introduction of ML into clinical practice. In particular, the paper considers the use of deep Reinforcement Learning, a type of ML, for sepsis treatment. The methodology starts with the modelling of a clinical workflow that integrates the ML model for sepsis treatment recommendations. Then safety analysis is carried out based on the clinical workflow, identifying hazards and safety requirements for the ML model. In this paper the design of the ML model is enhanced to satisfy the safety requirements for mitigating a major clinical hazard: sudden change of vasopressor dose. A rigorous evaluation is conducted to show how these requirements are met. A safety case is presented, providing a basis for regulators to make a judgement on the acceptability of introducing the ML model into sepsis treatment in a healthcare setting. The overall argument is broad in considering the wider patient safety considerations, but the detailed rationale and supporting evidence presented relate to this specific hazard. Whilst there are no agreed regulatory approaches to introducing ML into healthcare, the work presented in this paper has shown a possible direction for overcoming this barrier and exploit the benefits of ML without compromising safety.

Coagulation phenotypes in sepsis and effects of recombinant human thrombomodulin: an analysis of three multicentre observational studies

Background

A recent randomised trial showed that recombinant thrombomodulin did not benefit patients who had sepsis with coagulopathy and organ dysfunction. Several recent studies suggested presence of clinical phenotypes in patients with sepsis and heterogenous treatment effects across different sepsis phenotypes. We examined the latent phenotypes of sepsis with coagulopathy and the associations between thrombomodulin treatment and the 28-day and in-hospital mortality for each phenotype.

Methods

This was a secondary analysis of multicentre registries containing data on patients (aged ≥ 16 years) who were admitted to intensive care units for severe sepsis or septic shock in Japan. Three multicentre registries were divided into derivation (two registries) and validation (one registry) cohorts. Phenotypes were derived using k-means with coagulation markers, platelet counts, prothrombin time/international normalised ratios, fibrinogen, fibrinogen/fibrin-degradation-products (FDP), D-dimer, and antithrombin activities. Associations between thrombomodulin treatment and survival outcomes (28-day and in-hospital mortality) were assessed in the derived clusters using a generalised estimating equation.

Results

Four sepsis phenotypes were derived from 3694 patients in the derivation cohort. Cluster dA (n = 323) had severe coagulopathy with high FDP and D-dimer levels, severe organ dysfunction, and high mortality. Cluster dB had severe disease with moderate coagulopathy. Clusters dC and dD had moderate and mild disease with and without coagulopathy, respectively. Thrombomodulin was associated with a lower 28-day (adjusted risk difference [RD]: − 17.8% [95% CI − 28.7 to − 6.9%]) and in-hospital (adjusted RD: − 17.7% [95% CI − 27.6 to − 7.8%]) mortality only in cluster dA. Sepsis phenotypes were similar in the validation cohort, and thrombomodulin treatment was also associated with lower 28-day (RD: − 24.9% [95% CI − 49.1 to − 0.7%]) and in-hospital mortality (RD: − 30.9% [95% CI − 55.3 to − 6.6%]).

Conclusions

We identified four coagulation marker-based sepsis phenotypes. The treatment effects of thrombomodulin varied across sepsis phenotypes. This finding will facilitate future trials of thrombomodulin, in which a sepsis phenotype with high FDP and D-dimer can be targeted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03541-5.

Continuous renal replacement therapy and extended indications

Extracorporeal blood purification (EBP) techniques provide support for critically ill patients with single or multiple organ dysfunction. Continuous renal replacement therapy (CRRT) is the modality of choice for kidney support for those patients and orchestrates the interactions between the different artificial organ support systems. Intensive care teams should be familiar with the concept of sequential extracorporeal therapy and plan on how to incorporate new treatment modalities into their daily practices. Importantly, scientific evidence should guide the decision-making process at the bedside and provide robust arguments to justify the costs of implementing new EBP treatments. In this narrative review, we explore the extended indications for CRRT as an adjunctive treatment to provide support for the heart, lung, liver, and immune system. We detail practicalities on how to run the treatments and how to tackle the most frequent complications regarding each of the therapies, whether applied alone or integrated. The physicochemical processes and technologies involved at the molecular level encompassing the interactions between the molecules, membranes, and resins are spotlighted. A clinical case will illustrate the timing for the initiation, maintenance, and discontinuation of EBP techniques.

