Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome

The combination of kidney function variables with cell cycle arrest biomarkers identifies distinct subphenotypes of sepsis-associated acute kidney injury: a post-hoc analysis (the PHENAKI study)

BACKGROUND

The severity and course of sepsis-associated acute kidney injury (SA-AKI) are correlated with the mortality rate. Early detection of SA-AKI subphenotypes might facilitate the rapid provision of individualized care.

PATIENTS AND METHODS

In this post-hoc analysis of a multicenter prospective study, we combined conventional kidney function variables with serial measurements of urine (tissue inhibitor of metalloproteinase-2 [TIMP-2])* (insulin-like growth factor-binding protein [IGFBP7]) at 0, 6, 12, and 24 h) and then using an unsupervised hierarchical clustering of principal components (HCPC) approach to identify different phenotypes of SA-AKI. We then compared the subphenotypes with regard to a composite outcome of in-hospital death or the initiation of renal replacement therapy (RRT).

RESULTS

We included 184 patients presenting SA-AKI within 6 h of the initiation of catecholamines. Three distinct subphenotypes were identified: subphenotype A (99 patients) was characterized by a normal urine output (UO), a low SCr and a low [TIMP-2]*[IGFBP7] level; subphenotype B (74 patients) was characterized by existing chronic kidney disease (CKD), a higher SCr, a low UO, and an intermediate [TIMP-2]*[IGFBP7] level; and subphenotype C was characterized by very low UO, a very high [TIMP-2]*[IGFBP7] level, and an intermediate SCr level. With subphenotype A as the reference, the adjusted hazard ratio (aHR) [95%CI] for the composite outcome was 3.77 [1.92-7.42] (p < 0.001) for subphenotype B and 4.80 [1.67-13.82] (p = 0.004) for subphenotype C.

CONCLUSIONS

Combining conventional kidney function variables with urine measurements of [TIMP-2]*[IGFBP7] might help to identify distinct SA-AKI subphenotypes with different short-term courses and survival rates.

Hospital-Onset Sepsis Warrants Expanded Investigation and Consideration as a Unique Clinical Entity

Sepsis causes more than a quarter million deaths among hospitalized adults in the United States each year. Although most cases of sepsis are present on admission, up to one-quarter of patients with sepsis develop this highly morbid and mortal condition while hospitalized. Compared with patients with community-onset sepsis (COS), patients with hospital-onset sepsis (HOS) are twice as likely to require mechanical ventilation and ICU admission, have more than two times longer ICU and hospital length of stay, accrue five times higher hospital costs, and are twice as likely to die. Patients with HOS differ from those with COS with respect to underlying comorbidities, admitting diagnosis, clinical manifestations of infection, and severity of illness. Despite the differences between these patient populations, patients with HOS sepsis are understudied and warrant expanded investigation. Here, we outline important knowledge gaps in the recognition and management of HOS in adults and propose associated research priorities for investigators. Of particular importance are questions regarding standardization of research and clinical case identification, understanding of clinical heterogeneity among patients with HOS, development of tailored management recommendations, identification of impactful prevention strategies, optimization of care delivery and quality metrics, identification and correction of disparities in care and outcomes, and how to ensure goal-concordant care for patients with HOS.

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.

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.

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.

EMERGENCE OF A TECHNOLOGY-DEPENDENT PHENOTYPE OF PEDIATRIC SEPSIS IN A LARGE CHILDREN'S HOSPITAL

ABSTRACT

Objective: To investigate whether pediatric sepsis phenotypes are stable in time. Methods: Retrospective cohort study examining children with suspected sepsis admitted to a Pediatric Intensive Care Unit at a large freestanding children's hospital during two distinct periods: 2010-2014 (early cohort) and 2018-2020 (late cohort). K-means consensus clustering was used to derive types separately in the cohorts. Variables included ensured representation of all organ systems. Results: One thousand ninety-one subjects were in the early cohort and 737 subjects in the late cohort. Clustering analysis yielded four phenotypes in the early cohort and five in the late cohort. Four types were in both: type A (34% of early cohort, 25% of late cohort), mild sepsis, with minimal organ dysfunction and low mortality; type B (25%, 22%), primary respiratory failure; type C (25%, 18%), liver dysfunction, coagulopathy, and higher measures of systemic inflammation; type D (16%, 17%), severe multiorgan dysfunction, with high degrees of cardiorespiratory support, renal dysfunction, and highest mortality. Type E was only detected in the late cohort (19%) and was notable for respiratory failure less severe than B or D, mild hypothermia, and high proportion of diagnoses and technological dependence associated with medical complexity. Despite low mortality, this type had the longest PICU length of stay. Conclusions: This single center study identified four pediatric sepsis phenotypes in an earlier epoch but five in a later epoch, with the new type having a large proportion of characteristics associated with medical complexity, particularly technology dependence. Personalized sepsis therapies need to account for this expanding patient population.

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

OBJECTIVES

To identify cytokine signature clusters in patients with septic shock.

DESIGN

Prospective observational cohort study.

SETTING

Single academic center in the United States.

PATIENTS

Adult (≥ 18 yr old) patients admitted to the medical ICU with septic shock requiring vasoactive medication support.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred fourteen patients with septic shock completed cytokine measurement at time of enrollment (t 1 ) and 24 hours later (t 2 ). Unsupervised random forest analysis of the change in cytokines over time, defined as delta (t 2 -t 1 ), identified three clusters with distinct cytokine profiles. Patients in cluster 1 had the lowest initial levels of circulating cytokines that decreased over time. Patients in cluster 2 and cluster 3 had higher initial levels that decreased over time in cluster 2 and increased in cluster 3. Patients in clusters 2 and 3 had higher mortality compared with cluster 1 (clusters 1-3: 11% vs 31%; odds ratio [OR], 3.56 [1.10-14.23] vs 54% OR, 9.23 [2.89-37.22]). Cluster 3 was independently associated with in-hospital mortality (hazard ratio, 5.24; p = 0.005) in multivariable analysis. There were no significant differences in initial clinical severity scoring or steroid use between the clusters. Analysis of either t 1 or t 2 cytokine measurements alone or in combination did not reveal clusters with clear clinical significance.

