Deng Chao, Tang Hao, Li Jingyu, Li Zhenxiong, Shen Kangjun, Zhang Zhiwei, Jiang Bo, Tan Ling
Department of Cardiovascular Surgery, The Second Xiangya Hospital of Central South University, Changsha, 410011, China.
Heliyon. 2024 Feb 13;10(4):e25925. doi: 10.1016/j.heliyon.2024.e25925. eCollection 2024 Feb 29.
Early identification of postoperative ischemic stroke among patients with acute DeBakey type I aortic dissection (ADIAD) is of great significance to taking timely effective treatment. We aimed to develop and validate a prediction model for postoperative ischemic stroke in ADIAD patients who underwent total arch replacement (TAR) and frozen elephant trunk (FET) under mild hypothermia.
ADIAD patients who underwent TAR and FET between January 2017 and April 2023 were enrolled in our study. Preoperative and intraoperative variables were selected using pairwise comparisons, the Least Absolute Shrinkage and Selection Operator (LASSO), and logistic regression to construct a prediction model for postoperative ischemic stroke. The accuracy and calibration of the model were assessed using 1000 bootstrap resamples for internal validation, with the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow test. The AUC was also used to evaluate the model's accuracy in the validation cohort.
The development cohort included 246 patients. The mean [standard deviation (SD)] age of patients in the cohort was 50.7 (11.2) years, 196 (79.7%) were men, and 22 (8.9%) were diagnosed with postoperative ischemic stroke. The validation cohort included 73 patients with a mean (SD) age of 52.5 (11.9) years, 58 (79.5%) were men and 3 (4.1%) were diagnosed with postoperative ischemic stroke. Three variables out of the initial 40 potential predictors were included in the final prediction model: the platelet count [odd ratio (OR), 0.992; 95% confidence interval (CI), 0.983-1.000], the presence of innominate artery dissection (OR, 3.400; 95% CI, 1.027-11.260), and the flow of selective cerebral perfusion (OR, 0.147; 95% CI, 0.046-0.469). The mean AUC in the development cohort was 0.77 (95% CI, 0.68-0.87), and calibration was checked with the Hosmer-Lemeshow test (P = 0.78). In the validation cohort, the AUC was 0.98 (95% CI, 0.94-1.00). A prediction model and a clinical impact curve were developed for practical purposes.
In this study, we have developed a prediction model with competent discriminative ability and calibration. This model can be used for early assessment of the risk of postoperative ischemic stroke in patients with ADIAD following TAR and FET under mild hypothermia.
急性Ⅰ型主动脉夹层（ADIAD）患者术后缺血性卒中的早期识别对于及时采取有效治疗具有重要意义。我们旨在建立并验证一个预测模型，用于预测在轻度低温下接受全弓置换（TAR）和冰冻象鼻术（FET）的ADIAD患者术后缺血性卒中的发生情况。
纳入2017年1月至2023年4月期间接受TAR和FET的ADIAD患者。通过成对比较、最小绝对收缩和选择算子（LASSO）以及逻辑回归选择术前和术中变量，以构建术后缺血性卒中的预测模型。使用1000次自抽样重采样进行内部验证，通过受试者操作特征曲线（AUC）下面积和Hosmer-Lemeshow检验评估模型的准确性和校准情况。AUC还用于评估模型在验证队列中的准确性。
开发队列包括246例患者。队列中患者的平均[标准差（SD）]年龄为50.7（11.2）岁，196例（79.7%）为男性，22例（8.9%）被诊断为术后缺血性卒中。验证队列包括73例患者，平均（SD）年龄为52.5（11.9）岁，58例（79.5%）为男性，3例（4.1%）被诊断为术后缺血性卒中。最终预测模型纳入了最初40个潜在预测因素中的3个变量：血小板计数[比值比（OR），0.992；95%置信区间（CI），0.983 - 1.000]、无名动脉夹层的存在（OR，3.400；95% CI，1.027 - 11.260）以及选择性脑灌注流量（OR，0.147；95% CI，0.046 - 0.469）。开发队列中的平均AUC为0.77（95% CI，0.68 - 0.87），通过Hosmer-Lemeshow检验进行校准（P = 0.78）。在验证队列中，AUC为0.98（95% CI，0.94 - 1.00）。为实际应用开发了一个预测模型和一条临床影响曲线。
在本研究中，我们建立了一个具有良好鉴别能力和校准的预测模型。该模型可用于早期评估轻度低温下接受TAR和FET的ADIAD患者术后缺血性卒中的风险。