Characterisation, identification, clustering, and classification of disease

The importance of quantifying the distribution and determinants of multimorbidity has prompted novel data-driven classifications of disease. Applications have included improved statistical power and refined prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases, with studies using molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”) to classify disease clusters. Here we consider whether easily measured risk factors such as height and BMI can effectively characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for analysis using clinical and epidemiological criteria, and conventional proportional hazards models were used to estimate associations with 12 established risk factors. Several diseases had strongly sex-dependent associations of disease risk with BMI. Importantly, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. These included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

Potential Targets to Mitigate Trauma- or Sepsis-Induced Immune Suppression

In sepsis and trauma, pathogens and injured tissue provoke a systemic inflammatory reaction which can lead to overwhelming inflammation. Concurrent with the innate hyperinflammatory response is adaptive immune suppression that can become chronic. A current key issue today is that patients who undergo intensive medical care after sepsis or trauma have a high mortality rate after being discharged. This high mortality is thought to be associated with persistent immunosuppression. Knowledge about the pathophysiology leading to this state remains fragmented. Immunosuppressive cytokines play an essential role in mediating and upholding immunosuppression in these patients. Specifically, the cytokines Interleukin-10 (IL-10), Transforming Growth Factor-β (TGF-β) and Thymic stromal lymphopoietin (TSLP) are reported to have potent immunosuppressive capacities. Here, we review their ability to suppress inflammation, their dynamics in sepsis and trauma and what drives the pathologic release of these cytokines. They do exert paradoxical effects under certain conditions, which makes it necessary to evaluate their functions in the context of dynamic changes post-sepsis and trauma. Several drugs modulating their functions are currently in clinical trials in the treatment of other pathologies. We provide an overview of the current literature on the effects of IL-10, TGF-β and TSLP in sepsis and trauma and suggest therapeutic approaches for their modulation.

Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

[Extracorporeal Strategies in Sepsis Treatment: Role of Therapeutic Plasma Exchange]

BACKGROUND

 Mortality in sepsis remains high. Various techniques for extracorporeal cytokine removal have been investigated as additional therapeutic measures in sepsis and septic shock.

OBJECTIVES

 To summarize a selection of extracorporeal blood purification techniques, with a special focus on therapeutic plasma exchange, and their current evidence in clinical use.

METHODS

 Non-systematic literature review.

RESULTS

 Various extracorporeal blood purification techniques with different levels of evidence regarding cytokine removal, vasopressor sparing effects and reduction of mortality are currently in clinical use. Most extensively studied modalities include high-volume hemofiltration/dialysis with and without high cut-off filters a well as hemoadsorption techniques (including CytoSorb, and polymyxin-B filters). Despite partly encouraging observations regarding removal of inflammatory cytokines and hemodynamic stabilization, results from randomized studies did not show an effect on survival. Due to use of donor plasma as substitution fluid, therapeutic plasma exchange represents the only modality able to additionally replace protective and consumed factors.

CONCLUSIONS

 The use of extracorporeal blood purification methods cannot be recommended for sepsis patients outside of clinical trials given the current lack of evidence of their efficacy. Future investigations should aim to homogenize the studied patient collective in respect to clinical sepsis severity, time point of intervention and different inflammatory (sub-)phenotypes.

Immune Deregulation in Sepsis and Septic Shock: Reversing Immune Paralysis by Targeting PD-1/PD-L1 Pathway

Sepsis remains a major problem for human health worldwide, thereby manifesting high rates of morbidity and mortality. Sepsis, once understood as a monophasic sustained hyperinflammation, is currently recognized as a dysregulated host response to infection, with both hyperinflammation and immunoparalysis occurring simultaneously from the earliest stages of sepsis, involving multiple organ dysfunctions. Despite the recent progress in the understanding of the pathophysiology underlying sepsis, no specific treatment to restore immune dysregulation in sepsis has been validated in clinical trials. In recent years, treatment for immune checkpoints such as the programmed cell death protein 1/programmed death ligand (PD-1/PD-L) pathway in tumor-infiltrating T-lymphocytes has been successful in the field of cancer immune therapy. As immune-paralysis in sepsis involves exhausted T-lymphocytes, future clinical applications of checkpoint inhibitors for sepsis are expected. In addition, the functions of PD-1/PD-L on innate lymphoid cells and the role of exosomal forms of PD-L1 warrant further research. Looking back on the history of repeatedly failed clinical trials of immune modulatory therapies for sepsis, sepsis must be recognized as a difficult disease entity for performing clinical trials. A major obstacle that could prevent effective clinical trials of drug candidates is the disease complexity and heterogeneities; clinically diagnosed sepsis could contain multiple sepsis subgroups that suffer different levels of hyper-inflammation and immune-suppression in distinct organs. Thus, the selection of appropriate more homogenous sepsis subgroup is the key for testing the clinical efficacy of experimental therapies targeting specific pathways in either hyperinflammation and/or immunoparalysis. An emerging technology such as artificial intelligence (AI) may help to identify an immune paralysis subgroup who would best be treated by PD-1/PD-L1 pathway inhibitors.

