杨小芳, 路宁, 杨路希, 崔芬芬, 孟文勃, 李元敏. 急性Stanford A型主动脉夹层术后急性肾损伤发生风险列线图预测模型的建立[J]. 心脏杂志, 2022, 34(5): 562-566. DOI: 10.12125/j.chj.202109056
    引用本文: 杨小芳, 路宁, 杨路希, 崔芬芬, 孟文勃, 李元敏. 急性Stanford A型主动脉夹层术后急性肾损伤发生风险列线图预测模型的建立[J]. 心脏杂志, 2022, 34(5): 562-566. DOI: 10.12125/j.chj.202109056
    Xiao-fang YANG, Ning LU, Lu-xi YANG, Fen-fen CUI, Wen-bo MENG, Yuan-min LI. Predicting risks of acute kidney injury in patients after acute aortic dissection repair surgery: development and assessment of a nomogram prediction model[J]. Chinese Heart Journal, 2022, 34(5): 562-566. DOI: 10.12125/j.chj.202109056
    Citation: Xiao-fang YANG, Ning LU, Lu-xi YANG, Fen-fen CUI, Wen-bo MENG, Yuan-min LI. Predicting risks of acute kidney injury in patients after acute aortic dissection repair surgery: development and assessment of a nomogram prediction model[J]. Chinese Heart Journal, 2022, 34(5): 562-566. DOI: 10.12125/j.chj.202109056

    急性Stanford A型主动脉夹层术后急性肾损伤发生风险列线图预测模型的建立

    Predicting risks of acute kidney injury in patients after acute aortic dissection repair surgery: development and assessment of a nomogram prediction model

    • 摘要:
        目的  建立急性Stanford A型主动脉夹层(ATAAD)修复术后急性肾损伤(AKI)风险的疾病预测列线图模型。
        方法  回顾性分析2017年1月~2021年1月就诊于兰州大学第一医院的194名ATAAD患者的23项临床资料。分为Non-AKI组和AKI组,利用LASSO回归和Logistic回归进行筛选预测因素,使用R语言建立列线图预测模型,使用C指数、校准图、ROC曲线和决策曲线分析评估预测模型的识别、校准和临床有用性。
        结果  Logistic回归分析示心衰、肝功能不全、肾功能不全、异常心电图、心包积液及肾动脉夹层是影响ATAAD术后AKI的独立危险因素(P<0.05)。预测模型使用R语言建立,并以列线图的形式呈现。C指数为0.779,通过内部验证C指数为0.748,AUC值为0.778,该模型显示出良好的预测能力。
        结论  AKI列线图具有良好的预测能力,可用于ATAAD患者术后AKI的预测。

       

      Abstract:
        AIM  To develop and internally validate the risks of acute kidney injury (AKI) in patients after acute aortic dissection (ATAAD) repair surgery.
        METHODS  The 23 variables of 194 patients with ATAAD confirmed by CTA and surgery in the First Hospital of Lanzhou University from January 2017 to January 2021 were analyzed retrospectively. The incidence of postoperative AKI was counted and divided into non-AKI group and AKI group. The predictive factors were screened by LASSO regression and Logistic regression. The nomogram prediction model was established by R language. The identification, calibration and clinical usefulness of the prediction model were evaluated by C index, calibration chart, ROC curve and decision curve analysis.
        RESULTS  The morbidity of AKI was 43.30%. Logistic regression analysis showed that heart failure, liver insufficiency, renal insufficiency, abnormal ECG, pericardial effusion and renal artery dissection were independent factors affecting AKI after ATAAD repair surgery (P<0.05). Based on the risk factors of AKI morbidity in ATAAD patients, a model containing the independent predictors was developed using R language and presented in nomogram. The model showed good prediction ability, C index was 0.779, ROC AUC value was 0.778 and the calibration was good. In the interval verification, it reached the high C index of 0.748.
        CONCLUSION  AKI nomogram has good predictive ability and can be used to predict AKI in patients after ATAAD repair surgery.

       

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