Ma Huan, Qian Xiayan, Song Xiaodong, Jiang Rongjie, Li Jialin, Xiao Fang, Dou Ruoxu, Guan Xiangdong, Lui Ka Yin, Li Shuhe, Cai Changjie
Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
University of Exeter Medical School, University of Exeter, Exeter, United Kingdom.
Front Immunol. 2025 Jun 5;16:1610519. doi: 10.3389/fimmu.2025.1610519. eCollection 2025.
Blood 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.
This 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.
Among 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 ( = 0.077). Conversely, patients with a "high-stable" trajectory (HR: 1.61, 95% CI: 1.35-1.92, < 0.001) and those exhibiting unstable trends had significantly higher mortality risks ("high-decreasing", HR: 1.38, 95% CI: 1.16-1.65, < 0.001; "moderate-increasing", HR: 1.37, 95% CI: 1.18-1.60, < 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.
Early 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.
血糖（BG）失调，包括高血糖、低血糖和血糖变异性（GV）增加，在脓毒症患者中很常见，并且可能与不良临床结局相关。然而，早期血糖轨迹的预后价值仍不清楚。我们旨在研究脓毒症患者早期血糖动态轨迹与1年死亡率之间的关联。
这项回顾性研究包括一个推导队列，即2018年1月至2023年12月期间入住中山大学附属第一医院（FAH-SYSU）的脓毒症患者，以及一个来自重症监护医学信息集市（MIMIC）IV数据库的10874例脓毒症患者的外部验证队列。根据重症监护病房（ICU）入院后48小时内的血糖轨迹，使用K均值聚类划分不同的聚类，而聚类的最佳数量则通过定量指标的共识和肘部图来确定。使用Kaplan-Meier生存曲线和多变量Cox比例风险回归模型来评估这些确定的聚类与1年死亡率之间的关联。
在FAH-SYSU数据集中的3655例脓毒症患者中，我们确定了5种不同的血糖轨迹聚类，它们与1年死亡风险显著相关。在完整的Cox回归模型中，“低稳定型”和“中稳定型”轨迹的患者1年死亡风险最低（=0.077）。相反，“高稳定型”轨迹的患者（HR：1.61，95%CI：1.35-1.92，<0.001）以及呈现不稳定趋势的患者（“高下降型”，HR：1.38，95%CI：1.16-1.65，<0.001；“中上升型”，HR：1.37，95%CI：1.18-1.60，<0.001）的死亡风险显著更高。外部验证发现了具有相似死亡趋势的一致聚类。受限立方样条分析表明，平均血糖水平与1年死亡风险呈U形关联，GV与1年死亡风险呈J形关系，而最佳血糖范围为122至160mg/dL且GV小于0.18表明生存率提高。
早期血糖轨迹模式与脓毒症患者的长期死亡率独立相关。将动态血糖测量纳入临床实践可能会改善风险分层并指导个体化血糖管理策略。