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning

OBJECTIVE

To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

MATERIALS AND METHODS

A multicenter, retrospective cohort study of 29 253 hospitalized adult sepsis patients between 2010 and 2013 in Northern California. We applied an unsupervised machine learning method, Latent Dirichlet Allocation, to the orders, medications, and procedures recorded in the electronic health record within the first 24 hours of each patient's hospitalization to uncover empiric treatment topics across the cohort and to develop computable clinical signatures for each patient based on proportions of these topics. We evaluated how these topics correlated with common sepsis treatment and outcome metrics including inpatient mortality, time to first antibiotic, and fluids given within 24 hours.

RESULTS

Mean age was 70 ± 17 years with hospital mortality of 9.6%. We empirically identified 42 clinically recognizable treatment topics (eg, pneumonia, cellulitis, wound care, shock). Only 43.1% of hospitalizations had a single dominant topic, and a small minority (7.3%) had a single topic comprising at least 80% of their overall clinical signature. Across the entire sepsis cohort, clinical signatures were highly variable.

DISCUSSION

Heterogeneity in sepsis is a major barrier to improving targeted treatments, yet existing approaches to characterizing clinical heterogeneity are narrowly defined. A machine learning approach captured substantial patient- and population-level heterogeneity in treatment during early sepsis hospitalization.

CONCLUSION

Using topic modeling based on treatment patterns may enable more precise clinical characterization in sepsis and better understanding of variability in sepsis presentation and outcomes.

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

BACKGROUND

The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes.

METHODS

Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves.

RESULTS

The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.

CONCLUSION

The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

Host Diagnostic Biomarkers of Infection in the ICU: Where Are We and Where Are We Going?

Purpose of Review

Early identification of infection in the critically ill patient and initiation of appropriate treatment is key to reducing morbidity and mortality. On the other hand, the indiscriminate use of antimicrobials leads to harms, many of which may be exaggerated in the critically ill population. The current method of diagnosing infection in the intensive care unit relies heavily on clinical gestalt; however, this approach is plagued by biases. Therefore, a reliable, independent biomarker holds promise in the accurate determination of infection. We discuss currently used host biomarkers used in the intensive care unit and review new and emerging approaches to biomarker discovery.

Recent Findings

White cell count (including total white cell count, left shift, and the neutrophil-leucocyte ratio), C-reactive protein, and procalcitonin are the most common host diagnostic biomarkers for sepsis used in current clinical practice. However, their utility in the initial diagnosis of infection, and their role in the subsequent decision to commence treatment, remains limited. Novel approaches to biomarker discovery that are currently being investigated include combination biomarkers, host 'sepsis signatures' based on differential gene expression, site-specific biomarkers, biomechanical assays, and incorporation of new and pre-existing host biomarkers into machine learning algorithms.

Summary

To date, no single reliable independent biomarker of infection exists. Whilst new approaches to biomarker discovery hold promise, their clinical utility may be limited if previous mistakes that have afflicted sepsis biomarker research continue to be repeated.

5th generation vs 4th generation troponin T in predicting major adverse cardiovascular events and all-cause mortality in patients hospitalized for non-cardiac indications: A cohort study

Objective

The frequency and implications of an elevated cardiac troponin (4^th or 5^th generation TnT) in patients outside of the emergency department or presenting with non-cardiac conditions is unclear.

Methods

Consecutive patients aged 18 years or older admitted for a primary non-cardiac condition who had the 4^th generation TnT drawn had the 5^th generation TnT run on the residual blood sample. Primary and secondary outcomes were all-cause mortality (ACM) and major adverse cardiovascular events (MACE) respectively at 1 year.

Results

918 patients were included (mean age 59.8 years, 55% male) in the cohort. 69% had elevated 5^th generation TnT while 46% had elevated 4^th generation TnT. 5^th generation TnT was more sensitive and less specific than 4^th generation TnT in predicting both ACM and MACE. The sensitivities for the 5^th generation TnT assay were 85% for ACM and 90% for MACE rates, compared to 65% and 70% respectively for the 4^th generation assay. 5^th generation TnT positive patients that were missed by 4^th generation TnT had a higher risk of ACM (27.5%) than patients with both assays negative (27.5% vs 11.1%, p<0.001), but lower than patients who had both assay positive (42.1%). MACE rates were not better stratified using the 5^th generation TnT assay.

Conclusions

In patients admitted for a non-cardiac condition, 5^th generation TnT is more sensitive although less specific in predicting MACE and ACM. 5^th generation TnT identifies an intermediate risk group for ACM previously missed with the 4^th generation assay.

Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research

Big data analytics research using heterogeneous electronic health record (EHR) data requires accurate identification of disease phenotype cases and controls. Overreliance on ground truth determination based on administrative data can lead to biased and inaccurate findings. Hospital-acquired venous thromboembolism (HA-VTE) is challenging to identify due to its temporal evolution and variable EHR documentation. To establish ground truth for machine learning modeling, we compared accuracy of HA-VTE diagnoses made by administrative coding to manual review of gold standard diagnostic test results. We performed retrospective analysis of EHR data on 3680 adult stepdown unit patients identifying HA-VTE. International Classification of Diseases, Ninth Revision (ICD-9-CM) codes for VTE were identified. 4544 radiology reports associated with VTE diagnostic tests were screened using terminology extraction and then manually reviewed by a clinical expert to confirm diagnosis. Of 415 cases with ICD-9-CM codes for VTE, 219 were identified with acute onset type codes. Test report review identified 158 new-onset HA-VTE cases. Only 40% of ICD-9-CM coded cases (n = 87) were confirmed by a positive diagnostic test report, leaving the majority of administratively coded cases unsubstantiated by confirmatory diagnostic test. Additionally, 45% of diagnostic test confirmed HA-VTE cases lacked corresponding ICD codes. ICD-9-CM coding missed diagnostic test-confirmed HA-VTE cases and inaccurately assigned cases without confirmed VTE, suggesting dependence on administrative coding leads to inaccurate HA-VTE phenotyping. Alternative methods to develop more sensitive and specific VTE phenotype solutions portable across EHR vendor data are needed to support case-finding in big-data analytics.

Machine learning in liver transplantation: a tool for some unsolved questions?

Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.

Feasibility of Embedding a Scalable, Virtually Enabled Biorepository in the Electronic Health Record for Precision Medicine

Importance

A cornerstone of precision medicine is the identification and use of biomarkers that help subtype patients for targeted treatment. Such an approach requires the development and subsequent interrogation of large-scale biobanks linked to well-annotated clinical data. Traditional means of creating these data-linked biobanks are costly and lengthy, especially in acute conditions that require time-sensitive clinical data and biospecimens.

Objectives

To develop a virtually enabled biorepository and electronic health record (EHR)-embedded, scalable cohort for precision medicine (VESPRE) and compare the feasibility, enrollment, and costs of VESPRE with those of a traditional study design in acute care.

Design, Setting, and Participants

In a prospective cohort study, the EHR-embedded screening alert was generated for 3428 patients, and 2199 patients (64%) were eligible and screened. Of these, 1027 patients (30%) were enrolled. VESPRE was developed for regulatory compliance, feasibility, internal validity, and cost in a prospective cohort of 1027 patients (aged ≥18 years) with sepsis-3 within 6 hours of presentation to the emergency department. The VESPRE infrastructure included (1) automated EHR screening, (2) remnant blood collection for creation of a virtually enabled biorepository, and (3) automated clinical data abstraction. The study was conducted at an academic institution in southwestern Pennsylvania from October 17, 2017, to June 6, 2019.

Main Outcomes and Measures

Regulatory compliance, enrollment, internal validity of automated screening, biorepository acquisition, and costs.

Results

Of the 1027 patients enrolled in the study, 549 were included in the proof-of-concept analysis (305 [56%] men); median (SD) age was 59 (17) years. VESPRE collected 12 963 remnant blood and urine samples and demonstrated adequate feasibility for clinical, biomarker, and microbiome analyses. Over the 20-month test, the total cost beyond the existing operations infrastructure was $39 417.50 ($14 880.00 project management, $22 717.50 laboratory supplies/staff, and $1820.00 data management)-approximately $39 per enrolled patient vs $239 per patient for a traditional cohort study.

Conclusions and Relevance

Results of this study suggest that, in a large US health system that collects data using a common EHR platform and centralized laboratory system, VESPRE, a large-scale, inexpensive EHR-embedded infrastructure for precision medicine can be used. Tested in the sepsis setting, VESPRE appeared to capture a high proportion of eligible patients at low incremental cost.

Integrating infection and sepsis management through holistic early warning systems and heuristic approaches: a concept proposal

This is a first attempt to integrate the three pillars of infection management: the infection prevention and control (IPC), and surveillance (IPCS), antimicrobial stewardship (AMS), and rapid identification and management of sepsis (RIMS). The new 'Sepsis-3' definition extrapolates the diagnosis of sepsis from our previously slightly naïve concept of a stepwise evolving pattern. In doing so, however, we have placed the transition from infection toward sepsis in the domain of uncertainty and time-dependency. This now demands that clinical judgment be used in the risk stratification of patients with infection, and that pragmatic local solutions be used to prompt clinicians to evaluate formally for sepsis. We feel it is necessary to stimulate the development of a new generation of concepts and models aiming at embracing uncertainty. We see the opportunity for a heuristic approach focusing on the relevant clinical predictors at hand allowing to navigate the uncertainty of infection diagnosis under time constraints. The diverse and situated clinical approaches eventually emerging need to focus on the understanding of infection as the unbalanced interactions of host, pathogen, and environment. In order extend such approach throughout the patient journey we propose a holistic early warning system underpinned by the risk-based categories of hazards and vulnerabilities iteratively fostered by the information gathered by the infection prevention control and surveillance, clinical microbiology, and clinical chemistry services.

