Identifying Novel Sepsis Subphenotypes Using Temperature Trajectories

Impact of thermoneutral acclimation on a murine model of polymicrobial peritonitis

To examine the effects of ambient temperature on the reproducibility and translation of a murine sepsis model, we hypothesized that acclimation of mice in temperatures within their thermoneutral zone would alter immune responses and outcomes compared to standard housing temperatures. Mice housed for one week in thermoneutral (30°C) as compared to standard (22°C) conditions displayed lower counts of circulating neutrophils (0.52 ± 0.20 vs. 1.10 ± 0.54 x103/μL; p = 0.011) and peritoneal macrophages (0.80 ± 0.57 vs. 1.62 ± 0.62 x 105/μL; p = 0.002) as well as reduced in vitro production of IFN-γ by stimulated splenocytes (0.38 ± 0.68 vs 2.55 ± 0.76 x104 pg/mL, respectively, p = 0.004). After one week of temperature acclimation followed by CLP, the 7-day mortality was significantly lower under thermoneutral as compared to standard temperatures (80% vs 30%, respectively; p = 0.012), although core body temperature was preserved (average for 24 hours: 36.4 ± 1.3°C vs 31.7 ± 4.7°C; p < 0.0001). The lower survival was accompanied by increased systemic IL-6 levels (3.8 ± 3.3 vs 1.9 ± 1.3 x103 pg/mL; p = 0.04) and less robust influx of neutrophils into the peritoneum (1.68 ± 1.07 vs. 4.20 ± 2.46 x105/μL, respectively; p = 0.0003). Overall, thermoneutral temperatures impacted innate immune parameters before and after CLP, producing distinctly different outcomes. Therefore, ambient temperature is an important variable that could impact model reproducibility and should be reported for the acclimation period and experimentation phases of murine sepsis studies.

Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study

BACKGROUND

Sepsis is a life-threatening condition frequently observed in patients with intracerebral hemorrhage (ICH) who are critically ill. Early and accurate identification and prediction of sepsis are crucial. Machine learning (ML)-based predictive models exhibit promising sepsis prediction capabilities in emergency settings. However, their application in predicting sepsis among patients with ICH is still limited.

OBJECTIVE

The aim of the study is to develop an ML-driven risk calculator for early prediction of sepsis in patients with ICH who are critically ill and to clarify feature importance and explain the model using the Shapley Additive Explanations method.

METHODS

Patients with ICH admitted to the intensive care unit (ICU) from the Medical Information Mart for Intensive Care IV database between 2008 and 2022 were divided into training and internal test sets. The external test was performed using the eICU Collaborative Research Database, which includes over 200,000 ICU admissions across the United States between 2014 and 2015. Sepsis following ICU admission was identified using Sepsis-3.0 through clinical diagnosis combining elevation of the Sequential Organ Failure Assessment by ≥2 points with suspected infection. The Boruta algorithm was used for feature selection, confirming 29 features. Nine ML algorithms were used to construct the prediction models. Predictive performance was compared using several evaluation metrics, including the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanations technique was used to interpret the final model, and a web-based risk calculator was constructed for clinical practice.

RESULTS

Overall, 2414 patients with ICH were enrolled from the Medical Information Mart for Intensive Care IV database, with 1689 and 725 patients assigned to the training and internal test sets, respectively. An external test set of 2806 patients with ICH from the eICU database was used. Among the 9 ML models tested, the categorical boosting (CatBoost) model demonstrated the best discriminative ability. After reducing features based on their importance, an explainable final CatBoost model was developed using 8 features. The final model accurately predicted sepsis in internal (AUC=0.812) and external (AUC=0.771) tests.

CONCLUSIONS

We constructed a web-based risk calculator with 8 features based on the CatBoost model to assist clinicians in identifying people at high risk for sepsis in patients with ICH who are critically ill.

Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China

Sepsis is a clinically heterogeneous disease with high mortality. It is crucial to develop relevant therapeutic strategies for different sepsis phenotypes, but the impact of phenotypes on patients' clinical outcomes is unclear. This study aimed to identify potential sepsis phenotypes using readily available clinical parameters and assess their predictive value for 28-day clinical outcomes by logistic regression analysis. In this retrospective analysis, researchers extracted clinical data from adult patients admitted to the First Affiliated Hospital of Anhui Medical University between April and August 2022 and from the 2014-2015 eICU Collaborative Study database. K-Means clustering was utilized to identify and refine sepsis phenotypes, and their predictive performance was subsequently evaluated. Logistic regression models were trained independently for each phenotype and five-fold cross-validation was used to predict clinical outcomes. Predictive accuracy was then compared to traditional non-clustered prediction methods using model assessment scores. The study cohort consisted of 250 patients from the First Affiliated Hospital of Anhui Medical University, allocated in a 7:3 ratio for training and testing, respectively, and an external validation cohort of 3100 patients from the eICU Cooperative Research Database. The results of the phenotype-based prediction model demonstrated an improvement in F1 score from 0.74 to 0.82 and AUC from 0.74(95%CI 0.71-0.80) to 0.84(95%CI 0.82-0.87), and these results also highlight the superiority of clinical outcome prediction with the help of sepsis phenotypes over traditional prediction methods. Phenotype-based prediction of 28-day clinical outcomes in sepsis demonstrated significant advantages over traditional models, highlighting the impact of phenotype-driven modeling on clinical outcomes in sepsis.

INFORMING INTENSIVE CARE UNIT DIGITAL TWINS: DYNAMIC ASSESSMENT OF CARDIORESPIRATORY FAILURE TRAJECTORIES IN PATIENTS WITH SEPSIS

ABSTRACT

Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55-76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56-87), hospital length of stay (LOS) of 7 (IQR, 4-12) days, and ICU LOS of 2 (IQR, 1-4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2-15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6-13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0-1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3-8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

Temporal Transitions of the Hyperinflammatory and Hypoinflammatory Phenotypes in Critical Illness