CONCLUSIONS

Longitudinal measurement of cytokine profiles at initiation of vasoactive medications and 24 hours later revealed three distinct cytokine signature clusters that correlated with clinical outcomes.

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.

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.

Sepsis-Induced myocardial dysfunction: heterogeneity of functional effects and clinical significance

Sepsis is a life-threatening disease state characterized by organ dysfunction and a dysregulated response to infection. The heart is one of the many organs affected by sepsis, in an entity termed sepsis-induced cardiomyopathy. This was initially used to describe a reversible depression in ejection fraction with ventricular dilation but advances in echocardiography and introduction of new techniques such as speckle tracking have led to descriptions of other common abnormalities in cardiac function associated with sepsis. This includes not only depression of systolic function, but also supranormal ejection fraction, diastolic dysfunction, and right ventricular dysfunction. These reports have led to inconsistent definitions of sepsis-induced cardiomyopathy. Just as there is heterogeneity among patients with sepsis, there is heterogeneity in the cardiac response; thus resuscitating these patients with a single approach is likely suboptimal. Many factors affect the heart in sepsis including inflammatory mediators, catecholamine responsiveness, and pathogen related toxins. This review will discuss different functional effects characterized by echocardiographic changes in sepsis and their prognostic and management implications.

Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup

Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients and is strongly associated with adverse outcomes, including an increased risk of chronic kidney disease, cardiovascular events and death. The pathophysiology of SA-AKI remains elusive, although microcirculatory dysfunction, cellular metabolic reprogramming and dysregulated inflammatory responses have been implicated in preclinical studies. SA-AKI is best defined as the occurrence of AKI within 7 days of sepsis onset (diagnosed according to Kidney Disease Improving Global Outcome criteria and Sepsis 3 criteria, respectively). Improving outcomes in SA-AKI is challenging, as patients can present with either clinical or subclinical AKI. Early identification of patients at risk of AKI, or at risk of progressing to severe and/or persistent AKI, is crucial to the timely initiation of adequate supportive measures, including limiting further insults to the kidney. Accordingly, the discovery of biomarkers associated with AKI that can aid in early diagnosis is an area of intensive investigation. Additionally, high-quality evidence on best-practice care of patients with AKI, sepsis and SA-AKI has continued to accrue. Although specific therapeutic options are limited, several clinical trials have evaluated the use of care bundles and extracorporeal techniques as potential therapeutic approaches. Here we provide graded recommendations for managing SA-AKI and highlight priorities for future research.

Prospective study and validation of early warning marker discovery based on integrating multi-omics analysis in severe burn patients with sepsis

Background

Early detection, timely diagnosis and rapid response are essential for case management and precautions of burn-associated sepsis. However, studies on indicators for early warning and intervention have rarely been conducted. This study was performed to better understand the pathophysiological changes and targets for prevention of severe burn injuries.

Methods

We conducted a multi-center, prospective multi-omics study, including genomics, microRNAomics, proteomics and single-cell transcriptomics, in 60 patients with severe burn injuries. A mouse model of severe burn injuries was also constructed to verify the early warning ability and therapeutic effects of potential markers.

Results

Through genomic analysis, we identified seven important susceptibility genes (DNAH11, LAMA2, ABCA2, ZFAND4, CEP290, MUC20 and ENTPD1) in patients with severe burn injuries complicated with sepsis. Through plasma miRNAomics studies, we identified four miRNAs (hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-451a and hsa-miR-423-5p) that may serve as early warning markers of burn-associated sepsis. A proteomic study indicated the changes in abundance of major proteins at different time points after severe burn injury and revealed the candidate early warning markers S100A8 and SERPINA10. In addition, the proteomic analysis indicated that neutrophils play an important role in the pathogenesis of severe burn injuries, as also supported by findings from single-cell transcriptome sequencing of neutrophils. Through further studies on severely burned mice, we determined that S100A8 is also a potential early therapeutic target for severe burn injuries, beyond being an early warning indicator.

Conclusions

Our multi-omics study identified seven susceptibility genes, four miRNAs and two proteins as early warning markers for severe burn-associated sepsis. In severe burn-associated sepsis, the protein S100A8 has both warning and therapeutic effects.

Development and validation of novel sepsis subphenotypes using trajectories of vital signs

PURPOSE

Sepsis is a heterogeneous syndrome and identification of sub-phenotypes is essential. This study used trajectories of vital signs to develop and validate sub-phenotypes and investigated the interaction of sub-phenotypes with treatment using randomized controlled trial data.

METHODS

All patients with suspected infection admitted to four academic hospitals in Emory Healthcare between 2014-2017 (training cohort) and 2018-2019 (validation cohort) were included. Group-based trajectory modeling was applied to vital signs from the first 8 h of hospitalization to develop and validate vitals trajectory sub-phenotypes. The associations between sub-phenotypes and outcomes were evaluated in patients with sepsis. The interaction between sub-phenotype and treatment with balanced crystalloids versus saline was tested in a secondary analysis of SMART (Isotonic Solutions and Major Adverse Renal Events Trial).

