ArticleCritical care (London, England)2018
Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis.
Article in Critical care (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed, 141 citations in OpenAlex.
- Development and validation of novel sepsis subphenotypes using trajectories of vital signs.Intensive care medicine · 2022Trial
- Establishment of a prognostic model for patients with sepsis based on SOFA: a retrospective cohort study.The Journal of international medical research · 2021Trial
- A new murine gram-negative sepsis model with standard care satisfies Sepsis-3 and reproduces clinical pathology.Intensive care medicine experimental · 2026Article
- Cardiovascular and Autonomic Phenotypes Reveal Distinct Mechanisms of Sepsis Decompensation via Deep Learning.Research square · 2026Article
- Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis.Intensive care medicine experimental · 2026Article
- Clinical subphenotypes of sepsis based on mixed continuous and categorical data and differences in treatment effects: a cluster analysis of multicenter observational studies.Critical care (London, England) · 2026Observational
- Temperature Trajectories Are Associated With the Prognosis of Septic Patients.Emergency medicine international · 2026Article
- The immunology of sepsis: translating new insights into clinical practice.Nature reviews. Nephrology · 2026Review
- Article
- Clinical subphenotypes and molecular endotypes in sepsis: toward an integrated and dynamic framework.Annals of intensive care · 2026Review
- Longitudinal sepsis-induced coagulopathy score trajectories and mortality in relation to early prophylactic heparin: a multicentre cohort study.Frontiers in cellular and infection microbiology · 2026Article
- [Inflammatory phenotypes in sepsis and the role of vitamin C on the corresponding phenotypes].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Hemodynamic subphenotypes in septic shock and their different responses to fluid balance in relation to ICU mortality.BMC anesthesiology · 2025Article
- Article
- Subphenotyping sepsis based on organ interaction trajectory using a deep temporal graph clustering model: a retrospective cohort study.EClinicalMedicine · 2025Article
- Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.Infection · 2025Article
- Syndromic Analysis of Sepsis Cohorts Using Large Language Models.JAMA network open · 2025Article
- Lactate-to-albumin ratio as an independent predictor of mortality in critically ill patients with gastrointestinal bleeding: a retrospective cohort study.BMC gastroenterology · 2025Article
- The 15th Anniversary ofLife (Basel, Switzerland) · 2025Review
- Is "pre-sepsis" the new sepsis? A narrative review.PLoS pathogens · 2025Review
30 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
objectiveSepsis is a heterogeneous disease and identification of its subclasses may facilitate and optimize clinical management. This study aimed to identify subclasses of sepsis and its responses to different amounts of fluid resuscitation.
methodsThis was a retrospective study conducted in an intensive care unit at a large tertiary care hospital. The patients fulfilling the diagnostic criteria of sepsis from June 1, 2001 to October 31, 2012 were included. Clinical and laboratory variables were used to perform the latent profile analysis (LPA). A multivariable logistic regression model was used to explore the independent association of fluid input and mortality outcome.
resultsIn total, 14,993 patients were included in the study. The LPA identified four subclasses of sepsis: profile 1 was characterized by the lowest mortality rate and having the largest proportion and was considered the baseline type; profile 2 was characterized by respiratory dysfunction; profile 3 was characterized by multiple organ dysfunction (kidney, coagulation, liver, and shock), and profile 4 was characterized by neurological dysfunction. Profile 3 showed the highest mortality rate (45.4%), followed by profile 4 (27.4%), 2 (18.2%), and 1 (16.9%). Overall, the amount of fluid needed for resuscitation was the largest on day 1 (median 5115 mL, interquartile range (IQR) 2662 to 8800 mL) and decreased rapidly on day 2 (median 2140 mL, IQR 900 to 3872 mL). Higher cumulative fluid input in the first 48 h was associated with reduced risk of hospital mortality for profile 3 (odds ratio (OR) 0.89, 95% CI 0.83 to 0.95 for each 1000 mL increase in fluid input) and with increased risk of death for profile 4 (OR 1.20, 95% CI 1.11 to 1.30).
conclusionThe study identified four subphenotypes of sepsis, which showed different mortality outcomes and responses to fluid resuscitation. Prospective trials are needed to validate our findings.
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