Zhao Honglei, Zhang Wei, Liu Yongmin, Peng Wenxing
Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
J Thorac Dis. 2024 Jan 30;16(1):273-284. doi: 10.21037/jtd-23-1396. Epub 2024 Jan 29.
Thrombocytopenia, a common complication of coronary artery bypass graft (CABG) surgery, is particularly prevalent among elderly individuals. This study developed a risk prediction model utilizing preoperative and intraoperative variables to identify high-risk elderly patients prone to developing thrombocytopenia.
The patients were retrospectively recruited from Beijing Anzhen Hospital between February 2019 and December 2020. Postoperative thrombocytopenia was defined as a postoperative platelet (PLT) count <100×10/L as measured within 7 days after surgery. The entire population was randomly split into derivation and validation sets in a 7:3 ratio. The derivation set underwent variable screen by the least absolute shrinkage and selection operator (LASSO) regression method. To evaluate the predictive ability of the model for thrombocytopenia, decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were generated in the derivation and validation sets.
A total of 1,773 patients were recruited in this study, with random assignment to either the derivation set (1,242 cases) or the validation set (531 cases). LASSO regression was utilized the risk factors associated with thrombocytopenia, resulting in selection of preoperative baseline variables: body mass index (BMI), estimated glomerular filtration rate (eGFR), B-type natriuretic peptide (BNP), preoperative PLT, and use of beta-blocker, and intraoperative variables: red blood cell (RBC) transfusion, plasma transfusion, use of intra-aortic balloon pump (IABP) and cardiopulmonary bypass (CPB), reoperation for bleeding, washed RBC transfusion volume, and use of epinephrine. The logistic regression was employed to establish the risk prediction. The area under the ROC curve (AUC) for the derivation set was 0.900 [95% confidence interval (CI): 0.880-0.920], while for the validation cohort, it was 0.897 (95% CI: 0.866-0.928).
The model incorporating significant preoperative and intraoperative variables exhibited good predictive performance for thrombocytopenia in elderly patients undergoing CABG surgery.
血小板减少症是冠状动脉旁路移植术（CABG）常见的并发症，在老年患者中尤为普遍。本研究利用术前和术中变量建立了一个风险预测模型，以识别易发生血小板减少症的高危老年患者。
回顾性纳入2019年2月至2020年12月在北京安贞医院就诊的患者。术后血小板减少症定义为术后7天内测得的血小板（PLT）计数<100×10/L。将全部人群按7:3的比例随机分为推导集和验证集。推导集采用最小绝对收缩和选择算子（LASSO）回归方法进行变量筛选。为评估该模型对血小板减少症的预测能力，在推导集和验证集中生成决策曲线分析（DCA）和受试者工作特征（ROC）曲线。
本研究共纳入1773例患者，随机分为推导集（1242例）和验证集（531例）。利用LASSO回归分析血小板减少症的相关危险因素，选择术前基线变量：体重指数（BMI）、估计肾小球滤过率（eGFR）、B型利钠肽（BNP）、术前PLT以及β受体阻滞剂的使用情况，以及术中变量：红细胞（RBC）输血、血浆输血、主动脉内球囊反搏（IABP）和体外循环（CPB）的使用、因出血再次手术、洗涤红细胞输血量以及肾上腺素的使用情况。采用逻辑回归建立风险预测模型。推导集的ROC曲线下面积（AUC）为0.900 [95%置信区间（CI）：0.880 - 0.920]，验证队列的AUC为0.897（95% CI：0.866 - 0.928）。
纳入重要术前和术中变量的模型对行CABG手术的老年患者血小板减少症具有良好的预测性能。