RESULTS

There were 12,473 patients with suspected infection in training and 8256 patients in validation cohorts, and 4 vitals trajectory sub-phenotypes were found. Group A (N = 3483, 28%) were hyperthermic, tachycardic, tachypneic, and hypotensive. Group B (N = 1578, 13%) were hyperthermic, tachycardic, tachypneic (not as pronounced as Group A) and hypertensive. Groups C (N = 4044, 32%) and D (N = 3368, 27%) had lower temperatures, heart rates, and respiratory rates, with Group C normotensive and Group D hypotensive. In the 6,919 patients with sepsis, Groups A and B were younger while Groups C and D were older. Group A had the lowest prevalence of congestive heart failure, hypertension, diabetes mellitus, and chronic kidney disease, while Group B had the highest prevalence. Groups A and D had the highest vasopressor use (p < 0.001 for all analyses above). In logistic regression, 30-day mortality was significantly higher in Groups A and D (p < 0.001 and p = 0.03, respectively). In the SMART trial, sub-phenotype significantly modified treatment effect (p = 0.03). Group D had significantly lower odds of mortality with balanced crystalloids compared to saline (odds ratio (OR) 0.39, 95% confidence interval (CI) 0.23-0.67, p < 0.001).

CONCLUSION

Sepsis sub-phenotypes based on vital sign trajectory were consistent across cohorts, had distinct outcomes, and different responses to treatment with balanced crystalloids versus saline.

Early Biomarker Signatures in Surgical Sepsis

INTRODUCTION

Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients.

METHODS

This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naïve Bayes classifier predicted cluster labels in a validation cohort.

RESULTS

The development cohort contained one cluster characterized by early organ dysfunction (cluster I, n = 18) and one cluster characterized by recovery (cluster II, n = 139). Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II (30 versus 16, P < 0.001) and SOFA scores (13 versus 5, P < 0.001), greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock (78% versus 17%, P < 0.001). Cluster I had higher mortality within 14 d of sepsis onset (11% versus 1.5%, P = 0.001) and within 1 y (44% versus 20%, P = 0.032), and higher incidence of chronic critical illness (61% versus 30%, P = 0.001). The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters.

CONCLUSIONS

Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes.

Immune checkpoint inhibitors for the treatment of sepsis:insights from preclinical and clinical development

INTRODUCTION

Sepsis represents one-fifth of all deaths worldwide and is associated with huge costs. Regarding disease progression, it is now well established that sepsis induces a state of acquired immunosuppression, with an increased risk of secondary infections that contributes to patients' worsening. Thus, tackling sepsis-induced immunosuppression represents a promising perspective.

AREAS COVERED

Of mechanisms responsible for sepsis-induced immunosuppression, the increased expression of co-inhibitory receptors (aka immune checkpoint) such as PD-1, CTLA4, TIM-3, LAG-3 or BTLA and their ligands recently received considerable interest since their inhibition, thanks to the so-called checkpoint inhibitors (CPI), provided astonishing results in cancer by rebooting immune functions. This review reports on the first landmarks of these molecules in sepsis. We introduce them in terms of basic immunology in line with sepsis pathophysiology both in experimental models and observational works and assess the first human clinical studies.

EXPERT OPINION

Preclinical results are positive and the first human clinical trials, although currently limited to the early phase, showed a beneficial effect on immunological functions and/or markers and suggested that tolerance of CPIs side effects, mainly auto-immune disorders, is acceptable in sepsis. Elsewhere, in some specific infections leading to ICU admission (or occurring during ICU stay), such as fungal infections, preliminary convincing case reports have been published. Overall, the first results regarding CPIs in sepsis appear encouraging. However, further efforts are warranted, especially in defining the right patients to be treated (i.e., in an individualized approach) and establishing the optimal time to start an immune restoration. Larger trials are now mandatory to confirm CPIs' potential in sepsis.

Sepsis subphenotyping based on organ dysfunction trajectory

BACKGROUND

Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis.

METHODS

We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies.

RESULTS

A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening (n = 612, 13.1%), Delayed Worsening (n = 960, 20.5%), Rapidly Improving (n = 1932, 41.3%), and Delayed Improving (n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P-value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers.

CONCLUSIONS

Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.

A Standardized Multimodal Neurological Monitoring Protocol-Guided Cerebral Protection Therapy for Venoarterial Extracorporeal Membrane Oxygenation Supported Patients

Background

The main objective of this study was to investigate the role of a multimodal neurological monitoring (MNM)-guided protocol in the precision identification of neural impairment and long-term neurological outcomes in venoarterial extracorporeal membrane oxygenation (VA-ECMO) supported patients.

Methods

We performed a cohort study that examined adult patients who underwent VA-ECMO support in our center between February 2010 and April 2021. These patients were retrospectively assigned to the “with MNM group” and the “without MNM group” based on the presence or absence of MNM-guided precision management. The differences in ECMO-related characteristics, evaluation indicators (precision, sensitivity, and specificity) of the MNM-guided protocol, and the long-term outcomes of the surviving patients were measured and compared between the two groups.

Results

A total of 63 patients with VA-ECMO support were retrospectively assigned to the without MNM group (n = 35) and the with MNM group (n = 28). The incidence of neural impairment in the without MNM group was significantly higher than that in the with MNM group (82.1 vs. 54.3%, P = 0.020). The MNM group exhibited older median ages [52.5 (39.5, 65.3) vs. 31 (26.5, 48.0), P = 0.008], a higher success rate of ECMO weaning (92.8 vs. 71.4%, P = 0.047), and a lower median duration of building ECMO [40.0 (35.0, 52.0) vs. 58.0 (48.0, 76.0), P = 0.025] and median ECMO duration days [5.0 (4.0, 6.2) vs. 7.0 (5.0, 10.5), P = 0.018] than the group without MNM. The MNM-guided protocol exhibited a higher precision rate (82.1 vs. 60.0%), sensitivity (95.7 vs. 78.9%), and specificity (83.3 vs. 37.5%) in identifying neural impairment in VA-ECMO support patients. There were significant differences in the long-term outcomes of survivors at 1, 3 and 6 months after discharge between the two groups (P < 0.05). However, the results showed no significant differences in ICU length of stay (LOS), hospital LOS, survival to discharge, or 28-day mortality between the two groups (P > 0.05).

