下肢动脉硬化闭塞症介入疗效的预测模型  

Predictive model for interventional efficacy in lower extremity arteriosclerosis obliterans

作　　者：杨镇玮[1] 吴庆睿 马文杰 田野[1] Yang Zhenwei;Wu Qingrui;Ma Wenjie;Tian Ye(Department of Vascular and Thyroid Surgery,the First Affiliated Hospital of Xinjiang Medical University,Urumqi 830054,China)

机构地区：[1]新疆医科大学第一附属医院血管甲状腺外科,乌鲁木齐830054

出　　处：《国际外科学杂志》2024年第7期446-454,I0006,共10页International Journal of Surgery

基　　金：新疆维吾尔自治区自然科学基金(2020D01C239)。

摘　　要：目的构建下肢动脉硬化闭塞症(LEASO)介入疗效的预测模型并进行效能评价,预测下肢动脉硬化闭塞症患者介入治疗的效果。方法回顾性分析238例下肢动脉硬化闭塞症患者资料,其中男性188例,女性50例,年龄35~88岁,平均68岁。根据7∶3比例简单随机分为训练集(n=166)与测试集(n=72),并在训练集及测试集中根据是否发生不良预后,分为MALEs组和非MALEs组,训练集及测试集MALEs组分别为67例及26例,非MALEs组分别为99例及46例;在训练集中通过LASSO回归筛选出对于结局事件最重要的变量,作为模型的预测指标纳入到多因素Logistic回归中,构建预测模型并使用列线图可视化处理,并使用训练集及测试集的数据对模型效能进行检测。结果通过LASSO回归选择出全身免疫炎症反应指数(SIIRI)、Rutherford分级>4级、膝下动脉段分级(IP)>1级、踝下动脉段分级(P)≥1级作为模型的预测指标,构建回归模型。模型在训练集和测试集的数据中曲线下面积、敏感度、特异度分别为0.813、80.6%、72.7%和0.764、65.4%、80.4%,校准曲线与期望基本一致,模型的决策曲线在训练集及测试集的阈值概率为0~0.79和0~0.66的范围内时,模型在临床上应用的准确性、净获益率最高。结论通过患者术前的Rutherford分级、IP分级、P分级及SIIRI构建的预测模型可以早期识别MALEs的高危人群并针对性的强化治疗,有助于改善此类患者的预后,在临床中具有一定价值。ObjectiveTo develop a predictive model for the intervention efficacy of lower extremity atherosclerotic occlusive disease(LEASO)and evaluate its performance to predict the outcomes of intervention therapy for patients with lower extremity atherosclerotic occlusive disease.MethodsThis study retrospectively analyzed data from 238 patients with lower extremity atherosclerotic occlusive disease(LEASO),including 188 males and 50 females,aged between 35 and 88 years with a mean age of 68 years.These patients were randomly divided in a 7∶3 ratio into a training set(n=166)and a testing set(n=72)based on adverse outcomes,both training and test sets were divided into MALEs and non-MALEs groups.The training set had 67 MALEs and 99 non-MALEs,while the test set had 26 MALEs and 46 non-MALEs.Important variables related to outcome events were selected using LASSO regression in the training set and incorporated into a multifactorial logistic regression model to construct a predictive model.The model was visualized using forest plots and its performance was evaluated using data from both the training and testing sets.ResultsThrough LASSO regression,SIIRI(Systemic immune inflammatory response index,SIIRI),Rutherford>4,IP(Infrapopliteal,IP)>1,and P(Pedal,P)≥1 were selected as predictive indicators for the model.The area under the curve,sensitivity,and specificity of the model in the training set and testing set were 0.813,80.6%,72.7%,and 0.764,65.4%,80.4%.The calibration curve was consistent with expectations.The decision curves of the model had the highest accuracy,net benefit rate for clinical application of the model when the threshold probabilities of the training set and test set were in the range of 0~0.79 and 0~0.66.ConclusionsThe predictive model built using preoperative Rutherford classification,IP classification,P classification,and SIIRI can identify high-risk individuals for early detection of MALEs and provide targeted intensified treatment.This model has practical significance in improving the prognosis of such pa

关 键 词：动脉硬化 闭塞性 下肢 LOGISTIC模型 列线图 GLASS分级 

分 类 号：R654.4[医药卫生—外科学]