Rationale: Systemic molecular phenotypes of critical illness are prognostically informative, yet their temporal kinetics and implications of changing phenotypes remain incompletely understood. Objectives: To determine the temporal nature of the Hyperinflammatory and Hypoinflammatory phenotypes and assess the impact of transition between the phenotypes on mortality. Methods: We used data from one prospective observational cohort (MARS [Molecular Diagnosis and Risk Stratification of Sepsis]) and two randomized controlled trials in acute respiratory distress syndrome (ALVEOLI [Assessment of Low Tidal Volume and Elevated End-Expiratory Pressure to Obviate Lung Injury]) and sepsis (CLOVERS [Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis]). Critically ill patients with biomarkers available at multiple time points (Days 0-4) were included. We used a validated classifier model incorporating plasma IL-8, protein C, and serum bicarbonate to assign phenotypes on each day. We determined the association of longitudinal phenotype transition and 90-day all-cause mortality. Measurements and Main Results: Data from 2,407, 527, and 868 patients were included in MARS, ALVEOLI, and CLOVERS, respectively. In MARS, 36.0% were classified by the parsimonious model as Hyperinflammatory at Day 0, decreasing to 15.6% by Day 2 and 6.3% by Day 4. In ALVEOLI and CLOVERS, 26.4% and 24.8% of patients were Hyperinflammatory at Day 0, decreasing to 13.4% and 5.7% at Day 3, respectively. In all three cohorts, switching classification from Hyperinflammatory (Day 0) to Hypoinflammatory over time was associated with significantly improved mortality compared with persistently Hyperinflammatory patients. Mediation analysis indicated that only a minor proportion of this improvement could be attributed to ameliorating organ failure. Conclusions: The prevalence of the Hyperinflammatory phenotype, as assigned using a parsimonious biomarker classifier model, decreases over the first several days of critical illness, irrespective of acute respiratory distress syndrome diagnosis. The transition from Hyperinflammatory to Hypoinflammatory mediates a trajectory toward recovery beyond the resolution of organ failure.

A common longitudinal intensive care unit data format (CLIF) for critical illness research

RATIONALE

Critical illness threatens millions of lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness.

OBJECTIVES

Overcome the data management, security, and standardization barriers to large-scale critical illness EHR studies.

METHODS

We developed a Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format to harmonize EHR data necessary to study critical illness. We conducted proof-of-concept studies with a federated research architecture: (1) an external validation of an in-hospital mortality prediction model for critically ill patients and (2) an assessment of 72-h temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models.

MEASUREMENTS AND MAIN RESULTS

We converted longitudinal data from 111,440 critically ill patient admissions from 2020 to 2021 (mean age 60.7 years [standard deviation 17.1], 28% Black, 7% Hispanic, 44% female) across 9 health systems and 39 hospitals into CLIF databases. The in-hospital mortality prediction model had varying performance across CLIF consortium sites (AUCs: 0.73-0.81, Brier scores: 0.06-0.10) with degradation in performance relative to the derivation site. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.

CONCLUSIONS

CLIF enables transparent, efficient, and reproducible critical care research across diverse health systems. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational artificial intelligence (AI) models of critical illness, and real-time critical care quality dashboards.

Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies

Sepsis, a heterogeneous illness produced by a dysregulated host response to infection, remains a severe mortality risk. Recent discoveries in sepsis research have stressed phenotyping as a feasible strategy for tackling heterogeneity and enhancing therapy precision. Sepsis phenotyping has moved from traditional stratifications based on severity and prognosis to dynamic, phenotype-driven therapeutic options. This review covers recent progress in connecting sepsis subgroups to personalized treatments, with a focus on phenotype-based therapeutic predictions and decision-support systems. Despite ongoing challenges, such as standardizing phenotyping frameworks and incorporating findings into clinical practice, this topic has enormous promise. By investigating phenotypic variation in therapy responses, we hope to uncover new biomarkers and phenotype-driven therapeutic solutions, laying the groundwork for more effective therapies and, ultimately improving patient outcomes.

Enterobactin inhibits microbiota-dependent activation of AhR to promote bacterial sepsis in mice

Sepsis is a major cause of morbidity and mortality, but our understanding of the mechanisms underlying survival or susceptibility is limited. Here, as pathogens often subvert host defence mechanisms, we hypothesized that this might influence the outcome of sepsis. We used microbiota analysis, faecal microbiota transplantation, antibiotic treatment and caecal metabolite analysis to show that gut-microbiota-derived tryptophan metabolites including indoles increased host survival in a mouse model of Serratia marcescens sepsis. Infection in macrophage-specific aryl hydrocarbon receptor (AhR) knockout mice revealed that AhR activation induced transcriptional reprogramming in macrophages and increased bacterial clearance and host survival. However, culture supernatants from multiple bacterial pathogens inhibited AhR activation in vitro. We showed that the secreted siderophore, enterobactin, inhibited AhR activation in vitro and increased sepsis mortality in vivo. By contrast, oral or systemic tryptophan supplementation increased survival. These findings show that sepsis survival depends upon the interplay between pathogen inhibition and the activation of AhR by a microbiota-derived metabolite.

Aiming for precision: personalized medicine through sepsis subtyping

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

Association between body temperature and all-cause mortality in patients with sepsis: analysis of the MIMIC-IV database

BACKGROUND

Abnormal body temperature (fever or hypothermia) is a critical symptom in sepsis and is strongly associated with clinical prognosis and disease progression. Given the duality and variability of body temperature fluctuations throughout the disease course, further research is essential to refine clinical strategies for temperature management in sepsis patients.

METHODS

We extracted clinical data of sepsis patients from the MIMIC-IV database. A restricted cubic spline (RCS) curve was employed to describe the non-linear relationship between body temperature and clinical outcomes. Based on peak temperature within the first 24 h after admission, patients were categorized into three groups: < 36 °C, 36-38 °C, and > 38 °C. We subsequently matched patients one-to-one into three cohorts using a pairwise propensity score matching (PSM) approach. Alongside clinical data, we conducted log-rank and McNemar tests, and established multiple models, including multiple Cox regression, overlap-weighted (OW) adjusted Cox regression, multiple logistic regression, and OW-adjusted multiple logistic regression, to investigate the impact of temperature on clinical outcomes.

RESULTS

A total of 35,499 sepsis patients were included in my study: 311 with a temperature below 36 °C, 27,538 with a temperature between 36 and 38 °C, and 7650 with a temperature above 38 °C. The RCS analysis revealed a non-linear, U-shaped relationship between body temperature and 28-day, ICU, and in-hospital mortality. Patients with hypothermia had significantly higher 28-day mortality (54.34% vs. 19.28%), ICU mortality (44.37% vs. 12.89%), and in-hospital mortality (49.20% vs. 17.46%) compared to those with hyperthermia. Among patients younger than 65 years, hyperthermia was a protective factor against 28-day mortality relative to normal body temperature, while the opposite was observed in patients aged 65 and older. This trend was consistent in the analysis of ICU and in-hospital mortality.

CONCLUSIONS

Among sepsis patients admitted to the ICU, a peak temperature below 36 °C within the first 24 h of admission was associated with higher 28-day mortality. However, no significant difference in clinical prognosis was observed between normothermic and hyperthermic patients.

Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories

PURPOSE

Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes).

METHODS

Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes.

RESULTS

The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor).

CONCLUSION

Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines.