Conclusion

The MNM-guided protocol is conducive to guiding intensivists in the improvement of cerebral protection therapy for ECMO-supported patients to detect and treat potential neurologic impairment promptly, and then improving long-term neurological outcomes after discharge.

Multiple Organ Dysfunction in Older Major Trauma Critical Care Patients: A Multicenter Prospective Observational Study

The objective was to explore the characteristics and outcomes of multiple organ dysfunction syndrome (MODS) in older trauma patients.

Background

Severely injured older people present an increasing challenge for trauma systems. Recovery for those who require critical care may be complicated by MODS. In older trauma patients, MODS may not be predictable based on chronological age alone and factors associated with its development and resolution are unclear.

Methods

Consecutive adult patients (aged ≥16 years) admitted to 4 level 1 major trauma center critical care units were enrolled and reviewed daily until discharge or death. MODS was defined by a daily total sequential organ failure assessment score of >5.

Results

One thousand three hundred sixteen patients were enrolled over 18 months and one-third (434) were aged ≥65 years. Incidence of MODS was high for both age groups (<65 years: 64%, ≥65 years: 70%). There were few differences in severity, patterns, and duration of MODS between cohorts, except for older traumatic brain injury (TBI) patients who experienced a prolonged course of MODS recovery (TBI: 9 days vs no TBI: 5 days, P < 0.01). Frailty rather than chronological age had a strong association with MODS development (odds ratio [OR], 6.9; 95% confidence intervals [CI], 3.0-12.4; P < 0.001) and MODS mortality (OR, 2.1; 95% CI, 1.31-3.38; P = 0.02). Critical care resource utilization was not increased in older patients, but MODS had a substantial impact on mortality (<65 years: 17%; ≥65 years: 28%). The majority of older patients who did not develop MODS survived and had favorable discharge outcomes (home discharge ≥65 years NoMODS: 50% vs MODS: 15%; P < 0.01).

Conclusions

Frailty rather than chronological age appears to drive MODS development, recovery, and outcome in older cohorts. Early identification of frailty after trauma may help to predict MODS and plan care in older trauma.

Research in Pediatric Intensive Care

Many important clinical questions remain unanswered in the practice of pediatric intensive care due to the lack of high-quality evidence. Although challenges exist in conducting research in pediatric intensive care units, identification of research priorities, interdisciplinary collaborations, innovative trial designs, and the use of common datasets and outcome measures helps to bring new knowledge to our field. The topic of "Research in PICUs" is extremely broad; therefore, this review focuses on a few common themes receiving increased attention in the literature, including research agendas, core outcome sets, precision medicine, and novel clinical trial strategies.

Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated Lactate

Introduction

Targeted therapies for sepsis have failed to show benefit due to high variability among subjects. We sought to demonstrate different phenotypes of septic shock based solely on clinical features and show that these relate to outcome.

Methods

A retrospective analysis was performed of a 1,023-subject cohort with early septic shock from the ProCESS trial. Twenty-three clinical variables at baseline were analyzed using hierarchical clustering, with consensus clustering used to identify and validate the ideal number of clusters in a derivation cohort of 642 subjects from 20 hospitals. Clusters were visualized using heatmaps over 0, 6, 24, and 72 h. Clinical outcomes were 14-day all-cause mortality and organ failure pattern. Cluster robustness was confirmed in a validation cohort of 381 subjects from 11 hospitals.

Results

Five phenotypes were identified, each with unique organ failure patterns that persisted in time. By enrollment criteria, all patients had shock. The two high-risk phenotypes were characterized by distinct multi-organ failure patterns and cytokine signatures, with the highest mortality group characterized most notably by liver dysfunction and coagulopathy while the other group exhibited primarily respiratory failure, neurologic dysfunction, and renal dysfunction. The moderate risk phenotype was that of respiratory failure, while low-risk phenotypes did not have a high degree of additional organ failure.

Conclusions

Sepsis phenotypes with distinct biochemical abnormalities may be identified by clinical characteristics alone and likely provide an opportunity for early clinical actionability and prognosis.

Clinical outcome of nosocomial pneumonia caused by Carbapenem-resistant gram-negative bacteria in critically ill patients: a multicenter retrospective observational study

Nosocomial pneumonia caused by carbapenem-resistant gram-negative bacteria (CRGNB) is a growing threat due to the limited therapeutic choices and high mortality rate. The aim of this study was to evaluate the prognostic factors for mortality in patients with nosocomial pneumonia caused by CRGNB and the impact of colistin-based therapy on the outcomes of intensive care unit (ICU) patients. We conducted a retrospective study of the ICUs in five tertiary teaching hospitals in Taiwan. Patients with nosocomial pneumonia caused by CRGNB from January 2016 to December 2016 were included. Prognostic factors for mortality were analyzed using multivariate logistic regression. The influence of colistin-based therapy on mortality and clinical and microbiological outcomes were evaluated in subgroups using different severity stratification criteria. A total of 690 patients were enrolled in the study, with an in-hospital mortality of 46.1%. The most common CRGNB pathogens were Acinetobacter baumannii (78.7%) and Pseudomonas aeruginosa (13.0%). Significant predictors (odds ratio and 95% confidence interval) of mortality from multivariate analysis were a length of hospital stay (LOS) prior to pneumonia of longer than 9 days (2.18, 1.53-3.10), a sequential organ failure assessment (SOFA) score of more than 7 (2.36, 1.65-3.37), supportive care with vasopressor therapy (3.21, 2.26-4.56), and escalation of antimicrobial therapy (0.71, 0.50-0.99). There were no significant differences between the colistin-based therapy in the deceased and survival groups (42.1% vs. 42.7%, p = 0.873). In the subgroup analysis, patients with multiple organ involvement (> 2 organs) or higher SOFA score (> 7) receiving colistin-based therapy had better survival outcomes. Prolonged LOS prior to pneumonia onset, high SOFA score, vasopressor requirement, and timely escalation of antimicrobial therapy were predictors for mortality in critically ill patients with nosocomial CRGNB pneumonia. Colistin-based therapy was associated with better survival outcomes in subgroups of patients with a SOFA score of more than 7 and multiple organ involvement.