Respiratory Tract Dysbiosis Is Associated with Worse Outcomes in Mechanically Ventilated Patients

Rationale: Host inflammatory responses have been strongly associated with adverse outcomes in critically ill patients, but the biologic underpinnings of such heterogeneous responses have not been defined.Objectives: We examined whether respiratory tract microbiome profiles are associated with host inflammation and clinical outcomes of acute respiratory failure.Methods: We collected oral swabs, endotracheal aspirates (ETAs), and plasma samples from mechanically ventilated patients. We performed 16S ribosomal RNA gene sequencing to characterize upper and lower respiratory tract microbiota and classified patients into host-response subphenotypes on the basis of clinical variables and plasma biomarkers of innate immunity and inflammation. We derived diversity metrics and composition clusters with Dirichlet multinomial models and examined our data for associations with subphenotypes and clinical outcomes.Measurements and Main Results: Oral and ETA microbial communities from 301 mechanically ventilated subjects had substantial heterogeneity in α and β diversity. Dirichlet multinomial models revealed a cluster with low α diversity and enrichment for pathogens (e.g., high Staphylococcus or Pseudomonadaceae relative abundance) in 35% of ETA samples, associated with a hyperinflammatory subphenotype, worse 30-day survival, and longer time to liberation from mechanical ventilation (adjusted P < 0.05), compared with patients with higher α diversity and relative abundance of typical oral microbiota. Patients with evidence of dysbiosis (low α diversity and low relative abundance of "protective" oral-origin commensal bacteria) in both oral and ETA samples (17%, combined dysbiosis) had significantly worse 30-day survival and longer time to liberation from mechanical ventilation than patients without dysbiosis (55%; adjusted P < 0.05).Conclusions: Respiratory tract dysbiosis may represent an important, modifiable contributor to patient-level heterogeneity in systemic inflammatory responses and clinical outcomes.

Three Data-Driven Phenotypes of Multiple Organ Dysfunction Syndrome Preserved from Early Childhood to Middle Adulthood

Multiple organ dysfunction syndrome (MODS) is one of the major causes of death and long-term impairment in critically ill patients. MODS is a complex, heterogeneous syndrome consisting of different phenotypes, which has limited the development of MODS-specific therapies and prognostic models. We used an unsupervised learning approach to derive novel phenotypes of MODS based on the type and severity of six individual organ dysfunctions. In a large, multi-center cohort of pediatric, young and middle-aged adults admitted to three different intensive care units, we uncovered and characterized three distinct data-driven phenotypes of MODS which were reproducible across age groups, where independently associated with outcomes and had unique predictors of in-hospital mortality.

A road map from single-cell transcriptome to patient classification for the immune response to trauma

Immune dysfunction is an important factor driving mortality and adverse outcomes after trauma but remains poorly understood, especially at the cellular level. To deconvolute the trauma-induced immune response, we applied single-cell RNA sequencing to circulating and bone marrow mononuclear cells in injured mice and circulating mononuclear cells in trauma patients. In mice, the greatest changes in gene expression were seen in monocytes across both compartments. After systemic injury, the gene expression pattern of monocytes markedly deviated from steady state with corresponding changes in critical transcription factors, which can be traced back to myeloid progenitors. These changes were largely recapitulated in the human single-cell analysis. We generalized the major changes in human CD14⁺ monocytes into 6 signatures, which further defined 2 trauma patient subtypes (SG1 vs. SG2) identified in the whole-blood leukocyte transcriptome in the initial 12 hours after injury. Compared with SG2, SG1 patients exhibited delayed recovery, more severe organ dysfunction, and a higher incidence of infection and noninfectious complications. The 2 patient subtypes were also recapitulated in burn and sepsis patients, revealing a shared pattern of immune response across critical illness. Our data will be broadly useful to further explore the immune response to inflammatory diseases and critical illness.

