Han Xuechang, Wang Shuang, Cai Runlu, Chen Qiang, Li Jing, Zhong Liang, Ji Shuman, Mei Xiaopeng, Wu Rongqian, Yan Yang, Lv Yi, Zhang Zhanqin
Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Anesthesiology, The First Affiliated Hospital and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Heliyon. 2024 Jun 5;10(12):e32474. doi: 10.1016/j.heliyon.2024.e32474. eCollection 2024 Jun 30.
Acute aortic dissection (AAD) is an extremely life-threatening medical emergency, often misdiagnosed in its early stages, resulting in prolonged wait times for rescue. This study aims to identify potential serum biomarkers that can assist in the accurate diagnosis of AAD and effectively differentiate it from other conditions causing severe chest pain.
A total of 122 patients with AAD and 129 patients with other severe chest pain disorders were included in the study. Serum samples were analyzed by measuring the peak intensities of Raman spectra. For each measurement, the Raman spectrum was accumulated by two accumulations (3 s per acquisition). Logistic regression and nomogram models were developed using these peak intensities as well as D-dimer levels to predict the occurrence of AAD. The clinical utilities of these models were assessed through receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) in both training and internal test cohorts.
The D-dimer levels of AAD patients were significantly increased, as well as higher intensities at specific Raman peaks, including 505 cm, 842 cm, 947 cm, 1254 cm, 1448 cm, and 1655 cm when compared to non-AAD patients. Conversely, decreased intensities were observed at Raman peaks such as 750 cm, 1004 cm, 1153 cm, 1208 cm, and 1514 cm in AAD patients. Least absolute shrinkage and selection operator regression analysis on the training cohort identified eight potential predictors: D-dimer along with intensity measurements at peaks such as 505 cm, 750 cm, 1153 cm, 1208 cm, 1254 cm, 1448 cm, and 1655 cm. The combination of these eight potential predictors demonstrated a good discriminatory performance, with an area under the curve (AUC) value of 0.928 in the training cohort and an AUC of 0.936 in the internal test cohort, outperforming the use of D-dimer alone. Furthermore, DCA curve analysis revealed that leveraging this combination of eight potential predictors would provide substantial net benefits for clinical application. Moreover, this combination significantly augmented discrimination power, as evidenced by a continuous NRI of 39.8 % and IDI of 9.95 % in the training cohort, as well as a continuous NRI of 27.1 % and IDI of 9.95 % in the internal test cohort.
The employment of this combination of eight potential predictors effectively rules out AAD to a greater extent. This study presents a promising diagnostic strategy for early detection using optical diagnostic techniques such as Raman spectroscopy.
急性主动脉夹层（AAD）是一种极其危及生命的医疗急症，在早期常被误诊，导致救援等待时间延长。本研究旨在确定潜在的血清生物标志物，以协助准确诊断AAD，并有效将其与其他导致严重胸痛的病症区分开来。
本研究共纳入122例AAD患者和129例其他严重胸痛疾病患者。通过测量拉曼光谱的峰值强度来分析血清样本。每次测量时，拉曼光谱通过两次累加（每次采集3秒）进行累加。使用这些峰值强度以及D - 二聚体水平建立逻辑回归和列线图模型，以预测AAD的发生。通过在训练队列和内部测试队列中的受试者工作特征（ROC）曲线分析、净重新分类改善（NRI）、综合判别改善（IDI）和决策曲线分析（DCA）来评估这些模型的临床效用。
与非AAD患者相比，AAD患者的D - 二聚体水平显著升高，并且在特定拉曼峰处的强度更高，包括505cm、842cm、947cm、1254cm、1448cm和1655cm。相反，在AAD患者中，在750cm、1004cm、1153cm、1208cm和1514cm等拉曼峰处观察到强度降低。对训练队列进行最小绝对收缩和选择算子回归分析，确定了八个潜在预测因子：D - 二聚体以及在505cm、750cm、1153cm、1208cm、1254cm、1448cm和1655cm等峰处的强度测量值。这八个潜在预测因子的组合表现出良好的鉴别性能，训练队列中的曲线下面积（AUC）值为0.928，内部测试队列中的AUC为0.936，优于单独使用D - 二聚体。此外，DCA曲线分析表明，利用这八个潜在预测因子的组合将为临床应用提供显著的净效益。此外，这种组合显著增强了鉴别能力，训练队列中的连续NRI为39.8%，IDI为9.95%，内部测试队列中的连续NRI为27.1%，IDI为9.95%。
使用这八个潜在预测因子的组合能在更大程度上有效排除AAD。本研究提出了一种有前景的诊断策略，可利用拉曼光谱等光学诊断技术进行早期检测。