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

BACKGROUND

Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions.

METHODS

We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-induced Multiple organ failure Study (PHENOMS) dataset of 404 children. 24-hour computable phenotypes are derived using 25 available bedside variables including C-reactive protein and ferritin.

RESULTS

Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to all other phenotypes, PedSep-A patients (n = 135; 2% mortality) were younger and previously healthy, with the lowest C-reactive protein and ferritin levels, the highest lymphocyte and platelet counts, highest heart rate, and lowest creatinine (p < 0.05); PedSep-B patients (n = 102; 12% mortality) were most likely to be intubated and had the lowest Glasgow Coma Scale Score (p < 0.05); PedSep-C patients (n = 110; mortality 10%) had the highest temperature and Glasgow Coma Scale Score, least pulmonary failure, and lowest lymphocyte counts (p < 0.05); and PedSep-D patients (n = 56, 34% mortality) had the highest creatinine and number of organ failures, including renal, hepatic, and hematologic organ failure, with the lowest platelet counts (p < 0.05). PedSep-D had the highest likelihood of developing thrombocytopenia-associated multiple organ failure (Adj OR 47.51 95% CI [18.83-136.83], p < 0.0001) and macrophage activation syndrome (Adj OR 38.63 95% CI [13.26-137.75], p < 0.0001).

CONCLUSIONS

Four computable phenotypes are derived, with PedSep-D being optimal for enrollment in early personalized anti-inflammatory trials targeting thrombocytopenia-associated multiple organ failure and macrophage activation syndrome in pediatric sepsis. A computer tool for identification of individual patient membership ( www.pedsepsis.pitt.edu ) is provided. Reproducibility will be assessed at completion of two ongoing pediatric sepsis studies.

Identification of the robust predictor for sepsis based on clustering analysis

Sepsis is a life-threatening disorder with high incidence and mortality rate. However, the early detection of sepsis is challenging due to lack of specific marker and various etiology. This study aimed to identify robust risk factors for sepsis via cluster analysis. The integrative task of the automatic platform (i.e., electronic medical record) and the expert domain was performed to compile clinical and medical information for 2,490 sepsis patients and 16,916 health check-up participants. The subjects were categorized into 3 and 4 groups based on seven clinical and laboratory markers (Age, WBC, NLR, Hb, PLT, DNI, and MPXI) by K-means clustering. Logistic regression model was performed for all subjects including healthy control and sepsis patients, and cluster-specific cases, separately, to identify sepsis-related features. White blood cell (WBC), well-known parameter for sepsis, exhibited the insignificant association with the sepsis status in old age clusters (K3C3 and K4C3). Besides, NLR and DNI were the robust predictors in all subjects as well as three or four cluster-specific subjects including K3C3 or K4C3. We implemented the cluster-analysis for real-world hospital data to identify the robust predictors for sepsis, which could contribute to screen likely overlooked and potential sepsis patients (e.g., sepsis patients without WBC count elevation).

Cluster analysis integrating age and body temperature for mortality in patients with sepsis: a multicenter retrospective study

It is not clear whether mortality is associated with body temperature (BT) in older sepsis patients. This study aimed to evaluate the mortality rates in sepsis patients according to age and BT and identify the risk factors for mortality. We investigated the clusters using a machine learning method based on a combination of age and BT, and identified the mortality rates according to these clusters. This retrospective multicenter study was conducted at five hospitals in Korea. Data of sepsis patients aged ≥ 18 years who were admitted to the intensive care unit between January 1, 2011 and April 30, 2021 were collected. BT was divided into three groups (hypothermia < 36 °C, normothermia 36‒38 °C, and hyperthermia > 38 °C), and age groups were divided using a 75-year age threshold. Kaplan‒Meier analysis was performed to assess the cumulative mortality over 90 days. A K-means clustering algorithm using age and BT was used to characterize phenotypes. During the study period, 15,574 sepsis patients were enrolled. Overall, 90-day mortality was 20.5%. Kaplan‒Meier survival analyses demonstrated that 90-day mortality rates were 27.4%, 19.6%, and 11.9% in the hypothermia, normothermia, and hyperthermia groups, respectively, in those ≥ 75 years old (Log-rank p < 0.001). Cluster analysis demonstrated three groups: Cluster A (relatively older age and lower BT), Cluster B (relatively younger age and wide range of BT), and Cluster C (relatively higher BT than Cluster A). Kaplan‒Meier curve analysis showed that the 90-day mortality rates of Cluster A was significantly higher than those of Clusters B and C (24.2%, 17.1%, and 17.0%, respectively; Log-rank p < 0.001). The 90-day mortality rate correlated inversely with BT groups among sepsis patients in either age group (< 75 and ≥ 75 years). Clustering analysis revealed that the mortality rate was higher in the cluster of patients with relatively older age and lower BT.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Revising Host Phenotypes of Sepsis Using Microbiology

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

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

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

Clinical and biological clusters of sepsis patients using hierarchical clustering

Background

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

Methods

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

Results

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

Conclusions

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

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

Hemodynamic clinical phenotyping in septic shock

PURPOSE OF REVIEW

Recent studies have failed to show significant benefit from a uniform strategy, suggesting that hemodynamic management must be individually adapted in septic shock depending on different phenotypes. Different approaches that may be used to this end will be discussed.

RECENT FINDINGS

Fluid management is a cornerstone of resuscitation, as the positive fluid balance has been associated with higher mortality and right ventricular failure. Myocardial evaluation is mandatory, as sepsis patients may present with a hyperkinetic state, left ventricular (systolic and diastolic) and/or right ventricular dysfunction, the latter being associated with higher mortality. Statistical approaches with the identification of hemodynamic clusters based on echocardiographic and clinical parameters might be integrated into daily practice to develop precision medicine. Such approaches may also predict the progression of septic shock.