Computational Derivation of Core, Dynamic Human Blunt Trauma Inflammatory Endotypes

Systemic inflammation ensues following traumatic injury, driving immune dysregulation and multiple organ dysfunction (MOD). While a balanced immune/inflammatory response is ideal for promoting tissue regeneration, most trauma patients exhibit variable and either overly exuberant or overly damped responses that likely drive adverse clinical outcomes. We hypothesized that these inflammatory phenotypes occur in the context of severe injury, and therefore sought to define clinically distinct endotypes of trauma patients based on their systemic inflammatory responses. Using Patient-Specific Principal Component Analysis followed by unsupervised hierarchical clustering of circulating inflammatory mediators obtained in the first 24 h after injury, we segregated a cohort of 227 blunt trauma survivors into three core endotypes exhibiting significant differences in requirement for mechanical ventilation, duration of ventilation, and MOD over 7 days. Nine non-survivors co-segregated with survivors. Dynamic network inference, Fisher Score analysis, and correlations of IL-17A with GM-CSF, IL-10, and IL-22 in the three survivor sub-groups suggested a role for type 3 immunity, in part regulated by Th17 and γδ 17 cells, and related tissue-protective cytokines as a key feature of systemic inflammation following injury. These endotypes may represent archetypal adaptive, over-exuberant, and overly damped inflammatory responses.

Early prolonged neutrophil activation in critically ill patients with sepsis

We hypothesised that plasma concentrations of biomarkers of neutrophil activation and pro-inflammatory cytokines differ according to the phase of rapidly evolving sepsis. In an observational study, we measured heparin-binding protein (HBP), myeloperoxidase (MPO), IL-6 and IL-8 in 167 sepsis patients on intensive care unit admission. We prospectively used the emergence of the first sepsis-associated organ dysfunction (OD) as a surrogate for the sepsis phase. Fifty-five patients (of 167, 33%) developed the first OD > 1 h before, 74 (44%) within ± 1 h, and 38 (23%) > 1 h after intensive care unit admission. HBP and MPO were elevated at a median of 12 h before the first OD, remained high up to 24 h, and were not associated with sepsis phase. IL-6 and IL-8 rose and declined rapidly close to OD emergence. Elevation of neutrophil activation markers HBP and MPO was an early event in the evolution of sepsis, lasting beyond the subsidence of the pro-inflammatory cytokine reaction. Thus, as sepsis biomarkers, HBP and MPO were not as prone as IL-6 and IL-8 to the effect of sample timing.

Good clinical practice for the use of vasopressor and inotropic drugs in critically ill patients: state-of-the-art and expert consensus

Vasopressors and inotropic agents are widely used in critical care. However, strong evidence supporting their use in critically ill patients is lacking in many clinical scenarios. Thus, the Italian Society of Anesthesia and Intensive Care (SIAARTI) promoted a project aimed to provide indications for good clinical practice on the use of vasopressors and inotropes, and on the management of critically ill patients with shock. A panel of 16 experts in the field of intensive care medicine and hemodynamics has been established. Systematic review of the available literature was performed based on PICO questions. Basing on available evidence, the panel prepared a summary of evidence and then wrote the clinical questions. A modified semi-quantitative RAND/UCLA appropriateness method has been used to determine the appropriateness of specific clinical scenarios. The panel identified 29 clinical questions for the use of vasopressors and inotropes in patients with septic shock and cardiogenic shock. High level of agreement exists among the panel members about appropriateness of inotropes/vasopressors' use in patients with septic shock and cardiogenic shock.

A flow cytometric assay for HLA-DR expression on monocytes validated as a biomarker for enrollment in sepsis clinical trials

PURPOSE

Decreased expression of HLA-DR on monocytes (mHLA-DR) is a reliable indicator of immunosuppression in patients with sepsis and is correlated with increased risk of secondary infection and mortality. A flow cytometry-based laboratory developed test for the measurement of mHLA-DR in whole blood was validated for clinical trial enrollment, which is considered medical decision-making, for patients with severe sepsis or septic shock.

METHODS

The BD Quantibrite™ anti-HLA-DR/anti-monocyte reagent measures antibodies bound per cell of HLA-DR on CD14+ monocytes. The mHLA-DR assay was planned to support inclusion/exclusion of patients for a clinical trial and was validated according to New York State Department of Health (NYSDOH) requirements for a new non-malignant leukocyte immunophenotyping assay.

RESULTS

Normal, healthy donor and sepsis patient samples were stable up to 72 h post-collection in Cyto-Chex BCT phlebotomy tubes. Pre-determined acceptance criteria were met for precision parameters (average %CV ≤ 20%) and global laboratory-to-laboratory comparisons (average %Δ ≤ 20%). The approaches taken to evaluate and report accuracy, analytical specificity and sensitivity, reportable range, reference interval, and the proposed multi-level quality control were accepted by NYSDOH.

CONCLUSIONS

In this study, the validation strategy necessary when the intended use of assay results changes from exploratory to medical decision making (patient enrollment), which successfully resulted in regulatory approval, is described.

