Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Group-based Trajectory Modeling of Serum Sodium and Survival in Sepsis Patients with Lactic Acidosis: Results from MIMIC-IV Database

The purpose of this project was to characterize the longitudinal dynamic serum sodium trajectory of sepsis patients with lactic acidosis (LA) admitted to the intensive care unit (ICU), and to explore the association between these trajectories and the 30-day mortality rate of patients. Data on patients admitted to the ICU with a diagnosis of LA combined with sepsis from 2008-2019 were collected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Patients admitted to the ICU for > 24 hours and for the first time were sorted into 3 groups based on their serum sodium levels at admission. The Group-based Trajectory Modeling (GBTM) method was applied to analyze the trajectory changes of serum sodium in each group of patients over 72 hours. Patients' survival differences between different trajectory groups were compared using Kaplan-Meier (K-M) survival curves. Subgroup analysis was carried out to determine the influencing factors of the relationship between dynamic changes in serum sodium and patient survival. This study included 514 patients with LA complicated by sepsis, who were clustered into three groups based on their admission serum sodium levels, with 378 patients in the normal blood sodium (135-145 mEq/L) group, 116 patients in the hyponatremia (< 135 mEq/L) group, and 20 patients in the hypernatremia (> 145 mEq/L) group. GBTM analysis generated three different serum sodium trajectories. The K-M curve results demonstrated that patients with relatively stable serum sodium levels within the normal range (Class 2) had lower 30-day mortality compared to groups with larger fluctuations in sodium levels (Class 1, Class 3). Subgroup analysis uncovered notable interactions (P < 0.05) between different trajectories of serum sodium and covariates such as race, marital status, Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA), renal replacement therapy (RRT), congestive heart failure, kidney disease, liver disease, and diabetes. Among patients with LA complicated by sepsis, those with stable and normal fluctuations in serum sodium levels had better 30-day survival rates. GBTM is a refined method to describe the evolution of serum sodium and its association with clinical outcomes, which may enhance the current understanding of blood sodium level regulation.

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.

Analysis of the correlation between the group-based trajectory modeling of serum osmolality and prognosis in patients with sepsis-associated encephalopathy at 72 h after admission

BACKGROUND

This study aimed to identify distinct trajectories of serum osmolality within the first 72 h for patients with sepsis-associated encephalopathy (SAE) in the MIMIC-IV and eICU-CRD databases and assess their impact on mortality and adverse clinical outcomes.

METHODS

In this retrospective cohort study, patients with SAE from the MIMIC-IV database were included. Group-based trajectory modeling (GBTM) was used to categorize distinct patterns of serum osmolality changes over 72 h in ICU patients. Differences in survival across the trajectory groups were compared using Kaplan-Meier (K-M) survival curves.

RESULTS

A total of 11,376 patients with SAE were included in the analysis, with a median age of 65.6 ± 16.5 years. The in-hospital mortality rate at 30 days was 12.8%. Based on model-defined criteria, three distinct osmolality trajectory groups were identified: Group 1 (59.6%), Group 2 (36.4%), and Group 3 (4.0%). Kaplan-Meier survival analysis indicated that patients with relatively lower serum osmolality within the normal range (Group 1) had a lower 30-day mortality rate compared to those in the other groups (Group 2 and 3). Subgroup analysis demonstrated significant interactions (P < 0.05) between osmolality trajectories and covariates such as the Sequential Organ Failure Assessment (SOFA), vasopressor use and renal replacement therapy (RRT).

CONCLUSION

Identifying distinct serum osmolality trajectories may help recognize SAE patient subgroups with varying risks of adverse outcomes, providing clinically meaningful stratification.

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.

Group based trajectory modeling identifies distinct patterns of sympathetic hyperactivity following traumatic brain injury

Background

Paroxysmal Sympathetic Hyperactivity (PSH) occurs with high prevalence among critically ill Traumatic Brain Injury (TBI) patients and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale (CFS) and a Diagnosis Likelihood Tool (DLT), intended to quantify the severity of sympathetically-mediated symptoms and likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM.

Methods

We performed an observational cohort study of 221 acute critically ill TBI patients for whom daily PSH-AM scores were calculated over the first 14 days of hospitalization. A principled group-based trajectory modeling approach using unsupervised K-means clustering was used to identify distinct patterns of CFS evolution within the cohort. We also evaluated the relationships between trajectory group membership and PSH diagnosis, as well as PSH DLT score, hospital discharge GCS, ICU and hospital length of stay, duration of mechanical ventilation, and mortality. Baseline clinical and demographic features predictive of trajectory group membership were analyzed using univariate screening and multivariate multinomial logistic regression.

