Data_Sheet_6_Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning.CSV

ObjectiveA clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).
MethodsBaseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.
ResultsThe final incidence of AKI was 69.4% (120/173) in 173 patients with TAAAD and 28.6% (81/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630–0.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623–0.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA).
ConclusionWe successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.

研究目的 本研究借助机器学习(Machine Learning, ML)，构建针对A型急性主动脉夹层(Type A acute aortic dissection, TAAAD)及B型急性主动脉夹层(Type B acute aortic dissection, TBAAD)患者术后合并急性肾损伤(Acute kidney injury, AKI)的临床预测模型。
研究方法 本研究收集了2019年1月1日至2021年12月31日期间，新疆医科大学第一附属医院收治的急性主动脉夹层(Acute aortic dissection, AAD)患者的基线资料。(1) 明确基线血清肌酐(Serum creatinine, SCR)的估算方法，并以此作为AKI的诊断依据；(2) 将全部数据集随机划分为训练集(70%)与测试集(30%)，在训练集中采用多种机器学习方法进行Bootstrap建模与特征验证，并选取曲线下面积(Area Under Curve, AUC)最大的模型开展后续研究；(3) 通过模型可视化工具Shapley可加解释(Shapley Additive Explanations, SHAP)与递归特征缩减(Recursive feature reduction, REF)筛选最优机器学习模型的变量；(4) 最终利用测试集数据从区分度、校准度与临床获益三个方面对预筛选后的预测模型进行评估。
结果 173例A型急性主动脉夹层患者中，术后合并AKI的最终发生率为69.4%（120/173）；283例B型急性主动脉夹层患者中，该发生率为28.6%（81/283）。针对A型急性主动脉夹层相关AKI(TAAAD-AKI)，随机森林(Random Forest, RF)模型在训练集中展现出最优的预测性能（AUC=0.760，95%CI：0.630~0.881）；而针对B型急性主动脉夹层相关AKI(TBAAD-AKI)，轻量梯度提升机(Light Gradient Boosting Machine, LightGBM)模型表现最佳（AUC=0.734，95%CI：0.623~0.847）。特征筛选结果显示，两类急性主动脉夹层术后AKI的最终预测模型中共有的预测因子包括入院时的基线血清肌酐、血尿素氮(Blood urea nitrogen, BUN)、尿酸(Uric acid, UA)以及机械通气时长(Mechanical ventilation time, MVT)。A型急性主动脉夹层相关AKI模型的特异性预测因子包括：入院时的白细胞(White blood cell, WBC)、血小板(Platelet, PLT)、D-二聚体，以及血浆[原文存在截断]；B型急性主动脉夹层相关AKI模型的特异性预测因子则包括：入院时的N末端B型利钠肽原(N-terminal pro B-type natriuretic peptide, BNP)、血清钾、活化部分凝血活酶时间(Activated partial thromboplastin time, APTT)、收缩压(Systolic blood pressure, SBP)，以及术中联合肾动脉造影。最后从区分度来看，A型急性主动脉夹层所用随机森林模型的ROC曲线下面积为0.81，B型急性主动脉夹层所用LightGBM模型的ROC曲线下面积为0.74，二者均具备良好的预测精度。在校准度方面，A型急性主动脉夹层相关AKI的随机森林模型校准曲线与理想曲线拟合度最优，且布里尔分数(Brier score)最低（0.16）；同理，B型急性主动脉夹层相关AKI的LightGBM模型校准曲线与理想曲线拟合度最优，布里尔分数最低（0.15）。在临床获益方面，决策曲线分析(Decision Curve Analysis, DCA)结果显示，两类急性主动脉夹层所用的最优机器学习模型均具备良好的净获益。
研究结论 本研究通过不同的机器学习算法，成功构建并验证了针对A型及B型急性主动脉夹层患者术后AKI发生风险的临床预测模型。两类急性主动脉夹层相关AKI的主要预测因子存在一定差异，对应的AKI早期防控策略也有所不同，未来还需更多外部数据对模型进行验证。