Adjunctive treatments for the management of septic shock - a narrative review of the current evidence

Septic shock is a leading cause of death and morbidity worldwide. The cornerstones of management include prompt identification of sepsis, early initiation of antibiotic therapy, adequate fluid resuscitation and organ support. Over the past two decades, there have been considerable improvements in our understanding of the pathophysiology of sepsis and the host response, including regulation of inflammation, endothelial disruption and impaired immunity. This has offered opportunities for innovative adjunctive treatments such as vitamin C, corticosteroids and beta-blockers. Some of these approaches have shown promising results in early phase trials in humans, while others, such as corticosteroids, have been tested in large, international, multicentre randomised controlled trials. Contemporary guidelines make a weak recommendation for the use of corticosteroids to reduce mortality in sepsis and septic shock. Vitamin C, despite showing initial promise in observational studies, has so far not been shown to be clinically effective in randomised trials. Beta-blocker therapy may have beneficial cardiac and non-cardiac effects in septic shock, but there is currently insufficient evidence to recommend their use for this condition. The results of ongoing randomised trials are awaited. Crucial to reducing heterogeneity in the trials of new sepsis treatments will be the concept of enrichment, which refers to the purposive selection of patients with clinical and biological characteristics that are likely to be responsive to the intervention being tested.

Coronavirus disease-2019 and its current scenario – A review

Coronaviruses are enveloped non-segmented positive-sense RNA viruses belonging to the family Coronaviridae. The human coronavirus infections are mild; the epidemics of the two β-coronaviruses, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) have caused more than ten thousand cumulative cases in the past twodecades. There is a new public health crisis threatening the world with the emergence and spread of 2019 novel coronavirus (2019-nCoV). The virus originated in bats and was transmitted to humans through yet unknown intermediary animals in Wuhan, Hubei province in China during the month of December 2019. Till date around 7,823,289 reported cases of coronavirus disease 2019 (COVID-2019) and 431,541 reported deaths till date. The disease is transmitted by inhalation or contact with infected droplets with incubation period of 2–14 days. The symptoms are usually fever, sore throat, dry cough, breathlessness, fatigue while many people are asymptomatic. Coronavirus (2019-nCoV) may progress to pneumonia, acute respiratory distress syndrome (ARDS) and can cause multi-organ dysfunction. Currently diagnosis is done by demonstration of the virus in respiratory secretions by special molecular tests like real-time reverse-transcription–polymerase-chain-reaction (RT-PCR), Radiological examinations (chest CT). Common laboratory tests like white cell counts and C-reactive protein (CRP) and measure symptoms can be used as preliminary screening at large scale after lock down the area or country. Treatment is essentially supportive; role of antiviral agents is yet to be established. It is paramount to implement infection control practices by infection source controlling, transmission route blocking, and susceptible population protection. Early preventive measures can be home isolation of suspected cases and those with mild illnesses and strict infection control measures at hospitals that include contact and droplet precautions. The worldwide impact of this Coronavirus new epidemic is yet uncertain.

Artificial intelligence enabled applications in kidney disease

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Evaluation of Biomarkers in Critical Care and Perioperative Medicine: A Clinician’s Overview of Traditional Statistical Methods and Machine Learning Algorithms

Interest in developing and using novel biomarkers in critical care and perioperative medicine is increasing. Biomarkers studies are often presented with flaws in the statistical analysis that preclude them from providing a scientifically valid and clinically relevant message for clinicians. To improve scientific rigor, the proper application and reporting of traditional and emerging statistical methods (e.g., machine learning) of biomarker studies is required. This Readers' Toolbox article aims to be a starting point to nonexpert readers and investigators to understand traditional and emerging research methods to assess biomarkers in critical care and perioperative medicine.

COVID-19 Clinical Phenotypes: Presentation and Temporal Progression of Disease in a Cohort of Hospitalized Adults in Georgia, United States

BACKGROUND

The epidemiological features and outcomes of hospitalized adults with coronavirus disease 2019 (COVID-19) have been described; however, the temporal progression and medical complications of disease among hospitalized patients require further study. Detailed descriptions of the natural history of COVID-19 among hospitalized patients are paramount to optimize health care resource utilization, and the detection of different clinical phenotypes may allow tailored clinical management strategies.

METHODS

This was a retrospective cohort study of 305 adult patients hospitalized with COVID-19 in 8 academic and community hospitals. Patient characteristics included demographics, comorbidities, medication use, medical complications, intensive care utilization, and longitudinal vital sign and laboratory test values. We examined laboratory and vital sign trends by mortality status and length of stay. To identify clinical phenotypes, we calculated Gower's dissimilarity matrix between each patient's clinical characteristics and clustered similar patients using the partitioning around medoids algorithm.

RESULTS

One phenotype of 6 identified was characterized by high mortality (49%), older age, male sex, elevated inflammatory markers, high prevalence of cardiovascular disease, and shock. Patients with this severe phenotype had significantly elevated peak C-reactive protein creatinine, D-dimer, and white blood cell count and lower minimum lymphocyte count compared with other phenotypes (P < .01, all comparisons).