Hemoadsorption: consensus report of the 30th Acute Disease Quality Initiative workgroup

Adsorption-based extracorporeal therapies have been subject to technical developments and clinical application for close to five decades. More recently, new technological developments in membrane and sorbent manipulation have made it possible to deliver more biocompatible extracorporeal adsorption therapies to patients with a variety of conditions. There are several key rationales based on physicochemical principles and clinical considerations that justify the application and investigation of such therapies as evidenced by multiple ex vivo, experimental and clinical observations. Accordingly, unspecific adsorptive extracorporeal therapies have now been applied to the treatment of a wide array of conditions from poisoning to drug overdoses, to inflammatory states and sepsis, and acute or chronic liver and kidney failure. In response to the rapidly expanding knowledge base and increased clinical evidence, we convened an Acute Disease Quality Initiative consensus conference dedicated to such treatment. The data show that hemoadsorption has clinically acceptable short-term biocompatibility and safety, technical feasibility and experimental demonstration of specified target molecule removal. Pilot studies demonstrate potentially beneficial effects on physiology and larger studies of endotoxin-based hemoadsorption have identified possible target phenotypes for larger randomized controlled trials. Moreover, in a variety of endogenous and exogenous intoxications, removal of target molecules has been confirmed in vivo. However, some studies have raised concerns about harm, or failed to deliver benefits. Thus, despite many achievements, modern hemoadsorption remains a novel and experimental intervention with limited data, and a large research agenda.

Clinical Subtype Trajectories in Sepsis Patients Admitted to the ICU: A Secondary Analysis of an Observational Study

OBJECTIVES

Sepsis is an evolving process and proposed subtypes may change over time. We hypothesized that previously established sepsis subtypes are dynamic, prognostic of outcome, and trajectories are associated with host response alterations.

DESIGN

A secondary analysis of two observational critically ill sepsis cohorts: the Molecular diAgnosis and Risk stratification of Sepsis (MARS) and the Medical Information Mart for Intensive Care-IV (MIMIC-IV).

SETTING

ICUs in the Netherlands and United States between 2011-2014 and 2008-2019, respectively.

PARTICIPANTS

Patient admission fulfilling the Sepsis-3 criteria upon ICU admission adjudicated to one of four previously identified subtypes, comprising 2,416 admissions in MARS and 10,745 in MIMIC-IV.

MAIN OUTCOMES AND MEASURES

Subtype stability and the changes per subtype on days 2, 4 and 7 of ICU admission were assessed. Next, the associated between change in clinical subtype and outcome and host response alterations.

RESULTS

In MARS, upon ICU admission, 6% (n = 150) of the patient admissions were α-type, 3% (n = 70) β-type, 55% (n = 1317) γ-type, and 36% (n = 879) δ-type; in MIMIC-IV, this was α = 22% (n = 2398), β = 22% (n = 2365), γ = 31% (n = 3296), and δ = 25% (2686). Overall, prevalence of subtypes was stable over days 2, 4, and 7. However, 28-56% (MARS/MIMIC-IV) changed from α on ICU admission to any of the other subtypes on day 2, 33-71% from β, 57-32% from γ, and 50-48% from δ. On day 4, overall subtype persistence was 33-36%. γ or δ admissions remaining in, or transitioning to, subtype γ on days 2, 4, and 7 exhibited lower mortality rates compared with those remaining in, or transitioning to, subtype δ. Longitudinal host response biomarkers reflecting inflammation, coagulation, and endothelial dysfunction were most altered in the δ-δ group, followed by the γ-δ group, independent of the day or biomarker domain.

CONCLUSIONS AND RELEVANCE

In two large cohorts, subtype change to δ was associated with worse clinical outcome and more aberrant biomarkers reflecting inflammation, coagulation, and endothelial dysfunction. These findings underscore the importance of monitoring sepsis subtypes and their linked host responses for improved prognostication and personalized treatment strategies.

A Multivariate Phenotypical Approach of Sepsis and Septic Shock-A Comprehensive Narrative Literature Review

Despite medical advances, sepsis and septic shock remain some of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Evidence suggests that pulmonary sepsis is one of the most severe forms of sepsis, while liver dysfunction, left ventricular dysfunction, and coagulopathy impact the prognostic. Sepsis-related hypothermia and a hypoinflammatory state are related to a poor outcome. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis techniques, a new, personalized approach to sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms, and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients' comorbidities, body mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to the individual particularities of sepsis-related multi-organ failure. Finally, the patient's trajectory in the ICU points out the need for a better understanding of interindividual responses to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes as a useful clinical tool for a precision-based approach in sepsis and septic shock.

Early Mortality Stratification with Serum Albumin and the Sequential Organ Failure Assessment Score at Emergency Department Admission in Septic Shock Patients

Background: Early risk stratification is crucial due to septic patients' heterogeneity. Serum albumin level may reflect the severity of sepsis and host status. This study aimed to evaluate the prognostic ability of the initial sequential organ failure assessment (SOFA) score alone and combined with serum albumin levels for predicting 28-day mortality in patients with septic shock. Methods: We conducted an observational study using a prospective, multicenter registry of septic shock patients between October 2015 and May 2022 from 12 emergency departments in the Korean Shock Society and the results were validated by examining those from the septic shock cohort in Asan Medical Center. The primary outcome was 28-day mortality. The area under the receiver operating characteristic (ROC) curve was used to compare the predictive values of SOFA score alone and SOFA score combined with serum albumin level. Results: Among 5805 septic shock patients, 1529 (26.3%) died within 28 days. Mortality increased stepwise with decreasing serum albumin levels (13.6% in albumin ≥3.5, 20.7% in 3.5-3.0, 29.7% in 3.0-2.5, 44.0% in 2.5-2.0, 56.4% in <2.0). The albumin SOFA score was calculated by adding the initial SOFA score to the 4 points assigned for albumin levels. ROC analysis for predicting 28-day mortality showed that the area under the curve for the albumin SOFA score was 0.71 (95% CI 0.70-0.73), which was significantly higher than that of the initial SOFA score alone (0.68, 95% CI: 0.67-0.69). Conclusions: The combination of the initial SOFA score with albumin can improve prognostic accuracy for patients with septic shock, suggesting the albumin SOFA score may be used as an early mortality stratification tool.

Temperature trajectories and mortality in hypothermic sepsis patients

OBJECTIVES

Hypothermia is associated with poor outcomes in sepsis patients, and hypothermic sepsis patients exhibit temperature alterations during initial treatment. The objective of this study was to classify hypothermic sepsis patients based on body temperature trajectories and investigate the associations of these patients with 28-day mortality.

METHODS

This was a retrospective analysis of prospectively collected data from adult sepsis or septic shock patients who visited three emergency departments between August 2014 and December 2019. Hypothermic sepsis was defined as an initial body temperature <36 °C. delta temperature was calculated by subtracting the 0 h body temperature from the 6 h body temperature. We divided the patients into three groups according to delta temperature: Group A (delta temperature ≤ 0), Group B (0 < delta temperature ≤ 1) and Group C (delta temperature > 1). The primary outcome was 28-day mortality, and a multivariable Cox proportional hazards regression model was generated.