SUMMARY

Different hemodynamic phenotypes can occur at any stage of sepsis and be associated with one another. The clinician must regularly assess dynamic changes in phenotypes in septic shock patients. Statistical approaches based on machine learning need to be validated by prospective studies.

The Prognostic Value of Brain Dysfunction in Critically Ill Patients with and without Sepsis: A Post Hoc Analysis of the ICON Audit

Brain dysfunction is associated with poor outcome in critically ill patients. In a post hoc analysis of the Intensive Care over Nations (ICON) database, we investigated the effect of brain dysfunction on hospital mortality in critically ill patients. Brain failure was defined as a neurological sequential organ failure assessment (nSOFA) score of 3–4, based on the assumed Glasgow Coma Scale (GCS) score. Multivariable analyses were performed to assess the independent roles of nSOFA and change in nSOFA from admission to day 3 (ΔnSOFA) for predicting hospital mortality. Data from 7192 (2096 septic and 5096 non-septic) patients were analyzed. Septic patients were more likely than non-septic patients to have brain failure on admission (434/2095 (21%) vs. 617/4665 (13%), p < 0.001) and during the ICU stay (625/2063 (30%) vs. 736/4665 (16%), p < 0.001). The presence of sepsis (RR 1.66 (1.31–2.09)), brain failure (RR 4.85 (3.33–7.07)), and both together (RR 5.61 (3.93–8.00)) were associated with an increased risk of in-hospital death, but nSOFA was not. In the 3280 (46%) patients in whom ΔnSOFA was available, sepsis (RR 2.42 (1.62–3.60)), brain function deterioration (RR 6.97 (3.71–13.08)), and the two together (RR 10.24 (5.93–17.67)) were associated with an increased risk of in-hospital death, whereas improvement in brain function was not.

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

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

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

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

Monitoring immunomodulation in patients with sepsis

Introduction: This review aims to summarize current progress of the last ten years in the development of biomarkers used for classifying the immune response of the septic host and for monitoring the efficacy of the applied adjunctive immunotherapy.Areas covered: An extensive search of the literature was performed. In this review the authors discuss available biomarkers of host immune response in sepsis toward two directions; immunosuppression and hyperinflammation. Ferritin, sCD163, sIL-2 ra, and IL-18 may help in the diagnosis of macrophage activation syndrome (MAS) complicating sepsis whereas lymphopenia, decreased HLA-DR expression on monocytes, overexpression of Programmed cell death protein-1 (PD-1)/Programmed death-ligand 1 (PD-L1) and IL-10 are indicators of sepsis-induced immunosuppression. Novel approaches in the classification of immune state in sepsis include Myeloid-Derived Suppressor Cells (MDSC) and specific endotypes, defined by gene expression and molecular techniques.Expert opinion: HLA-DR and ferritin are the most commonly used biomarkers to monitor immunomodulation in clinical practice whereas developing specific sepsis endotypes is the future target. New immunotherapy trials in sepsis need to incorporate biomarkers for a personalized treatment.

Approaches to Addressing Post-Intensive Care Syndrome among Intensive Care Unit Survivors. A Narrative Review

Critical illness can be lethal and devastating to survivors. Improvements in acute care have increased the number of intensive care unit (ICU) survivors. These survivors confront a range of new or worsened health states that collectively are commonly denominated post-intensive care syndrome (PICS). These problems include physical, cognitive, psychological, and existential aspects, among others. Burgeoning interest in improving long-term outcomes for ICU survivors has driven an array of potential interventions to improve outcomes associated with PICS. To date, the most promising interventions appear to relate to very early physical rehabilitation. Late interventions within aftercare and recovery clinics have yielded mixed results, although experience in heart failure programs suggests the possibility that very early case management interventions may help improve intermediate-term outcomes, including mortality and hospital readmission. Predictive models have tended to underperform, complicating study design and clinical referral. The complexity of the health states associated with PICS suggests that careful and rigorous evaluation of multidisciplinary, multimodality interventions-tied to the specific conditions of interest-will be required to address these important problems.

Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children

IMPORTANCE

Multiple organ dysfunction syndrome (MODS) is a dynamic and heterogeneous process associated with high morbidity and mortality in critically ill children.

OBJECTIVE

To determine whether data-driven phenotypes of MODS based on the trajectories of 6 organ dysfunctions have prognostic and therapeutic relevance in critically ill children.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 20 827 pediatric intensive care encounters among 14 285 children admitted to 2 large academic pediatric intensive care units (PICUs) between January 2010 and August 2016. Patients were excluded if they were older than 21 years or had undergone cardiac surgery. The 6 subscores of the pediatric Sequential Organ Failure Assessment (pSOFA) score were calculated for the first 3 days, including the subscores for respiratory, cardiovascular, coagulation, hepatic, neurologic, and renal dysfunctions. MODS was defined as a pSOFA subscore of at least 2 in at least 2 organs. Encounters were split in a 80:20 ratio for derivation and validation, respectively. The trajectories of the 6 subscores were used to derive a set of data-driven phenotypes of MODS using subgraph-augmented nonnegative matrix factorization in the derivation set. Data analysis was conducted from March to October 2019.

EXPOSURES

The primary exposure was phenotype membership. In the subset of patients with vasoactive-dependent shock, the interaction between hydrocortisone and phenotype membership and its association with outcomes were examined in a matched cohort.

MAIN OUTCOMES AND MEASURES

The primary outcome was in-hospital mortality. Secondary outcomes included persistent MODS on day 7, and vasoactive-free, ventilator-free, and hospital-free days. Regression analysis was used to adjust for age, severity of illness, immunocompromised status, and study site.

