动脉瘤性蛛网膜下腔出血患者下肢深静脉血栓形成风险预测模型的构建与应用

Construction and application of a risk prediction model for lower limb deep vein thrombosis in patients with aneurysmal subarachnoid hemorrhage

目的探讨动脉瘤性蛛网膜下腔出血(aSAH)患者下肢深静脉血栓(DVT)形成风险预测模型的构建与应用。方法选取250例aSAH患者为研究对象,根据是否形成下肢深静脉血栓将患者分为DVT组(n=45)和非DVT组(n=205)。收集2组患者的一般资料,并比较2组患者的常规实验室指标。采用多因素Logistic回归分析筛选aSAH患者发生DVT的风险因素,建立aSAH后DVT的列线图预测模型并通过受试者工作特征(ROC)曲线分析模型的区分能力,采用临床决策曲线分析列线图的临床实用性。结果DVT组年龄、高血压史、格拉斯哥昏迷量表(GCS)评分、Hunt-Hess分级、住院时间与非DVT组患者比较,差异有统计学意义(P<0.05)。DVT组血浆纤维蛋白原水平高于非DVT组患者,差异有统计学意义(P<0.05),其他指标与非DVT组比较,差异无统计学意义(P>0.05)。多因素Logistic回归分析结果表明,年龄、Hunt-Hess分级、住院时间以及血浆纤维蛋白原水平是aSAH患者发生DVT的影响因素(P<0.05)。ROC曲线表明,列线图在建模组和验证组中均表现出良好预测性能,曲线下面积(AUC)分别为0.875(95%CI:0.802~0.948)和0.872(95%CI:0.757~0.987)。临床决策曲线分析表明,列线图预测aSAH患者发生DVT在大范围阈值中均有较高的净收益。结论aSAH患者的年龄、Hunt-Hess分级、住院时间以及血浆纤维蛋白原水平是发生DVT的影响因素。本研究建立的预测aSAH后发生的DVT列线图模型具有良好的区分能力、校准度和临床应用价值,可以更好地识别高风险患者,便于为患者提供个性化的预防和治疗策略。

Objective To investigate the construction and application of a risk prediction model for deep vein thrombosis(DVT)in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods A total of 250 patients with aSAH were enrolled in this study and divided into DVT(n=45)and non-DVT groups(n=205)based on the occurrence of DVT.General information and routinelaboratory indicators were collected and compared between the two groups.Multivariate Logistic regression analysis was performed to identify risk factors for DVT in aSAH patients.A nomogram prediction model for DVT after aSAH was established and its discriminative ability was evaluated using the receiver operating characteristic(ROC)curve.The clinical utility of the nomogram was assessed by decision curve analysis.Results Statistically significant differences were observed in age,history of hypertension,Glasgow Coma Scale(GCS)score,Hunt-Hess grade and length of hospital stay between the DVT and non-DVT groups(P<0.05).The plasma fibrinogen level was significantly higher in the DVT group compared to the non-DVT group(P<0.05),while no significant differences were found for other indicators(P>0.05).Multivariate Logistic regression analysis revealed that age,Hunt-Hess grade,length of hospital stay,and plasma fibrinogen level were influencing factors of the occurrence of DVT in aSAH patients(P<0.05).ROC analysis showed that the nomogram exhibited good predictive performance in both the modeling and validation groups,with areas under the curve(AUCs)of 0.875(95%CI,0.802 to 0.948)and 0.872(95%CI,0.757 to 0.987),respectively.Decision curve analysis indicated that the nomogram provided a high net benefit for predicting DVT in aSAH patients across a wide range of threshold probabilities.Conclusion Age,Hunt-Hess grade,length of hospital stay,and plasma fibrinogen level are influential factors for DVT in aSAH patients.The nomogram in predicting DVT after aSAH demonstrates good discriminative ability,calibration degree,and clinical application value.This model can better identify high-risk patients and provide individualized prevention and treatment strategies.