RESULTS

Among 7344 patients with sepsis or septic shock, 325 hypothermic patients were included in the analysis, and the overall mortality rate was 36%. While initial body temperature was not different between survivors and nonsurvivors, survivors exhibited a higher body temperature at 6 h. The 28-day mortality rates for Groups A, B and C were 53.1%, 36.0%, and 30.0%, respectively, and Group A had significantly higher mortality than Group C did (p < 0.05). Group C demonstrated a 44.2% decrease in 28-day mortality compared to Group A (adjusted hazard ratio of 0.558; 95% confidence interval of 0.330-0.941).

CONCLUSIONS

In hypothermic sepsis patients, an increase of 1 °C or more in body temperature after the initial 6 h is associated with a reduced risk of 28-day mortality.

Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study

BACKGROUND

Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis, considering multi-organ dynamics. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate the generalizability of the derived phenotypes.

METHODS

We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥ 24 h. Data from two different high-volume academic hospital centers were used, where all phenotypes were derived in MICU of Hospital-I (N = 3225). The derived phenotypes were validated in MICU of Hospital-II (N = 848), SICU of Hospital-I (N = 1112), and SICU of Hospital-II (N = 465). Clinical data from 24 h preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.

RESULTS

Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F = 123]), C (mild hypoxia [median P/F = 240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing the MICU of Hospital-II and SICUs from Hospital-I and -II. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p < 0.01) and consistent across MICU and SICU of both Hospital-I and -II. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.

CONCLUSION

The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research

Background

Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.

Methods

A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models.

Results

We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.

Conclusions

CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.

The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol

Importance

Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial mortality benefit from balanced crystalloids compared with normal saline.

Objective

To test the hypothesis that targeting balanced crystalloids to patients with group D sepsis through an electronic health record (EHR) alert will reduce 30-day inpatient mortality.

Design, Setting, and Participants

The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) trial is a parallel-group, multihospital, single-blind, pragmatic randomized clinical trial to be conducted at 6 hospitals in the Emory Healthcare system. Patients with suspicion of group D infection in whom a clinician initiates an order for normal saline in the emergency department (ED) or intensive care unit (ICU) will be randomized to usual care and intervention arms.

Intervention

An EHR alert that appears in the ED and ICUs to nudge clinicians to use balanced crystalloids instead of normal saline.

Main Outcomes and Measures

The primary outcome is 30-day inpatient mortality. Secondary outcomes are ICU admission, in-hospital mortality, receipt of vasoactive drugs, receipt of new kidney replacement therapy, and receipt of mechanical ventilation (vasoactive drugs, kidney replacement therapy, and mechanical ventilation are counted if they occur after randomization and within the 30-day study period). Intention-to-treat analysis will be conducted.

Discussion

The PRECISE trial may be one of the first precision medicine trials of crystalloid fluids in sepsis. Using routine vital signs (temperature, heart rate, respiratory rate, and blood pressure), available even in low-resource settings, a validated machine learning algorithm will prospectively identify and enroll patients with group D sepsis who may have a substantial mortality reduction from used of balanced crystalloids compared with normal saline.

Results

On finalizing participant enrollment and analyzing the data, the study's findings will be shared with the public through publication in a peer-reviewed journal.

Conclusions

With use of a validated machine learning algorithm, precision resuscitation in sepsis could fundamentally redefine international standards for intravenous fluid resuscitation.

Trial Registration

ClinicalTrials.gov Identifier: NCT06253585.

Novel cortisol trajectory sub-phenotypes in sepsis

BACKGROUND

Sepsis is a heterogeneous syndrome. This study aimed to identify new sepsis sub-phenotypes using plasma cortisol trajectory.

METHODS

This retrospective study included patients with sepsis admitted to the intensive care unit of Zhongshan Hospital Fudan University between March 2020 and July 2022. A group-based cortisol trajectory model was used to classify septic patients into different sub-phenotypes. The clinical characteristics, biomarkers, and outcomes were compared between sub-phenotypes.

RESULTS

A total of 258 patients with sepsis were included, of whom 186 were male. Patients were divided into two trajectory groups: the lower-cortisol group (n = 217) exhibited consistently low and slowly declining cortisol levels, while the higher-cortisol group (n = 41) showed relatively higher levels in comparison. The 28-day mortality (65.9% vs.16.1%, P < 0.001) and 90-day mortality (65.9% vs. 19.8%, P < 0.001) of the higher-cortisol group were significantly higher than the lower-cortisol group. Multivariable Cox regression analysis showed that the trajectory sub-phenotype (HR = 5.292; 95% CI 2.218-12.626; P < 0.001), APACHE II (HR = 1.109; 95% CI 1.030-1.193; P = 0.006), SOFA (HR = 1.161; 95% CI 1.045-1.291; P = 0.006), and IL-1β (HR = 1.001; 95% CI 1.000-1.002; P = 0.007) were independent risk factors for 28-day mortality. Besides, the trajectory sub-phenotype (HR = 4.571; 95% CI 1.980-10.551; P < 0.001), APACHE II (HR = 1.108; 95% CI 1.043-1.177; P = 0.001), SOFA (HR = 1.270; 95% CI 1.130-1.428; P < 0.001), and IL-1β (HR = 1.001; 95% CI 1.000-1.001; P = 0.015) were also independent risk factors for 90-day mortality.

CONCLUSION

This study identified two novel cortisol trajectory sub-phenotypes in patients with sepsis. The trajectories were associated with mortality, providing new insights into sepsis classification.

Longitudinal Vasoactive Inotrope Score Trajectories and Their Prognostic Significance in Critically Ill Sepsis Patients: A Retrospective Cohort Analysis

PURPOSE

Sepsis continues to be a critical issue in intensive care, characterized by significant morbidity and mortality. This study explores the association between Vasoactive Inotrope Score (VIS) trajectories and 28-day mortality in ICU patients with sepsis, employing VIS trajectories as a marker for assessing severity and guiding therapy.

METHODS

We conducted a retrospective analysis of the MIMIC-IV database, which included sepsis patients admitted to the ICU between 2008 and 2019. VIS calculations were performed bi-hourly during the first 72 hours of ICU admission. Using latent growth mixture modeling, we identified distinct VIS trajectory patterns, and multivariate Cox proportional hazards models were employed to evaluate their association with 28-day mortality.

FINDINGS

Among 6,802 sepsis patients who met the inclusion criteria, four distinct VIS trajectory patterns were identified: "Low-Decreasing" (52.1%), "Mild-Ascending" (13.2%), "Moderate-Decreasing" (23.0%), and "High-Stable" (11.6%). The 28-day survival analysis demonstrated that, compared to the "Low-Decreasing" group, the "Mild-Ascending" group had a hazard ratio (HR) for mortality of 2.55 (95% CI: 2.19-2.97, P < 0.001), the "Moderate-Decreasing" group had an HR of 1.20 (95% CI: 1.03-1.41, P = 0.021), and the "High-Stable" group presented the highest risk with an HR of 4.19 (95% CI: 3.43-5.12, P < 0.001).

