脓毒症患者血肝素结合蛋白清除率与28 d院内死亡风险的关联

Association between clearance rate of serum heparin-binding protein and 28-day nosocomial mortality risk in patients with sepsis

  • 摘要:
    目的 探讨脓毒症患者血清肝素结合蛋白(HBP)清除率变化与28 d院内死亡风险的关联。
    方法 纳入2022年5月-2025年5月于河北省深州市医院就诊的脓毒症患者206例,根据入院1~7 d内的血清HBP变化建立HBP清除率潜在类别轨迹模型(LCGM),根据轨迹特征分为低水平下降组(n=58)、中水平下降组(n=56)和高水平稳定组(n=92)。比较各组临床资料和实验室指标;采用多元logistic回归分析轨迹潜在类别的影响因素;绘制Kaplan-Meier生存曲线比较累积生存率;采用Cox回归分析轨迹与28 d死亡风险的关系;采用受试者工作特征(ROC)曲线分析轨迹对死亡的预测效能,并进行亚组分析。
    结果 低水平下降组感染性休克比例、白细胞计数(WBC)、降钙素原(PCT)、C-反应蛋白(CRP)、凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)、D-二聚体(D-D)高于其他两组,高水平稳定组血小板(PLT)和纤维蛋白原(FIB)水平高于其他两组(P<0.05)。PLT、PCT、CRP、D-D是HBP清除率轨迹类别的影响因素(P<0.05)。与低水平下降组和中水平下降组相比,高水平稳定组28 d生存率更高(P<0.05)。校正混杂因素后,中水平下降组和低水平下降组死亡风险分别增加2.866倍和5.541倍(P<0.05)。ROC曲线分析显示,HBP清除率呈低水平下降变化轨迹时预测脓毒症患者28 d医院死亡风险的曲线下面积(AUC)为0.836(95%CI:0.764~0.887)。亚组分析显示,各亚组内HBP清除率与死亡风险均相关(P趋势<0.05)。
    结论 脓毒症患者血清HBP清除率呈低水平下降轨迹是28 d死亡的危险因素,对死亡风险具有良好预测价值,动态监测HBP清除率可为早期识别高危患者及风险分层提供重要依据。

     

    Abstract:
    OBJECTIVE  To investigate the association between changes in clearance rate of serum heparin-binding protein (HBP) and the 28-day in-hospital mortality risk in patients with septic.
    METHODS  A total of 206 septic patients admitted to Shenzhou Hospital in Hebei Province from May 2022 to May 2025 were enrolled. A latent class growth model (LCGM) was established based on serum HBP changes within 1–7 days of admission, categorizing patients into three trajectory groups: low-level decline (n=58), medium-level decline (n=56) and high-level stability (n=92). Clinical data and laboratory indicators were compared among the groups. Multivariate logistic regression analysis was employed to identify influencing factors of trajectory categories. Kaplan-Meier survival curves were plotted to compare cumulative survival rates among the groups. Cox regression was employed to assess the relationship between trajectories and 28-day mortality risk. Receiver operating characteristic (ROC) curve analysis was adopted evaluated the predictive efficacy of trajectories for mortality, followed by subgroup analysis.
    RESULTS  The low-level decline group exhibited a high proportion of septic shock, as well as high levels of white blood cell count (WBC), procalcitonin (PCT), C-reactive protein (CRP), prothrombin time (PT), activated partial thromboplastin time (APTT) and D-dimer (D-D) compared to the other two groups, while the high-level stability group showed high platelet (PLT) and fibrinogen (FIB) levels compared to the other two groups (P<0.05). PLT, PCT, CRP and D-D were identified as influencing factors for HBP clearance rate trajectory categories (P<0.05). The high-level stability group demonstrated a higher 28-day survival rate than the low- and medium-level decline groups (P<0.05). After adjusting for confounders, the medium- and low-level decline groups had 2.866-fold and 5.541-fold increased in mortality risks, respectively (P<0.05). ROC analysis revealed an area under the curve (AUC) of 0.836 (95%CI: 0.764–0.887) for predicting 28-day in-hospital mortality when HBP clearance followed a low-level decline trajectory. Subgroup analysis confirmed associations between HBP clearance rate and mortality risk across all subgroups (Ptrend<0.05).
    CONCLUSIONS  A low-level decline trajectory in serum HBP clearance rate is a risk factor for 28-day mortality in septic patients, demonstrating strong predictive value for mortality risk. Dynamic monitoring of HBP clearance provides critical insights for early identification of high-risk patients and risk stratification.

     

/

返回文章
返回