Liu Hong, Qian Si-Chong, Han Lu, Zhang Ying-Yuan, Wu Ying, Hong Liang, Yang Ji-Nong, Zhong Ji-Sheng, Wang Yu-Qi, Wu Dong-Kai, Fan Guo-Liang, Chen Jun-Quan, Zhang Sheng-Qiang, Peng Xing-Xing, Tang Zhi-Wei, Hamzah Al-Wajih, Shao Yong-Feng, Li Hai-Yang, Zhang Hong-Jia
Department of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P.R. China.
Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, P.R. China.
Eur Heart J Digit Health. 2022 Nov 1;3(4):587-599. doi: 10.1093/ehjdh/ztac068. eCollection 2022 Dec.
The incremental usefulness of circulating biomarkers from different pathological pathways for predicting mortality has not been evaluated in acute Type A aortic dissection (ATAAD) patients. We aim to develop a risk prediction model and investigate the impact of arch repair strategy on mortality based on distinct risk stratifications.
A total of 3771 ATAAD patients who underwent aortic surgery retrospectively included were randomly divided into training and testing cohorts at a ratio of 7:3 for the development and validation of the risk model based on multiple circulating biomarkers and conventional clinical factors. Extreme gradient boosting was used to generate the risk models. Subgroup analyses were performed by risk stratifications (low vs. middle-high risk) and arch repair strategies (proximal vs. extensive arch repair). Addition of multiple biomarkers to a model with conventional factors fitted an ABC risk model consisting of platelet-leucocyte ratio, mean arterial pressure, albumin, age, creatinine, creatine kinase-MB, haemoglobin, lactate, left ventricular end-diastolic dimension, urea nitrogen, and aspartate aminotransferase, with adequate discrimination ability {area under the receiver operating characteristic curve (AUROC): 0.930 [95% confidence interval (CI) 0.906-0.954] and 0.954, 95% CI (0.930-0.977) in the derivation and validation cohort, respectively}. Compared with proximal arch repair, the extensive repair was associated with similar mortality risk among patients at low risk [odds ratio (OR) 1.838, 95% CI (0.559-6.038); = 0.316], but associated with higher mortality risk among patients at middle-high risk [OR 2.007, 95% CI (1.460-2.757); < 0.0001].
In ATAAD patients, the simultaneous addition of circulating biomarkers of inflammatory, cardiac, hepatic, renal, and metabolic abnormalities substantially improved risk stratification and individualized arch repair strategy.
在急性A型主动脉夹层(ATAAD)患者中,尚未评估来自不同病理途径的循环生物标志物对预测死亡率的增量效用。我们旨在开发一种风险预测模型,并基于不同的风险分层研究主动脉弓修复策略对死亡率的影响。
回顾性纳入3771例行主动脉手术的ATAAD患者,按照7:3的比例随机分为训练队列和测试队列,用于基于多种循环生物标志物和传统临床因素开发和验证风险模型。采用极端梯度提升法生成风险模型。通过风险分层(低风险与中高风险)和主动脉弓修复策略(近端主动脉弓修复与广泛主动脉弓修复)进行亚组分析。在包含传统因素的模型中加入多种生物标志物,构建了一个ABC风险模型,该模型由血小板-白细胞比率、平均动脉压、白蛋白、年龄、肌酐、肌酸激酶-MB、血红蛋白、乳酸、左心室舒张末期内径、尿素氮和天冬氨酸转氨酶组成,具有良好的区分能力{受试者操作特征曲线下面积(AUROC):在推导队列和验证队列中分别为0.930 [95%置信区间(CI)0.906 - 0.954]和0.954, 95% CI((0.930 - 0.977)}。与近端主动脉弓修复相比,广泛修复在低风险患者中的死亡风险相似[比值比(OR)1.838, 95% CI(0.559 - 6.038);P = 0.316],但在中高风险患者中与更高的死亡风险相关[OR 2.007, 95% CI(1.460 - 2.757);P < 0.0001]。
在ATAAD患者中,同时加入炎症、心脏、肝脏、肾脏和代谢异常的循环生物标志物可显著改善风险分层和个体化主动脉弓修复策略。