作者：余国雄，伍丹丹

单位：

430060湖北省武汉市，武汉大学人民医院呼吸与危重症科1科

单位（英文）：

Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China

关键词：

肺栓塞；预后；影响因素分析；列线图

关键词（英文）：

Pulmonary embolism; Prognosis; Root cause analysis; Nomograms

中图分类号：

R 563.5

DOI：

10.12114/j.issn.1008-5971.2023.00.289

基金项目：

湖北省重点实验室开放项目（2021KFY022）

摘要：

目的　探讨肺栓塞患者预后不良的影响因素，构建其风险预测列线图模型并进行验证。方法　选取2019年6月至2021年10月武汉大学人民医院收治的肺栓塞患者184例为研究对象。收集患者的临床资料，根据患者发病12个月后预后情况将其分为预后良好组（n=125）和预后不良组（n=59）。采用多因素Logistic回归分析探讨肺栓塞患者预后不良的影响因素；采用R 3.5.2软件包构建肺栓塞患者预后不良风险预测列线图模型；采用Bootstrap法重复抽样1 000次进行内部验证，计算一致性指数，并进行ROC曲线分析、Hosmer-Lemeshoe拟合优度检验，同时绘制校准曲线及决策曲线。结果　预后不良组有恶性肿瘤史者占比、有静脉血栓栓塞症（VTE）史者占比、有右心室功能不全者占比、有下肢深静脉血栓形成（DVT）者占比、服用避孕药者占比、入院时D-二聚体、入院时同型半胱氨酸、入院时肌钙蛋白、入院时脑钠肽高于预后良好组（P＜0.05）。多因素Logistic回归分析结果显示，恶性肿瘤史、VTE史、右心室功能不全、DVT、入院时D-二聚体、入院时同型半胱氨酸、入院时肌钙蛋白为肺栓塞患者预后不良的独立影响因素（P＜0.05）。基于多因素Logistic回归分析结果，构建肺栓塞患者预后不良风险预测列线图模型。该列线图模型的一致性指数为0.775〔95%CI（0.650，0.821）〕。ROC曲线分析结果显示，该列线图模型预测肺栓塞患者预后不良的AUC为0.888〔95%CI（0.831，0.946）〕。Hosmer-Lemeshoe拟合优度检验结果显示，该列线图模型拟合较好（χ2=11.589，P=0.071）。决策曲线分析结果显示，当该列线图模型预测肺栓塞患者预后不良的风险阈值＞0.07时，患者的净获益率大于0。结论　恶性肿瘤史、VTE史、右心室功能不全、DVT、入院时D-二聚体、入院时同型半胱氨酸、入院时肌钙蛋白为肺栓塞患者预后不良的独立影响因素，基于上述影响因素构建的列线图模型对肺栓塞患者预后不良具有一定预测价值，这可为疾病预防策略的制定提供指导依据。

英文摘要：

Objective To explore the influencing factors of poor prognosis of patients with pulmonary embolism, andconstruct and validate the nomogram model for predicting its risk. Methods A total of 184 patients with pulmonary embolismadmitted to Renmin Hospital Wuhan University from June 2019 to October 2021 were selected as the research subjects. Theclinical data of the patients were collected, the patients were divided into good prognosis group (n=125) and poor prognosisgroup (n=59) according to their prognosis after 12 months of onset. Multivariate Logistic regression analysis was used to analyzethe influencing factors of poor prognosis of patients with pulmonary embolism. The nomogram model for predicting the risk ofpoor prognosis of patients with pulmonary embolism was constructed by using the R 3.5.2 software. The internal validation wasperformed by the Bootstrap method (1 000 repetitive samples) , the consistency index was calculated. The ROC curve analysis,Hosmer-Lemeshoe goodness of fit test were performed. The calibration curve and decision curve were plotted. Results Theproportion of patients with history of malignant tumors, proportion of patients with history of venous thromboembolism (VTE) ,proportion of patients with right ventricular dysfunction, proportion of patients with deep vein thrombosis (DVT) , proportion ofpatients taking contraceptive, D-dimer at admission, homocysteine at admission, troponin at admission, and brain natriureticpeptide at admission in the poor prognosis group were higher than those in the good prognosis group (P < 0.05) . MultivariateLogistic regression analysis showed that history of malignant tumors, history of VTE, right ventricular dysfunction, DVT, D-dimer at admission, homocysteine at admission, and troponin at admission were the independent influencing factors of poor prognosisof patients with pulmonary embolism (P < 0.05) . The nomogram model for predicting poor prognosis of patients with pulmonaryembolism was constructed based on the multivariate Logistic regression analysis results. The consistency index of the nomogrammodel was 0.775 [95%CI (0.650, 0.821) ] . The results of ROC curve analysis showed that the AUC of the nomogram model forpredicting the poor prognosis of patients with pulmonary embolism was 0.888 [95%CI (0.831, 0.946) ] . The results of HosmerLemeshoe goodness of fit test showed that the nomogram model fit well (χ2=11.589, P=0.071) . The results of decision curveanalysis showed that when the adverse risk threshold of the nomogram model for predicting the poor prognosis of patients withpulmonary embolism was > 0.07, the net benefit rate of patients was greater than 0. Conclusion History of malignant tumors,history of VTE, right ventricular dysfunction, DVT, D-dimer at admission, homocysteine at admission, and troponin at admissionare the independent influencing factors of poor prognosis of patients with pulmonary embolism. The nomogram model constructedbased on the above influencing factors has a certain predictive value for the poor prognosis of patients with pulmonary embolism,this can provide guidance basis for the development of disease prevention strategies.

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