IMPLICATIONS

This study offers significant insights into the prognostic value of VIS trajectories in sepsis patients. The identification of distinct trajectory patterns not only underscores the heterogeneity in sepsis but also emphasizes the importance of personalized management strategies. The findings underscore the potential of VIS trajectory monitoring in predicting 28-day outcomes and in guiding clinical decision-making in ICU settings.

Informative Subtyping of Patients with Sepsis

Sepsis pathobiology is complex. Heterogeneity refers to the clinical and biological variation within sepsis cohorts. Sepsis subtypes refer to subpopulations within sepsis cohorts derived based on these observable variations and latent features. The overarching goal of such endeavors is to enable precision immunomodulation. However, we are yet to identify immune endotypes of sepsis to achieve this goal. The sepsis subtyping field is just starting to take shape. The current subtypes in the literature do not have a core set of shared features between studies. Thus, in this narrative review, we reason that there is a need to a priori state the purpose of sepsis subtyping and minimum set of features that would be required to achieve the goal of precision immunomodulation for future sepsis.

Association Between Persistent Hypothermia After Cardiopulmonary Bypass in Neonates and Odds of Serious Complications

IMPORTANCE

Persistent hypothermia after cardiopulmonary bypass (CPB) in neonates with congenital heart defects (CHD) has been historically considered benign despite lack of evidence on its prognostic significance.

OBJECTIVES

Examine associations between the magnitude and pattern of unintentional postoperative hypothermia and odds of complications in neonates with CHD undergoing CPB.

DESIGN

Retrospective cohort study.

SETTING

Single northeastern U.S., urban pediatric quaternary care center with an established cardiac surgery program.

PARTICIPANTS

Population-based sample of neonates greater than or equal to 34 weeks gestation undergoing their first CPB between 2015 and 2019.

INTERVENTIONS

None.

MAIN OUTCOMES AND MEASUREMENTS

Hourly temperature measurements for the first 48 postoperative hours were extracted from inpatient medical records, and clinical characteristics and outcomes were accessed through the local patient registry. Group-based trajectory modeling (GBTM) identified latent temporal temperature trajectories. Associations of trajectories with outcomes were assessed using multivariable binary logistic regression. Outcomes (postoperative complications) were manually adjudicated by experts or were predefined by the patient registry.

RESULTS

Four hundred fifty neonates met inclusion criteria. Their mean (sd) gestational age was 38 weeks (1.3), mean (sd) birth weight was 3.19 kilograms (0.55), median (interquartile range) surgical age was 4.7 days (3.3-7.0), 284 of 450 (63%) were male, and 272 of 450 (60%) were White. GBTM identified three distinct curvilinear temperature trajectories: persistent hypothermia (n = 38, 9%), resolving hypothermia (n = 233, 52%), and normothermia (n = 179, 40%). Compared with the normothermic group, those with persistent hypothermia had significantly higher odds of cardiac arrest, actionable arrhythmia, delayed first successful extubation, prolonged cardiac ICU length of stay, very poor weight gain, and 30-day hospital mortality. The persistent hypothermia group was characterized by greater odds of having a lower gestational age, more prevalent neurologic abnormalities, more unplanned reoperations, and a low surgical mortality risk assessment.

CONCLUSIONS

Persistent postoperative hypothermia in neonates after CPB is independently associated with having greater odds of complications. Recovery patterns from postoperative hypothermia may be a clinically useful marker to identify patient instability in neonates. Additional research is needed for causal modeling and prospective validation before clinical adoption.

Cardiogenic shock trajectories: is the Society for Cardiovascular Angiography and Interventions definition the right one?

PURPOSE OF REVIEW

We review the current Society for Cardiovascular Angiography and Interventions (SCAI) cardiogenic shock classification system and consider alternatives or iterations that may enhance our current descriptions of cardiogenic shock trajectory.

RECENT FINDINGS

Several studies have identified the potential prognostic value of serial SCAI stage re-assessment, usually within the first 24 h of shock onset, to predict deterioration and clinical outcomes across shock causes. In parallel, numerous registry-based analyses support the utility of a more precise assessment of the macrocirculation and microcirculation, leveraging invasive haemodynamics, imaging and additional laboratory and clinical markers. The emergence of machine learning and artificial intelligence capabilities offers the opportunity to integrate multimodal data into high fidelity, real-time metrics to more precisely define trajectory and inform our therapeutic decision making.

SUMMARY

Whilst the SCAI staging system remains a pivotal tool in cardiogenic shock assessment, communication and reassessment, it is vital that the sophistication with which we measure and assess shock trajectory evolves in parallel our understanding of the complexity and variability of clinical course and clinical outcomes.

Clinical phenotypes of sepsis: a narrative review

Background and Objective

Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis.

Methods

We searched PubMed with the terms "Clinical phenotypes AND sepsis" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided.

Key Content and Findings

While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics.

Conclusions

The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.

The potential immunological mechanisms of sepsis

Sepsis is described as a life-threatening organ dysfunction and a heterogeneous syndrome that is a leading cause of morbidity and mortality in intensive care settings. Severe sepsis could incite an uncontrollable surge of inflammatory cytokines, and the host immune system's immunosuppression could respond to counter excessive inflammatory responses, characterized by the accumulated anti-inflammatory cytokines, impaired function of immune cells, over-proliferation of myeloid-derived suppressor cells and regulatory T cells, depletion of immune effector cells by different means of death, etc. In this review, we delve into the underlying pathological mechanisms of sepsis, emphasizing both the hyperinflammatory phase and the associated immunosuppression. We offer an in-depth exploration of the critical mechanisms underlying sepsis, spanning from individual immune cells to a holistic organ perspective, and further down to the epigenetic and metabolic reprogramming. Furthermore, we outline the strengths of artificial intelligence in analyzing extensive datasets pertaining to septic patients, showcasing how classifiers trained on various clinical data sources can identify distinct sepsis phenotypes and thus to guide personalized therapy strategies for the management of sepsis. Additionally, we provide a comprehensive summary of recent, reliable biomarkers for hyperinflammatory and immunosuppressive states, facilitating more precise and expedited diagnosis of sepsis.

Inflammatory Trajectory and Anti-Inflammatory Pharmacotherapy in Frozen Elephant Trunk-Treated Acute Type I Aortic Dissection

Background

Acute DeBakey type I aortic dissection is associated with high morbidity and mortality. Little is known regarding the role of leukocyte trajectory in prognosis.

Methods

We included adult acute DeBakey type I aortic dissection patients with emergency frozen elephant trunk and total arch replacement in 2 cardiovascular centers (2020-2022). We used latent class mixed model to group patients according to their leukocyte patterns from hospital admission to the first 5 days after surgery. We investigated the association of leukocyte trajectory and 30-day and latest follow-up mortality (October 31, 2023), exploratorily analyzing the effects of ulinastatin treatment on outcome.

