Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, People's Republic of China.
National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha 410008, Hunan, People's Republic of China.
J Clin Endocrinol Metab. 2024 Jan 18;109(2):351-360. doi: 10.1210/clinem/dgad543.
Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL).
We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome.
A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves.
The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed.
The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.
术中血流动力学不稳定（HDI）可导致嗜铬细胞瘤/副神经节瘤（PPGL）手术期间发生心血管和脑血管并发症。
我们旨在评估 PPGL 患者术中发生 HDI 的风险，以改善手术结果。
本研究回顾性纳入了 199 例经手术病理证实的 PPGL 患者。根据术中收缩压，将该队列分为 2 组，即 HDI 组（n = 101）和血流动力学稳定（HDS）组（n = 98）。此外，还将其分为 2 个预测建模子队列：训练队列（n = 140）和验证队列（n = 59）。使用 CT 上的身体成分参数、肿瘤放射组学和临床数据，使用集成机器学习方法（EL 模型）和多变量逻辑回归模型建立预测模型。使用判别、校准和决策曲线评估模型的效率。
EL 模型在 HDI 组和 HDS 组之间显示出良好的判别能力，在训练队列中的曲线下面积（AUC）为 96.2%（95%CI，93.5%-99.0%），在验证队列中的 AUC 为 93.7%（95%CI，88.0%-99.4%）。EL 模型的 AUC 值明显高于逻辑回归模型，在训练队列中的 AUC 为 74.4%（95%CI，66.1%-82.6%），在验证队列中的 AUC 为 74.2%（95%CI，61.1%-87.3%）。EL 模型具有良好的校准性能和临床适用性。
结合术前 CT 基于的身体成分、肿瘤放射组学和临床数据的 EL 模型，可能有助于预测 PPGL 患者术中发生 HDI。