Results

We identified four distinct trajectory groups. Trajectory group membership was significantly associated with clinical outcomes including PSH diagnosis and DLT score, ICU length of stay, and duration of mechanical ventilation. Baseline features independently predictive of trajectory group membership included age and post-resuscitation motor GCS.

Conclusions

This study adds to the sparse research characterizing the heterogeneous temporal trends of sympathetic nervous system activation during the acute phase following TBI. This may open avenues for early identification of at-risk patients to receive tailored interventions to limit secondary brain injury associated with autonomic dysfunction and thereby improve TBI patient outcomes.

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.

Temporal patterns of organ dysfunction in COVID-19 patients hospitalized in the intensive care unit: A group-based multitrajectory modeling analysis

BACKGROUND

The course of organ dysfunction (OD) in Corona Virus Disease 2019 (COVID-19) patients is unknown. Herein, we analyze the temporal patterns of OD in intensive care unit-admitted COVID-19 patients.

METHODS

Sequential organ failure assessment scores were evaluated daily within 2 weeks of admission to determine the temporal trajectory of OD using group-based multitrajectory modeling (GBMTM).

RESULTS

A total of 392 patients were enrolled with a 28-day mortality rate of 53.6%. GBMTM identified four distinct trajectories. Group 1 (mild OD, n = 64), with a median APACHE II score of 13 (IQR 9-21), had an early resolution of OD and a low mortality rate. Group 2 (moderate OD, n = 140), with a median APACHE II score of 18 (IQR 13-22), had a 28-day mortality rate of 30.0%. Group 3 (severe OD, n = 117), with a median APACHR II score of 20 (IQR 13-27), had a deterioration trend of respiratory dysfunction and a 28-day mortality rate of 69.2%. Group 4 (extremely severe OD, n = 71), with a median APACHE II score of 20 (IQR 17-27), had a significant and sustained OD affecting all organ systems and a 28-day mortality rate of 97.2%.

CONCLUSIONS

Four distinct trajectories of OD were identified, and respiratory dysfunction trajectory could predict nonpulmonary OD trajectories and patient prognosis.

SOFA in sepsis: with or without GCS

PURPOSE

Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis.

METHODS

First, 3048 patients with sepsis admitted to Peking Union Medical College Hospital (PUMCH) were enrolled in this survey. The data were collected from June 8, 2013 to October 12, 2022. Second, 18,108 patients with sepsis in the eICU database were enrolled. Third, 2397 septic patients with respiratory system ≥ 3 points in SOFA in the eICU database were included. We investigated the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis in various ICUs of PUMCH, and then we validated the results in the eICU database.

MAIN RESULTS

In data of ICUs in PUMCH, the predictive efficiency of SOFA without GCS (AUROC [95% CI], 24 h, 0.724 [0.688, 0.760], 48 h, 0.734 [0.699, 0.769], 72 h, 0.748 [0.713, 0.783], 168 h, 0.781 [0.747, 0.815]) was higher than that of SOFA with GCS (AUROC [95% CI], 24 h, 0.708 [0.672, 0.744], 48 h, 0.721 [0.685, 0.757], 72 h, 0.735 [0.700, 0.757], 168 h, 0.770 [0.736, 0.804]) on ICU mortality in patients with sepsis, and the difference was statistically significant (P value, 24 h, 0.001, 48 h, 0.003, 72 h, 0.004, 168 h, 0.005). In septic patients with respiratory system ≥ 3 points in SOFA in the eICU database, although the difference was not statistically significant (P value, 24 h, 0.148, 48 h, 0.178, 72 h, 0.132, 168 h, 0.790), SOFA without GCS (AUROC [95% CI], 24 h, 0.601 [0.576, 0.626], 48 h, 0.625 [0.601, 0.649], 72 h, 0.639 [0.615, 0.663], 168 h, 0.653 [0.629, 0.677]) had a higher predictive efficiency on ICU mortality than SOFA with GCS (AUROC [95% CI], 24 h, 0.591 [0.566, 0.616], 48 h, 0.616 [0.592, 0.640], 72 h, 0.628 [0.604, 0.652], 168 h, 0.651 [0.627, 0.675]).

CONCLUSIONS

In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality.

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.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.