CONCLUSIONS

Among a cohort of hospitalized adults, we identified a severe phenotype of COVID-19 based on the characteristics of its clinical course and poor prognosis. These findings need to be validated in other cohorts, as improved understanding of clinical phenotypes and risk factors for their development could help inform prognosis and tailored clinical management for COVID-19.

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

Criticality: A New Concept of Severity of Illness for Hospitalized Children

OBJECTIVES

To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care.

DESIGN

Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database.

SETTING

Hospitals with pediatric routine inpatient and ICU care.

PATIENTS

Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations.

CONCLUSIONS

The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

The role of platelets in sepsis

A State of the Art lecture titled "The role of platelets in sepsis" was presented at the ISTH congress in 2020. Sepsis is a life-threatening organ dysfunction caused by a dysregulated and multifaceted host response to infection. Platelets play a significant role in the coordinated immune response to infection and therefore in the inflammation and coagulation dysfunction that contributes to organ damage in sepsis. Thrombocytopenia has a high incidence in sepsis, and it is a marker of poor prognosis. The genesis of thrombocytopenia is likely multifactorial, and unraveling the involved molecular mechanisms will allow development of biomarkers of platelet function in sepsis. Such platelet biomarkers can facilitate study of antiplatelet interventions as immunomodulatory treatment in sepsis. Finally, relevant new data on this topic presented during the 2020 ISTH virtual congress are reviewed.

Leukocyte glucocorticoid receptor expression and related transcriptomic gene signatures during early sepsis

PURPOSE

The study aimed to understand the molecular mechanisms that might lead to differences in the glucocorticoid response during sepsis.

METHODS

Patients diagnosed with sepsis (n = 198) and 40 healthy controls were enrolled. Glucocorticoid receptor (GR) expression in circulating leukocytes and plasma levels of adrenocorticotropic hormone and cortisol on days 1 and 7 were measured in all participants. Expression profiling of 16 genes associated with GR expression in peripheral blood mononuclear cells (PBMCs) in 12 healthy controls and 26 patients with sepsis was performed by PCR.

RESULTS

Cortisol levels were higher in patients with sepsis than in healthy controls on day 1 after admission and recovered to normal levels by day 7. GR expression was gradually downregulated in leukocyte subsets. Non-survivors showed lower GR and higher cortisol levels than survivors. GRα expression was lower in patients with sepsis than in controls, whereas GRβ showed the opposite trend. MicroRNAs related to GR resistance and suppression were altered in PBMCs during sepsis.

CONCLUSION

Patients with sepsis showed upregulated plasma cortisol levels along with downregulated GR expression on various leukocyte subtypes, portending poor cortisol response and outcome. Changes in GR-regulatory miRNAs may be responsible for GR low expression.

A neutrophil subset defined by intracellular olfactomedin 4 is associated with mortality in sepsis

Sepsis is a heterogeneous syndrome clinically and biologically, but biomarkers of distinct host response pathways for early prognostic information and testing targeted treatments are lacking. Olfactomedin 4 (OLFM4), a matrix glycoprotein of neutrophil-specific granules, defines a distinct neutrophil subset that may be an independent risk factor for poor outcomes in sepsis. We hypothesized that increased percentage of OLFM4⁺ neutrophils on sepsis presentation would be associated with mortality. In a single-center, prospective cohort study, we enrolled adults admitted to an academic medical center from the emergency department (ED) with suspected sepsis [identified by 2 or greater systemic inflammatory response syndrome (SIRS) criteria and antibiotic receipt] from March 2016 through December 2017, followed by sepsis adjudication according to Sepsis-3. We collected 200 µL of whole blood within 24 h of admission and stained for the neutrophil surface marker CD66b followed by intracellular staining for OLFM4 quantitated by flow cytometry. The predictors for 60-day mortality were 1) percentage of OLFM4⁺ neutrophils and 2) OLFM4⁺ neutrophils at a cut point of ≥37.6% determined by the Youden Index. Of 120 enrolled patients with suspected sepsis, 97 had sepsis and 23 had nonsepsis SIRS. The mean percentage of OLFM4⁺ neutrophils was significantly increased in both sepsis and nonsepsis SIRS patients who died (P ≤ 0.01). Among sepsis patients with elevated OLFM4⁺ (≥37.6%), 56% died, compared with 18% with OLFM4⁺ <37.6% (P = 0.001). The association between OLFM4⁺ and mortality withstood adjustment for age, sex, absolute neutrophil count, comorbidities, and standard measures of severity of illness (SOFA score, APACHE III) (P < 0.03). In summary, OLFM4⁺ neutrophil percentage is independently associated with 60-day mortality in sepsis and may represent a novel measure of the heterogeneity of host response to sepsis.