Results

Of 255 patients included, 3 distinct leukocyte trajectories were identified: 196 in group I (decreasing trajectory), 34 in group II (stable trajectory), and 25 in group III (rising trajectory). Overall, 30-day mortality was 25 (9.8%), ranging from 8.2% (16/196) in group I, 8.8% (3/34) in group II, to 24.0% (6/25) in group III (P for trend = .036). Group III was associated with higher mortality both at 30 days (adjusted hazard ratio, 3.260; 95% CI, 1.071-9.919; P = .037) and at the last follow-up (adjusted hazard ratio, 2.840; 95% CI, 1.098-7.345; P = .031) compared with group I.

Conclusions

Distinct and clinically relevant groups can be identified by analyzing leukocyte trajectories, and a rising trajectory was associated with higher short-term and midterm mortality.

Identifying novel clinical phenotypes of acute respiratory distress syndrome using trajectories of daily fluid balance: a secondary analysis of randomized controlled trials

BACKGROUND

Previously identified phenotypes of acute respiratory distress syndrome (ARDS) could not reveal the dynamic change of phenotypes over time. We aimed to identify novel clinical phenotypes in ARDS using trajectories of fluid balance, to test whether phenotypes respond differently to different treatment, and to develop a simplified model for phenotype identification.

METHODS

FACTT (conservative vs liberal fluid management) trial was classified as a development cohort, joint latent class mixed models (JLCMMs) were employed to identify trajectories of fluid balance. Heterogeneity of treatment effect (HTE) for fluid management strategy across phenotypes was investigated. We also constructed a parsimonious probabilistic model using baseline data to predict the fluid trajectories in the development cohort. The trajectory groups and the probabilistic model were externally validated in EDEN (initial trophic vs full enteral feeding) trial.

RESULTS

Using JLCMM, we identified two trajectory groups in the development cohort: Class 1 (n = 758, 76.4% of the cohort) had an early positive fluid balance, but achieved negative fluid balance rapidly, and Class 2 (n = 234, 24.6% of the cohort) was characterized by persistent positive fluid balance. Compared to Class 1 patients, patients in Class 2 had significantly higher 60-day mortality (53.5% vs. 17.8%, p < 0.001), and fewer ventilator-free days (0 vs. 20, p < 0.001). A significant HTE between phenotypes and fluid management strategies was observed in the FACTT. An 8-variables model was derived for phenotype assignment.

CONCLUSIONS

We identified and validated two novel clinical trajectories for ARDS patients, with both prognostic and predictive enrichment. The trajectories of ARDS can be identified with simple classifier models.

Physiologic disruption and metabolic reprogramming in infection and sepsis

Effective responses against severe systemic infection require coordination between two complementary defense strategies that minimize the negative impact of infection on the host: resistance, aimed at pathogen elimination, and disease tolerance, which limits tissue damage and preserves organ function. Resistance and disease tolerance mostly rely on divergent metabolic programs that may not operate simultaneously in time and space. Due to evolutionary reasons, the host initially prioritizes the elimination of the pathogen, leading to dominant resistance mechanisms at the potential expense of disease tolerance, which can contribute to organ failure. Here, we summarize our current understanding of the role of physiological perturbations resulting from infection in immune response dynamics and the metabolic program requirements associated with resistance and disease tolerance mechanisms. We then discuss how insight into the interplay of these mechanisms could inform future research aimed at improving sepsis outcomes and the potential for therapeutic interventions.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection

OBJECTIVES

To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery.

METHODS

We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method.

RESULTS

In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2.

CONCLUSIONS

CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.

Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective Study

Background

Septic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of these patients may reveal insights about the broader heterogeneity in the clinical course of sepsis. We aimed to derive novel phenotypes of sepsis-induced ARF using observational clinical data and investigate their generalizability across multi-ICU specialties, considering multi-organ dynamics.

Methods

We performed a multi-center retrospective study of ICU patients with sepsis who required mechanical ventilation for ≥24 hours. Data from two different high-volume academic hospital systems were used as a derivation set with N=3,225 medical ICU (MICU) patients and a validation set with N=848 MICU patients. For the multi-ICU validation, we utilized retrospective data from two surgical ICUs at the same hospitals (N=1,577). Clinical data from 24 hours preceding intubation was used to derive distinct phenotypes using an explainable machine learning-based clustering model interpreted by clinical experts.

Results

Four distinct ARF phenotypes were identified: A (severe multi-organ dysfunction (MOD) with a high likelihood of kidney injury and heart failure), B (severe hypoxemic respiratory failure [median P/F=123]), C (mild hypoxia [median P/F=240]), and D (severe MOD with a high likelihood of hepatic injury, coagulopathy, and lactic acidosis). Patients in each phenotype showed differences in clinical course and mortality rates despite similarities in demographics and admission co-morbidities. The phenotypes were reproduced in external validation utilizing an external MICU from second hospital and SICUs from both centers. Kaplan-Meier analysis showed significant difference in 28-day mortality across the phenotypes (p<0.01) and consistent across both centers. The phenotypes demonstrated differences in treatment effects associated with high positive end-expiratory pressure (PEEP) strategy.

Conclusion

The phenotypes demonstrated unique patterns of organ injury and differences in clinical outcomes, which may help inform future research and clinical trial design for tailored management strategies.

Identifying novel acute pancreatitis sub-phenotypes using total serum calcium trajectories

BACKGROUND

Acute pancreatitis (AP) has heterogeneous clinical features, and identifying clinically relevant sub-phenotypes is useful. We aimed to identify novel sub-phenotypes in hospitalized AP patients using longitudinal total serum calcium (TSC) trajectories.

METHODS

AP patients had at least two TSC measurements during the first 24 h of hospitalization in the US-based critical care database (Medical Information Mart for Intensive Care-III (MIMIC-III) and MIMIC-IV were included. Group-based trajectory modeling was used to identify calcium trajectory phenotypes, and patient characteristics and treatment outcomes were compared between the phenotypes.

RESULTS

A total of 4518 admissions were included in the analysis. Four TSC trajectory groups were identified: "Very low TSC, slow resolvers" (n = 65; 1.4% of the cohort); "Moderately low TSC" (n = 559; 12.4%); "Stable normal-calcium" (n = 3875; 85.8%); and "Fluctuating high TSC" (n = 19; 0.4%). The "Very low TSC, slow resolvers" had the lowest initial, maximum, minimum, and mean TSC, and highest SOFA score, creatinine and glucose level. In contrast, the "Stable normal-calcium" had the fewest ICU admission, antibiotic use, intubation and renal replace treatment. In adjusted analysis, significantly higher in-hospital mortality was noted among "Very low TSC, slow resolvers" (odds ratio [OR], 7.2; 95% CI, 3.7 to 14.0), "moderately low TSC" (OR, 5.0; 95% CI, 3.8 to 6.7), and "Fluctuating high TSC" (OR, 5.6; 95% CI, 1.5 to 20.6) compared with the "Stable normal-calcium" group.

