Septic Shock: Phenotypes and Outcomes

Cereuil, Alexandre; Ronflé, Romain; Culver, Aurélien; Papazian, Laurent; Lefèbvre, Laurent; Léone, Marc

doi:10.1007/s12325-022-02280-5

Cited by 6 publications

(4 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arterial hypertension (n=1123; 48.2%) and obesity (n= 942;40.4%) were the most frequently comorbid conditions reported. The severity of illness was intermediate according to the APACHE II (12 [9-16]) and SOFA (4 [3][4][5][6]) scores. PaO2/FiO2 ratio on the day of ICU admission was 124.8 (99-145) and 915 (39.3%) patients required high ow nasal cannula (HFNC).…”

Section: Resultsmentioning

confidence: 99%

“…Posted Date: February 24th, 2023 DOI: https://doi.org/10.21203/rs.3.rs-2598565/v1 License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Introduction Phenotypes have been described as a way to characterize patients heterogeneous in different diseases (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). Patients with sepsis are amongst those patients with a very variable presentation including comorbid conditions, age, and in ammatory response (1)(2)(3).…”

mentioning

confidence: 99%

“…Read Full License Introduction Phenotypes have been described as a way to characterize patients heterogeneous in different diseases (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). Patients with sepsis are amongst those patients with a very variable presentation including comorbid conditions, age, and in ammatory response (1)(2)(3). More recently, this approach has been also introduced and tested in COVID-19 patients (4)(5)(6)(7)(8)(9)(10)(11)(12)(13).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Reliability and reproducibility of clinical phenotypes developed during the first wave of COVID-19: A validation study in critically ill patients from the second and third wave

Rodríguez¹,

Gómez

Franquet

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: During the first wave of the COVID-19 pandemic, different clinical phenotypes were published. However, none of them have been validated in subsequent waves, so their current validity is unknown. The aim of the study is to validate the unsupervised cluster model developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. Methods: Retrospective, multicentre, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 74 Intensive Care Units (ICU) in Spain. To validate our original phenotypes model, we assigned a phenotype to each patient of the validation cohort using the same medoids, the same number of clusters (n= 3), the same number of variables (n= 25) and the same discretisation used in the development cohort. The performance of the classification was determined by Silhouette analysis and general linear modelling. The prognostic models were validated, and their performance was measured using accuracy test and area under curve (AUC)ROC. Results: The database included a total of 2,033 patients (mean age 63[53-92] years, 1643(70.5%) male, median APACHE II score (12[9-16]) and SOFA score (4[3-6]) points. The ICU mortality rate was 27.2%. Although the application of unsupervised cluster analysis classified patients in the validation population into 3 clinical phenotypes. Phenotype A (n=1,206 patients, 59.3%), phenotype B (n=618 patients, 30.4%) and phenotype C (n=506 patients, 24.3%), the characteristics of patients within each phenotype were significantly different from the original population. Furthermore, the silhouette coefficients were close to or below zero and the inclusion of phenotype classification in a regression model did not improve the model performance (accuracy =0.78, AUC=0.78) with respect to a standard model (accuracy = 0.79, AUC=0.79) or even worsened when the model was applied to patients within each phenotype (accuracy = 0.80, AUC 0.77 for Phenotype A, accuracy=0.73, AUC= 0.67 for phenotype B and accuracy= 0.66 , AUC= 0.76 for phenotype C ) Conclusion: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation. Trial Registration: The study was retrospectively registered (NCT 04948242) on June 30, 2021

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Reliability and reproducibility of clinical phenotypes developed during the first wave of COVID-19: A validation study in critically ill patients from the second and third wave

Rodríguez¹,

Gómez

Franquet

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Specific cardiovascular subphenotypes are identified by echocardiography in both sepsis (201) and acute respiratory distress syndrome (202). Different subphenotypes of septic patients have been characterized based on several approaches, including baseline characteristics, hemodynamics, genomics, RNA sequencing, and evolution of vital signs (203)(204)(205)(206)(207)(208)(209)(210)(211). These subphenotypes are associated with varying risks of poor outcomes but may also affect response to therapy.…”

Section: B4: For Patients With Septic Shock Receiving Norepinephrine ...mentioning

confidence: 99%

Surviving Sepsis Campaign Research Priorities 2023

De Backer,

Deutschman,

Hellman

et al. 2024

Critical Care Medicine

View full text Add to dashboard Cite

OBJECTIVES: To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock. DESIGN: Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities. METHODS: Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article. RESULTS: A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis? CONCLUSIONS: Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

show abstract