在危重病房患者中，周围插管中心静脉导管（PICC）发生导管阻塞的发生率很高，但关于此类阻塞的风险因素的研究还不足。本研究旨在检验多个风险因素对PICC导管阻塞的影响，以找到能够帮助临床医务人员早期识别具有高阻塞风险的患者的证据。研究团队进行了一项回顾性观察性临床研究。该研究在中国温州市温州医科大学附属第二医院和玉瑛儿童医院进行。研究对象为2022年1月至2022年4月期间在该医院成人重症监护室接受治疗的300名使用PICC导管的患者。根据导管插入时间，研究团队对1 ~ 300名参与者进行编号，然后根据随机数字表选择一个起始数字将其分为两组。这两组为：（1）培训组225名参与者和（2）验证组75名参与者。主要的结果指标是评估影响PICC导管滞留期间发生导管阻塞的因素，包括完全和不完全阻塞，以建立PICC导管阻塞的风险预测模型。次要的结果指标是重症监护室患者PICC拔管排出的发生情况。研究团队对培训组的数据进行了单因素分析和多因素logistic回归分析以确定风险因素。团队：（1）利用重症监护室PICC患者导管阻塞的独立风险因素建立了风险预测模型，（2）使用Hosmer-Lemeshow拟合优度检验测试了预测模型。双尾P >0.05表明模型拟合良好。然后，团队将该模型应用于验证组，并利用接收者操作特征曲线（ROC曲线）评估模型的预测能力。面积超过0.5表示有预测价值，面积越大，预测值越好。训练组PICC阻塞的发生率为18.22％，其中包括10名完全阻塞和31名部分阻塞的参与者。研究团队使用SPSS 22.0和R软件进行统计分析。单因素分析显示与PICC阻塞有关的13个因素，包括：（1）年龄≥65岁（P < .001），BMI ≥24 kg/m2（P < .001），（2）糖尿病（P < .001），（3）中风（P < .001），（4）高血压（P < .001），（5）恶性肿瘤（P < .001），（6）深静脉血栓症史（P < .001），（7）肢体活动（P < .001），（8）沉菇封管频率（Q8h）（P = .035），（9）滞留时间（P < .001），（10）血小板计数增多（P = .036），（11）输血（P < .001），和（12）静脉营养（P < .001）。PICC阻塞的独立风险因素包括：（1）年龄≥65岁-OR = 1.224，P = .028;（2）BMI ≥24 kg/m2-OR = 1.679，P = .004;（3）糖尿病-OR = 1.343，P = .017;（4）恶性肿瘤-OR = 2.736，P <.001;（5）输血-OR = 1.947，P <.001)，和（6）静脉营养-OR = 2.021，P <.001。沉菇封管频率（Q8h）-OR = -2.145，P = .002，是一个保护性因素。在培训组中，预测PICC阻塞的曲线下面积（AUC）为0.917。预测模型的Hosmer-Lemeshow检验显示该模型内部检验结果无显著差异（χ2 = 5.830，P = .666），表明该模型通过了内部验证。预测模型的理想与校准曲线高度一致，模型校准良好。验证组的Hosmer-Lemeshow检验显示模型外部测试结果无显著差异，说明模型具有较高的一致性。年龄≥65岁、BMI≥24 kg/m2、糖尿病、恶性肿瘤、输血和静脉营养是PICC阻塞的独立风险因素，而沉菇封管频率（Q8h）是一个保护性因素。该预测模型具有在重症监护室中识别高风险PICC阻塞患者的出色能力。

Peripherally inserted central catheters (PICCs) have a high incidence of catheter occlusion, but research exploring the risk factors for such an occlusion for patients in intensive care units (ICUs) is lacking.The study intended to examine the impact of multiple risk factors on the occurrence of PICC catheter occlusion to find evidence that can help clinical medical staff identify patients at an early stage who are at high risk of a catheter occlusion.The research team performed a retrospective, observational clinical study.The study took place at a tertiary general hospital, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University in Wenzhou, China.Participants were 300 patients with a PICC who received treatment in the hospital's adult ICU between January 2019 and April 2022.According to the time of catheterization, the research team numbered the 1~300 participants and then selected one starting number to divided them into two groups according to the random number table. These two groups were: (1) a training group with 225 participants and (2) validation group with 75 participants.The main outcome measure was the evaluation of the factors impacting patients who had had a PICC occlusion during catheter retention, including complete and incomplete occlusions, to build a risk prediction model of PICC occlusion. A secondary outcome measure was the occurrence of extubation of the PICC discharge of the ICU patient. The research team performed a univariate analysis of the training group's data and a multivariate logistic regression analysis of the risk factors. The team: (1) built a risk prediction model of PICC occlusion using the independent risk factors for catheter occlusion for PICC patients in an ICU and (2) used the Hosmer-Lemeshow goodness-of-fit test to test the prediction model. A two tailed using p>0.05 indicated that the model had a good fit. Then, the team applied the model to the validation group and evaluated the model's predictive ability using a receiver operating characteristic (ROC) curve. The team considered an area under the curve (AUC) >0.5 to have predictive value. The larger the area was, the better the predicted value was. The incidence of PICC occlusion in the training group was 18.22%, including 10 participants with complete occlusion and 31 with partial occlusion. The team used the SPSS 22.0 and R software for statistical analysis.The univariate analysis showed that 13 factors were associated with PICC occlusion, including: (1) an age ≥65 years (P < .001), a BMI of ≥24 kg/m2 (P < .001), (2) a BMI of ≥24kg/m2 (P = .002), (3) diabetes (P < .001), (4) stroke (P < .001), (5) hypertension (P < .001), (6) malignant tumors (P < .001), (7) a history of deep vein thrombosis (P < .001), (8) limb activity (P < .001), (10) flushing and sealing pipe frequency of Q8h (P = .035), (11) retention time (P < .001), (12) an increased platelet count (P = .036), (13) blood transfusions (P < .001), and (14) intravenous nutrition (P < .001). The independent risk factors for PICC occlusion included: (1) age ≥65 years-OR=1.224, P = .028; (2) BMI ≥24 kg/m2-OR=1.679, P = .004; (3) diabetes-OR=1.343, P = .017; (4) malignant tumors-OR=2.736, P < .001; (5) blood transfusions-OR=1.947, P < .001), and (6) intravenous nutrition-OR=2.021, P < .001. The frequency of flushing and sealing the pipe (Q8h)-OR=-2.145, P = .002-was a protective factor. In the training group, the area under the curve (AUC) for predicting a PICC occlusion was 0.917. The Hosmer-Lemeshow test of the prediction model showed that no significant differences existed in the test results within the model (χ2 = 5.830, P = .666), indicating that the model passed the internal validation. The ideal and calibration curves of the prediction model were highly coincident, and the model was well calibrated. The Hosmer-Lemeshow test of the validation group showed that no significant differences existed in the test results outside the model, suggesting that the model had high consistency.Age ≥65 years, BMI ≥24 kg/m2, diabetes, malignant tumors, blood transfusions, and intravenous nutrition were independent risk factors for PICC occlusion, while the frequency of flushing and sealing pipe (Q8h) was a protective factor. This prediction model had an outstanding ability to discriminate in identifying patients with a high-risk of PICC occlusion in the ICU.