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目的:探讨脓毒症患者在重症监护病房（ICU）住院期间发生下肢深静脉血栓（LEDVT）的危险因素，基于危重症评分联合炎症标志物构建ICU脓毒症患者发生LEDVT的列线图预测模型，并验证其早期预测的有效性。方法:回顾性纳入2015年1月至2021年12月入住济宁医学院附属manbet官网登录 ICU的726例脓毒症患者作为训练集，以构建预测模型；此外，纳入2022年1月至2023年6月入住济宁医学院附属manbet官网登录 ICU的213例脓毒症患者作为验证集，以验证预测模型的性能。收集患者临床数据，如人口统计学信息、入ICU时的生命体征、基础疾病、既往史、入ICU 24 h内各类评分、入ICU首次实验室指标、下肢静脉超声结果、治疗情况、预后指标等。采用Lasso回归分析筛选脓毒症患者发生LEDVT的影响因素，并综合Logistic回归分析的结果构建列线图模型。通过受试者工作特征曲线（ROC曲线）、校准曲线、临床影响曲线（CIC）和决策曲线分析（DCA）对列线图模型进行评价。结果:训练集脓毒症患者入ICU后LEDVT发生率为21.5%（156/726），验证集脓毒症患者入ICU后LEDVT发生率为21.6%（46/213）。训练集与验证集患者基线资料具有可比性。Lasso回归分析显示，从67个参数中筛选出7个自变量与脓毒症患者发生LEDVT有关。Logistic回归分析显示，年龄〔优势比（ OR）＝1.03，95%可信区间（95% CI）为1.01～1.04， P<0.001〕、体质量指数（BMI： OR＝1.05，95% CI为1.01～1.09， P＝0.009）、静脉血栓栓塞症（VTE）评分（ OR＝1.20，95% CI为1.11～1.29， P<0.001）、活化部分凝血活酶时间（APTT： OR＝0.98，95% CI为0.97～0.99， P＝0.009）、D-二聚体（ OR＝1.03，95% CI为1.01～1.04， P<0.001）、皮肤或软组织感染（ OR＝2.53，95% CI为1.29～4.98， P＝0.007）、股静脉置管（ OR＝3.72，95% CI为2.50～5.54， P<0.001）是脓毒症患者发生LEDVT的独立影响因素。结合以上变量构建列线图预测模型，ROC曲线分析显示，该列线图模型预测脓毒症患者发生LEDVT的曲线下面积（AUC）为0.793（95% CI为0.746～0.841），在验证集中AUC为0.844（95% CI为0.786～0.901）；校准曲线表明，预测概率与实际发生的概率之间存在良好的匹配度，CIC曲线和DCA曲线均提示其具有良好的临床净获益。 结论:基于危重症评分联合炎症标志物构建的列线图模型可用于ICU脓毒症患者发生LEDVT的早期预测，这有助于临床医生更早地识别脓毒症患者发生LEDVT的潜在风险，从而实现早期治疗。

Objective:To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction.Methods:726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA).Results:The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio ( OR) = 1.03, 95% confidence interval (95% CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95% CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score ( OR = 1.20, 95% CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95% CI was 0.97 to 0.99, P = 0.009), D-dimer ( OR = 1.03, 95% CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection ( OR = 2.53, 95% CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation ( OR = 3.72, 95% CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95% CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95% CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. Conclusion:The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.