ArticleScientific reports2022
Cluster analysis integrating age and body temperature for mortality in patients with sepsis: a multicenter retrospective study.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fever on hospital admission is associated with increased disease severity and mortality in acute pancreatitis: a systematic review and meta-analysis.Systematic reviews · 2026Pooled it
- Unsupervised Clinical Phenotyping Identifies Distinct Risk Profiles in Incisional Hernia Repair.Medicina (Kaunas, Lithuania) · 2026Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- Age-Comorbidity Interactions and Clinical Outcomes in Septic Shock: An Emergency Department-Based Multicenter Cohort Study.Healthcare (Basel, Switzerland) · 2026Article
- The poikilothermic hypothesis of sepsis.Critical care (London, England) · 2026Review
- Identification and Characterization of Sepsis Phenotypes in an Indian Cohort.Critical care research and practice · 2026Article
- Article
- Hypothermia association with all-cause mortality in critically ill patients with sepsis based on the MIMIC-IV database.Scientific reports · 2025Article
- Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements.Mayo Clinic proceedings. Digital health · 2025Article
- Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- A Multivariate Phenotypical Approach of Sepsis and Septic Shock-A Comprehensive Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2024Review
- Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy.Scientific reports · 2024Article
- Review
- Plasma Insulin-like Growth Factor-Binding Protein-2 of Critically Ill Patients Is Related to Disease Severity and Survival.Biomedicines · 2023Article
- Article
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study.Critical care (London, England) · 2023Article
- K-means cluster analysis of characteristic patterns of allergen in different ages: Real life study.Clinical and translational allergy · 2023Article
- Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study.Research square · 2023Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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