CONCLUSIONS

We identified four novel sub-phenotypes of patients with AP, with significant variability in clinical outcomes. Not only the absolute TSC levels but also their trajectories were significantly associated with in-hospital mortality.

Clinical impact of transient lymphopenia

Transient or persistent immunosuppression is a known risk factor for morbidity and mortality in critically ill patients. Aim of the present study is to evaluate the lymphopenia in patients admitted to the Emergency Unit of AOU Policlinico Umberto I, to investigate its prevalence at admission and the persistence during hospitalization until discharge. Possible correlations were evaluated between lymphopenia, diagnosis of admission, comorbidities and chronic treatments. In this study, 240 patients (142 men; 98 female; mean age 75.1 ± 15.1) were enrolled. Patients were divided into two groups according to the lymphocytes count at hospital admission, namely "Group A" with lymphopenia and "Group B" with values in the normal range. Moreover, the patients in group A were distinguished in relation to the regression or persistence of the lymphopenia assessed at the time of hospital discharge (Group A1: persistence; Group A2: normalization). Prevalence of lymphopenia at admission was 57%; Group A showed higher mean age and percentage of patients over 65 years of age; and none differences were observed regarding gender. Prevalence of lymphopenia at admission was 57%; Group A showed higher mean age and percentage of patients over 65 years of age; no differences were observed regarding gender. All subsets of the lymphocytes (CD4+, CD8+, NK) were equally reduced. Persistent lymphopenia was found in 19% of patients. Lymphopenia should be valued at the time of hospital admission as a factor influencing the prognosis, the management and the treatment of these patients.

Identification and validation of sepsis subphenotypes using time-series data

Purpose

The recognition of sepsis as a heterogeneous syndrome necessitates identifying distinct subphenotypes to select targeted treatment.

Methods

Patients with sepsis from the MIMIC-IV database (2008-2019) were randomly divided into a development cohort (80%) and an internal validation cohort (20%). Patients with sepsis from the ICU database of Peking University People's Hospital (2008-2022) were included in the external validation cohort. Time-series k-means clustering analysis and dynamic time warping was performed to develop and validate sepsis subphenotypes by analyzing the trends of 21 vital signs and laboratory indicators within 24 h after sepsis onset. Inflammatory biomarkers were compared in the ICU database of Peking University People's Hospital, whereas treatment heterogeneity was compared in the MIMIC-IV database.

Findings

Three sub-phenotypes were identified in the development cohort. Type A patients (N = 2525, 47%) exhibited stable vital signs and fair organ function, type B (N = 1552, 29%) was exhibited an obvious inflammatory response and stable organ function, and type C (N = 1251, 24%) exhibited severely impaired organ function with a deteriorating tendency. Type C demonstrated the highest mortality rate (33%) and levels of inflammatory biomarkers, followed by type B (24%), whereas type A exhibited the lowest mortality rate (11%) and levels of inflammatory biomarkers. These subphenotypes were confirmed in both the internal and external cohorts, demonstrating similar features and comparable mortality rates. In type C patients, survivors had significantly lower fluid intake within 24 h after sepsis onset (median 2891 mL, interquartile range (IQR) 1530-5470 mL) than that in non-survivors (median 4342 mL, IQR 2189-7305 mL). For types B and C, survivors showed a higher proportion of indwelling central venous catheters (p < 0.05).

Conclusion

Three novel phenotypes of patients with sepsis were identified and validated using time-series data, revealing significant heterogeneity in inflammatory biomarkers, treatments, and consistency across cohorts.

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B's favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

Micronutrients as therapy in critical illness

PURPOSE OF REVIEW

Recent large-scale randomized controlled trials (RCTs) challenged current beliefs about the potential role of micronutrients to attenuate the inflammatory response and improve clinical outcomes of critically ill patients. The purpose of this narrative review is to provide an overview and critical discussion about most recent clinical trials, which evaluated the clinical significance of a vitamin C, vitamin D, or selenium administration in critically ill patients.

RECENT FINDINGS

None of the most recent large-scale RCTs could demonstrate any clinical benefits for a micronutrient administration in ICU patients, whereas a recent RCT indicated harmful effects, if high dose vitamin C was administered in septic patients. Following meta-analyses could not confirm harmful effects for high dose vitamin C in general critically ill patients and indicated benefits in the subgroup of general ICU patients with higher mortality risk. For vitamin D, the most recent large-scale RCT could not demonstrate clinical benefits for critically ill patients, whereas another large-scale RCT is still ongoing. The aggregated and meta-analyzed evidence highlighted a potential role for intravenous vitamin D administration, which encourages further research. In high-risk cardiac surgery patients, a perioperative application of high-dose selenium was unable to improve patients' outcome. The observed increase of selenium levels in the patients' blood did not translate into an increase of antioxidative or anti-inflammatory enzymes, which illuminates the urgent need for more research to identify potential confounding factors.

SUMMARY

Current data received from most recent large-scale RCTs could not demonstrate clinically meaningful effects of an intervention with either vitamin C, vitamin D, or selenium in critically ill patients. More attention is needed to carefully identify potential confounding factors and to better evaluate the role of timing, duration, and combined strategies.

Reframing sepsis immunobiology for translation: towards informative subtyping and targeted immunomodulatory therapies

Sepsis is a common and deadly condition. Within the current model of sepsis immunobiology, the framing of dysregulated host immune responses into proinflammatory and immunosuppressive responses for the testing of novel treatments has not resulted in successful immunomodulatory therapies. Thus, the recent focus has been to parse observable heterogeneity into subtypes of sepsis to enable personalised immunomodulation. In this Personal View, we highlight that many fundamental immunological concepts such as resistance, disease tolerance, resilience, resolution, and repair are not incorporated into the current sepsis immunobiology model. The focus for addressing heterogeneity in sepsis should be broadened beyond subtyping to encompass the identification of deterministic molecular networks or dominant mechanisms. We explicitly reframe the dysregulated host immune responses in sepsis as altered homoeostasis with pathological disruption of immune-driven resistance, disease tolerance, resilience, and resolution mechanisms. Our proposal highlights opportunities to identify novel treatment targets and could enable successful immunomodulation in the future.

Using Trajectories of Bedside Vital Signs to Identify COVID-19 Subphenotypes

BACKGROUND

Trajectories of bedside vital signs have been used to identify sepsis subphenotypes with distinct outcomes and treatment responses. The objective of this study was to validate the vitals trajectory model in a multicenter cohort of patients hospitalized with COVID-19 and to evaluate the clinical characteristics and outcomes of the resulting subphenotypes.