RESULTS

There were 14 285 patients with 20 827 encounters (median [interquartile range] age 5.2 years [1.5-12.7] years; 11 409 [54.8%; 95% CI, 54.1%-55.5%] male patients). Of these, 5297 encounters (25.4%; 95% CI, 24.8%-26.0%) were with patients who had MODS, of which 5054 (95.4%) met the subgraph count threshold and were included in the analysis. Subgraph augmented nonnegative matrix factorization uncovered 4 data-driven phenotypes of MODS, characterized by a combination of neurologic, respiratory, coagulation, and cardiovascular dysfunction, as follows: phenotype 1, severe, persistent encephalopathy (1019 patients [19.2%]); phenotype 2, moderate, resolving hypoxemia (1828 patients [34.5%]); phenotype 3, severe, persistent hypoxemia and shock (1012 patients [19.1%]); and phenotype 4, moderate, persistent thrombocytopenia and shock (1195 patients [22.6%]). These phenotypes were reproducible in a validation set of encounters, had distinct clinical characteristics, and were independently associated with outcomes. For example, using phenotype 2 as reference, the adjusted hazard ratios (aHRs) for death by 28 days were as follows: phenotype 1, aHR of 3.0 (IQR, 2.1-4.3); phenotype 3, aHR of 2.8 (IQR, 2.0-4.1); and phenotype 4, aHR of 1.8 (IQR, 1.2-2.6). Interaction analysis in a matched cohort of patients with vasoactive-dependent shock revealed that hydrocortisone had differential treatment association with vasoactive-free days across phenotypes. For example, patients in phenotype 3 who received hydrocortisone had more vasoactive-free days than those who did not (23 days vs 18 days; P for interaction < .001), whereas patients in other phenotypes who received hydrocortisone either had no difference or had less vasoactive-free days.

CONCLUSIONS AND RELEVANCE

In this study, data-driven phenotyping in critically ill children with MODS uncovered 4 distinct and reproducible phenotypes with prognostic relevance and possible therapeutic relevance. Further validation and characterization of these phenotypes is warranted.

Population enrichment for critical care trials: phenotypes and differential outcomes

PURPOSE OF REVIEW

Sepsis and acute respiratory distress syndrome (ARDS) are two heterogenous acute illnesses where numerous RCTs have indeterminate results. We present a narrative review on the recent developments in enriching patient populations for future sepsis and ARDS trials.

RECENT FINDINGS

Many researchers are actively pursuing enrichment strategies to reduce heterogeneity to increase the sensitivity of future trials. Enrichment refers to the use of measurable patient characteristics, known before randomisation, to refine trial populations. Biomarkers could increase the diagnostic certainty of sepsis, whereas chest radiology training to enhance reliability of interpretation and stabilisation period of mechanical ventilation have been considered to increase the diagnostic certainty of ARDS. Clinical and biomarker data analyses identifies four to six sepsis clinical phenotypes and two ARDS clinical phenotypes. Similarly, leukocyte gene expression data identifies two to four sepsis molecular phenotypes. Use of a test-dose identifies ARDS subpopulations who are likely to benefit from higher PEEP. Early-phase trials report how a biomarker that is altered by the intervention, such as lymphocyte count for recombinant interleukin-7 therapy and higher check point inhibitor expression for anti-check point treatments in sepsis, could identify a higher treatment effect population for future trials.

SUMMARY

Enrichment reduces heterogeneity and will enhance the sensitivity of future trials. However, enrichment, even when it identifies more homogenous populations, may not be efficient to deploy in trials or clinical practice.

Biomarker Panels in Critical Care

Biomarker panels have the potential to advance the field of critical care medicine by stratifying patients according to prognosis and/or underlying pathophysiology. This article discusses the discovery and validation of biomarker panels, along with their translation to the clinical setting. The current literature on the use of biomarker panels in sepsis, acute respiratory distress syndrome, and acute kidney injury is reviewed.

On classifying sepsis heterogeneity in the ICU: insight using machine learning

OBJECTIVES

Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data.

MATERIALS AND METHODS

Using an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguish patients who develop sepsis in the ICU from those who do not.

RESULTS

The classification results show that features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients regardless of the classification model used. The improved performance is especially pronounced in specificity, which is a current bottleneck in sepsis prediction machine learning models.

CONCLUSION

Our findings can steer machine learning efforts toward more personalized models for complex conditions including sepsis.

Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children

Multiple organ dysfunction syndrome (MODS) is one of the most common causes of death in critically ill children. However, despite decades of clinical trials, there are no comprehensive approaches to the management of MODS or effective targeted therapies that have consistently improved outcomes. Better understanding the heterogeneity of MODS and characterizing subgroups of MODS patients could improve our understanding of the syndrome and help us develop new management strategies. We analyzed a cohort of 5,297 children with MODS from two children's hospitals and used subgraph-augmented non-negative matrix factorization (SANMF) to identify unique temporal patterns in organ dysfunction across four novel subgroups. We demonstrate that these subgroups are composed of patients with distinct clinical characteristics and are independently predictive of clinical outcomes. Our work suggests that these subgroups represent four relevant phenotypes of pediatric MODS that could be used to identify novel management strategies.

A boosting inspired personalized threshold method for sepsis screening

Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.

A Multicenter Network Assessment of Three Inflammation Phenotypes in Pediatric Sepsis-Induced Multiple Organ Failure

OBJECTIVES

Ongoing adult sepsis clinical trials are assessing therapies that target three inflammation phenotypes including 1) immunoparalysis associated, 2) thrombotic microangiopathy driven thrombocytopenia associated, and 3) sequential liver failure associated multiple organ failure. These three phenotypes have not been assessed in the pediatric multicenter setting. We tested the hypothesis that these phenotypes are associated with increased macrophage activation syndrome and mortality in pediatric sepsis.

DESIGN

Prospective severe sepsis cohort study comparing children with multiple organ failure and any of these phenotypes to children with multiple organ failure without these phenotypes and children with single organ failure.

