Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation
BackgroundBlood glucose (BG) dysregulation, including hyperglycemia, hypoglycemia and increased glycemic variability (GV), is common in septic patients and potentially associated with poor clinical outcomes. However, the prognostic value of early BG trajectories remains unclear. We intend to investi...
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Frontiers Media S.A.
2025-06-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1610519/full |
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| author | Huan Ma Xiayan Qian Xiaodong Song Rongjie Jiang Jialin Li Fang Xiao Ruoxu Dou Xiangdong Guan Ka Yin Lui Shuhe Li Changjie Cai |
| author_facet | Huan Ma Xiayan Qian Xiaodong Song Rongjie Jiang Jialin Li Fang Xiao Ruoxu Dou Xiangdong Guan Ka Yin Lui Shuhe Li Changjie Cai |
| author_sort | Huan Ma |
| collection | DOAJ |
| description | BackgroundBlood glucose (BG) dysregulation, including hyperglycemia, hypoglycemia and increased glycemic variability (GV), is common in septic patients and potentially associated with poor clinical outcomes. However, the prognostic value of early BG trajectories remains unclear. We intend to investigate the association between the early dynamic trajectory of BG and 1-year mortality among sepsis patients.MethodsThis retrospective study comprises a derivation cohort of sepsis patients admitted to the First Affiliated Hospital of Sun Yat-sen University (FAH-SYSU) from January 2018 to December 2023, and an external validation cohort of 10,874 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC) IV database. Distinct clusters were demarcated using K-means clustering based on the BG trajectory within the first 48 hours after ICU admission, while the optimal number of clusters was determined by a consensus of quantitative metrics and the elbow plot. Kaplan-Meier survival curves and multivariable Cox proportional hazards regression models were used to assess the association between these identified clusters and 1-year mortality.ResultsAmong 3,655 sepsis patients from the FAH-SYSU dataset, we identified 5 distinct clusters of BG trajectories, which were significantly associated with 1-year mortality risk. In the full Cox regression model, patients with “low-stable” and “moderate-stable” trajectories had the lowest 1-year mortality risk (P = 0.077). Conversely, patients with a “high-stable” trajectory (HR: 1.61, 95% CI: 1.35-1.92, P < 0.001) and those exhibiting unstable trends had significantly higher mortality risks (“high-decreasing”, HR: 1.38, 95% CI: 1.16-1.65, P < 0.001; “moderate-increasing”, HR: 1.37, 95% CI: 1.18-1.60, P < 0.001). External validation found consistent clusters with similar mortality trends. Restricted cubic spline analysis demonstrated a U-shaped association for mean glucose levels and a J-shaped relationship for GV linked to 1-year mortality risks, while an optimal glycemic range of 122 to 160 mg/dL and GV less than 0.18 indicated improved survival.ConclusionEarly BG trajectory patterns are independently associated with long-term mortality in sepsis patients. Incorporating dynamic BG measurements into clinical practice may improve risk stratification and guide individualized glucose management strategies. |
| format | Article |
| id | doaj-art-7fcd91cc9557461a8d2536e780845a30 |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-7fcd91cc9557461a8d2536e780845a302025-08-20T02:32:06ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.16105191610519Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validationHuan Ma0Xiayan Qian1Xiaodong Song2Rongjie Jiang3Jialin Li4Fang Xiao5Ruoxu Dou6Xiangdong Guan7Ka Yin Lui8Shuhe Li9Changjie Cai10Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaUniversity of Exeter Medical School, University of Exeter, Exeter, United KingdomDepartment of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, ChinaBackgroundBlood glucose (BG) dysregulation, including hyperglycemia, hypoglycemia and increased glycemic variability (GV), is common in septic patients and potentially associated with poor clinical outcomes. However, the prognostic value of early BG trajectories remains unclear. We intend to investigate the association between the early dynamic trajectory of BG and 1-year mortality among sepsis patients.MethodsThis retrospective study comprises a derivation cohort of sepsis patients admitted to the First Affiliated Hospital of Sun Yat-sen University (FAH-SYSU) from January 2018 to December 2023, and an external validation cohort of 10,874 sepsis patients from the Medical Information Mart for Intensive Care (MIMIC) IV database. Distinct clusters were demarcated using K-means clustering based on the BG trajectory within the first 48 hours after ICU admission, while the optimal number of clusters was determined by a consensus of quantitative metrics and the elbow plot. Kaplan-Meier survival curves and multivariable Cox proportional hazards regression models were used to assess the association between these identified clusters and 1-year mortality.ResultsAmong 3,655 sepsis patients from the FAH-SYSU dataset, we identified 5 distinct clusters of BG trajectories, which were significantly associated with 1-year mortality risk. In the full Cox regression model, patients with “low-stable” and “moderate-stable” trajectories had the lowest 1-year mortality risk (P = 0.077). Conversely, patients with a “high-stable” trajectory (HR: 1.61, 95% CI: 1.35-1.92, P < 0.001) and those exhibiting unstable trends had significantly higher mortality risks (“high-decreasing”, HR: 1.38, 95% CI: 1.16-1.65, P < 0.001; “moderate-increasing”, HR: 1.37, 95% CI: 1.18-1.60, P < 0.001). External validation found consistent clusters with similar mortality trends. Restricted cubic spline analysis demonstrated a U-shaped association for mean glucose levels and a J-shaped relationship for GV linked to 1-year mortality risks, while an optimal glycemic range of 122 to 160 mg/dL and GV less than 0.18 indicated improved survival.ConclusionEarly BG trajectory patterns are independently associated with long-term mortality in sepsis patients. Incorporating dynamic BG measurements into clinical practice may improve risk stratification and guide individualized glucose management strategies.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1610519/fullsepsisblood glucose1-year mortalitytrajectory analysisK-means clustering |
| spellingShingle | Huan Ma Xiayan Qian Xiaodong Song Rongjie Jiang Jialin Li Fang Xiao Ruoxu Dou Xiangdong Guan Ka Yin Lui Shuhe Li Changjie Cai Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation Frontiers in Immunology sepsis blood glucose 1-year mortality trajectory analysis K-means clustering |
| title | Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation |
| title_full | Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation |
| title_fullStr | Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation |
| title_full_unstemmed | Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation |
| title_short | Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation |
| title_sort | identifying early blood glucose trajectories in sepsis linked to distinct long term outcomes a k means clustering study with external validation |
| topic | sepsis blood glucose 1-year mortality trajectory analysis K-means clustering |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1610519/full |
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