RESEARCH QUESTION

Can the trajectory of routine bedside vital signs identify COVID-19 subphenotypes with distinct clinical characteristics and outcomes?

STUDY DESIGN AND METHODS

The study included adult patients admitted with COVID-19 to four academic hospitals in the Emory Healthcare system between March 1, 2020, and May 31, 2022. Using a validated group-based trajectory model, we classified patients into previously defined vital sign trajectories using oral temperature, heart rate, respiratory rate, and systolic and diastolic BP measured in the first 8 h of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes. Heterogeneity of treatment effect to tocilizumab was evaluated.

RESULTS

The 7,065 patients with hospitalized COVID-19 were classified into four subphenotypes: group A (n = 1,429, 20%)-high temperature, heart rate, respiratory rate, and hypotensive; group B (1,454, 21%)-high temperature, heart rate, respiratory rate, and hypertensive; group C (2,996, 42%)-low temperature, heart rate, respiratory rate, and normotensive; and group D (1,186, 17%)-low temperature, heart rate, respiratory rate, and hypotensive. Groups A and D had higher ORs of mechanical ventilation, vasopressors, and 30-day inpatient mortality (P < .001). On comparing patients receiving tocilizumab (n = 55) with those who met criteria for tocilizumab but were admitted before its use (n = 461), there was significant heterogeneity of treatment effect across subphenotypes in the association of tocilizumab with 30-day mortality (P = .001).

INTERPRETATION

By using bedside vital signs available in even low-resource settings, we found novel subphenotypes associated with distinct manifestations of COVID-19, which could lead to preemptive and targeted treatments.

Advances in sepsis biomarkers

Sepsis, a dysregulated host immune response to an infectious agent, significantly increases morbidity and mortality for hospitalized patients worldwide. This chapter reviews (1) the basic principles of infectious diseases, pathophysiology and current definition of sepsis, (2) established sepsis biomarkers such lactate, procalcitonin and C-reactive protein, (3) novel, newly regulatory-cleared/approved biomarkers, such as assays that evaluate white blood cell properties and immune response molecules, and (4) emerging biomarkers and biomarker panels to highlight future directions and opportunities in the diagnosis and management of sepsis.

Surviving Sepsis Campaign Research Priorities 2023

OBJECTIVES

To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock.

DESIGN

Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities.

METHODS

Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article.

RESULTS

A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis?

CONCLUSIONS

Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome

BACKGROUND

The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored.

METHODS

Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test.

RESULTS

A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020).

CONCLUSIONS

For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.

COAGULOPATHY PARAMETERS PREDICTIVE OF OUTCOMES IN SEPSIS-INDUCED ACUTE RESPIRATORY DISTRESS SYNDROME: A SUBANALYSIS OF THE TWO PROSPECTIVE MULTICENTER COHORT STUDIES

ABSTRACT

Background: Although coagulopathy is often observed in acute respiratory distress syndrome (ARDS), its clinical impact remains poorly understood. Objectives: This study aimed to clarify the coagulopathy parameters that are clinically applicable for prognostication and to determine anticoagulant indications in sepsis-induced ARDS. Method: This study enrolled patients with sepsis-derived ARDS from two nationwide multicenter, prospective observational studies. We explored coagulopathy parameters that could predict outcomes in the Focused Outcome Research on Emergency Care for Acute Respiratory Distress Syndrome, Sepsis, and Trauma (FORECAST) cohort, and the defined coagulopathy criteria were validated in the Sepsis Prognostication in Intensive Care Unit and Emergency Room-Intensive Care Unit (SPICE-ICU) cohort. The correlation between anticoagulant use and outcomes was also evaluated. Results: A total of 181 patients with sepsis-derived ARDS in the FORECAST study and 61 patients in the SPICE-ICU study were included. In a preliminary study, we found the set of prothrombin time-international normalized ratio ≥1.4 and platelet count ≤12 × 10 4 /μL, and thrombocytopenia and elongated prothrombin time (TEP) coagulopathy as the best coagulopathy parameters and used it for further analysis; the odds ratio (OR) of TEP coagulopathy for in-hospital mortality adjusted for confounding was 3.84 (95% confidence interval [CI], 1.66-8.87; P = 0.005). In the validation cohort, the adjusted OR for in-hospital mortality was 32.99 (95% CI, 2.60-418.72; P = 0.002). Although patients without TEP coagulopathy showed significant improvements in oxygenation over the first 4 days, patients with TEP coagulopathy showed no significant improvement (ΔPaO 2 /FiO 2 ratio, 24 ± 20 vs. 90 ± 9; P = 0.026). Furthermore, anticoagulant use was significantly correlated with mortality and oxygenation recovery in patients with TEP coagulopathy but not in patients without TEP coagulopathy. Conclusion: Thrombocytopenia and elongated prothrombin time coagulopathy is closely associated with better outcomes and responses to anticoagulant therapy in sepsis-induced ARDS, and our coagulopathy criteria may be clinically useful.

Targeting the host response in sepsis: current approaches and future evidence

Sepsis, a dysregulated host response to infection characterized by organ failure, is one of the leading causes of death worldwide. Disbalances of the immune response play an important role in its pathophysiology. Patients may develop simultaneously or concomitantly states of systemic or local hyperinflammation and immunosuppression. Although a variety of effective immunomodulatory treatments are generally available, attempts to inhibit or stimulate the immune system in sepsis have failed so far to improve patients' outcome. The underlying reason is likely multifaceted including failure to identify responders to a specific immune intervention and the complex pathophysiology of organ dysfunction that is not exclusively caused by immunopathology but also includes dysfunction of the coagulation system, parenchymal organs, and the endothelium. Increasing evidence suggests that stratification of the heterogeneous population of septic patients with consideration of their host response might led to treatments that are more effective. The purpose of this review is to provide an overview of current studies aimed at optimizing the many facets of host response and to discuss future perspectives for precision medicine approaches in sepsis.

Temperature control in sepsis

Fever can be viewed as an adaptive response to infection. Temperature control in sepsis is aimed at preventing potential harms associated with high temperature (tachycardia, vasodilation, electrolyte and water loss) and therapeutic hypothermia may be aimed at slowing metabolic activities and protecting organs from inflammation. Although high fever (>39.5°C) control is usually performed in critically ill patients, available cohorts and randomized controlled trials do not support its use to improve sepsis prognosis. Finally, both spontaneous and therapeutic hypothermia are associated with poor outcomes in sepsis.

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

OBJECTIVES

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

DESIGN

Multicenter observational cohort study.

SETTING

Thirteen PICUs in the United States.

PATIENTS

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

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

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

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

Sepsis heterogeneity

BACKGROUND

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

DATA SOURCES

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

RESULTS

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

CONCLUSIONS

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

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

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

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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