SETTING

Nine PICUs in the Eunice Kennedy Shriver National Institutes of Child Health and Human Development Collaborative Pediatric Critical Care Research Network.

PATIENTS

Children with severe sepsis and indwelling arterial or central venous catheters.

INTERVENTIONS

Clinical data collection and twice weekly blood sampling until PICU day 28 or discharge.

MEASUREMENTS AND MAIN RESULTS

Of 401 severe sepsis cases enrolled, 112 (28%) developed single organ failure (0% macrophage activation syndrome 0/112; < 1% mortality 1/112), whereas 289 (72%) developed multiple organ failure (9% macrophage activation syndrome 24/289; 15% mortality 43/289). Overall mortality was higher in children with multiple organ and the phenotypes (24/101 vs 20/300; relative risk, 3.56; 95% CI, 2.06-6.17). Compared to the 188 multiple organ failure patients without these inflammation phenotypes, the 101 multiple organ failure patients with these phenotypes had both increased macrophage activation syndrome (19% vs 3%; relative risk, 7.07; 95% CI, 2.72-18.38) and mortality (24% vs 10%; relative risk, 2.35; 95% CI, 1.35-4.08).

CONCLUSIONS

These three inflammation phenotypes were associated with increased macrophage activation syndrome and mortality in pediatric sepsis-induced multiple organ failure. This study provides an impetus and essential baseline data for planning multicenter clinical trials targeting these inflammation phenotypes in children.

Multiple organ dysfunction after trauma

Background

The nature of multiple organ dysfunction syndrome (MODS) after traumatic injury is evolving as resuscitation practices advance and more patients survive their injuries to reach critical care. The aim of this study was to characterize contemporary MODS subtypes in trauma critical care at a population level.

Methods

Adult patients admitted to major trauma centre critical care units were enrolled in this 4‐week point‐prevalence study. MODS was defined by a daily total Sequential Organ Failure Assessment (SOFA) score of more than 5. Hierarchical clustering of SOFA scores over time was used to identify MODS subtypes.

Results

Some 440 patients were enrolled, of whom 245 (55·7 per cent) developed MODS. MODS carried a high mortality rate (22·0 per cent versus 0·5 per cent in those without MODS; P < 0·001) and 24·0 per cent of deaths occurred within the first 48 h after injury. Three patterns of MODS were identified, all present on admission. Cluster 1 MODS resolved early with a median time to recovery of 4 days and a mortality rate of 14·4 per cent. Cluster 2 had a delayed recovery (median 13 days) and a mortality rate of 35 per cent. Cluster 3 had a prolonged recovery (median 25 days) and high associated mortality rate of 46 per cent. Multivariable analysis revealed distinct clinical associations for each form of MODS; 24‐hour crystalloid administration was associated strongly with cluster 1 (P = 0·009), traumatic brain injury with cluster 2 (P = 0·002) and admission shock severity with cluster 3 (P = 0·003).

Conclusion

Contemporary MODS has at least three distinct types based on patterns of severity and recovery. Further characterization of MODS subtypes and their underlying pathophysiology may lead to future opportunities for early stratification and targeted interventions.

Rapid lipidomics analysis for sepsis-induced liver injury in rats and insights into lipid metabolic pathways using ultra-performance liquid chromatography/mass spectrometry

Lipidomics has been applied in the identification and quantification of molecular lipids within an organism, and to provide insights into mechanisms in clinical medicine. Sepsis is a major systemic inflammatory syndrome and the liver here is a potential target organ for dysfunctional response. However, the study of alterations in global lipid profiles associated with sepsis-induced liver injury is still limited. In this work, we set out to determine alterations of lipidomics profiles in a rat model of sepsis-induced liver injury using an untargeted lipidomics strategy. Liquid chromatography coupled with mass spectrometry in conjunction with multivariate data analysis and pathway analysis were used to acquire a global lipid metabolite profile. Meanwhile, biochemistry index and histopathological examinations of the liver were performed to obtain auxiliary measurements for determining the pathological changes associated with sepsis-induced liver injury. Eleven lipid metabolites and two metabolic pathways were discovered and associated with sepsis-induced liver injury. The results indicated that various biomarkers and pathways may provide evidence for and insight into lipid profile alterations associated with sepsis-induced liver injury, and hence pointed to potential strategic targets for clinical diagnosis and therapy in the future.

Lipidomics has been applied in the identification and quantification of molecular lipids within an organism, and to provide insights into mechanisms in clinical medicine.

Cost-effectiveness of second-line vasopressors for the treatment of septic shock

PURPOSE

To determine the cost-effectiveness of escalating doses of norepinephrine or norepinephrine plus the adjunctive use of vasopressin or angiotensin II as a second-line vasopressor for septic shock.

MATERIALS AND METHODS

Decision tree analysis was performed to compare costs and outcomes associated with norepinephrine monotherapy or the two adjunctive second-line vasopressors. Short- and long-term outcomes modeled included ICU survival and lifetime quality-adjusted-life-years (QALY) gained. Costs were modeled from a payer's perspective, with a willingness-to-pay threshold set at $100,000/unit gained. One-way (tornado diagrams) and probabilistic sensitivity analyses were performed.

RESULTS

Adjunctive vasopressin was the most cost-effective therapy, and dominated both norepinephrine monotherapy and adjunctive angiotensin II by producing higher ICU survival at less cost. For the lifetime horizon, while norepinephrine monotherapy was least expensive, adjunctive vasopressin was the most cost-effective with an incremental cost-effectiveness ratio of $19,762 / QALY gained. Although adjunctive angiotensin II produced more QALYs compared to norepinephrine monotherapy, it was dominated in the long-term evaluation by second-line vasopressin. Sensitivity analyses demonstrated model robustness and medication costs were not significant drivers of model results.

CONCLUSIONS

Vasopressin is the most cost-effective second-line vasopressor in both the short- and long-term evaluations. Vasopressor price is a minor contributor to overall cost.