Gao Ruize, Xu Fei, Song Yuntang, Ke Shan, Kong Jian, Wang Shaohong, Sun Wenbing, Gao Jun
Department of Interventional Radiology, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, 100043, People's Republic of China.
Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, 100043, People's Republic of China.
J Pain Res. 2025 Apr 9;18:1909-1921. doi: 10.2147/JPR.S510668. eCollection 2025.
To develop a least absolute shrinkage and selection operator (LASSO) logistic regression to predict postoperative severe pain after thermal ablation of hepatic hemangioma (HH).
From January 2014 to March 2024, 285 patients with HH treated by thermal ablation were retrospectively recruited. Forty-seven patients with postoperative severe pain [visual analogue scale (VAS) score ≥ 5] were matched 1:2 with 94 patients with mild pain (VAS score < 5). The LASSO and multivariate logistic regression identified independent risk factors for severe pain after thermal ablation for HH. The model's performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed using the Bootstrap method.
The ablation time (OR = 1.070, p = 0.046), postoperative levels of aspartate aminotransferase (AST) (OR = 1.012, p < 0.001), lactate dehydrogenase (LDH) (OR = 1.009, p = 0.001), neutrophil to lymphocyte ratio (NLR) (OR = 1.266, p = 0.034) were independent risk factors of severe pain. The model's area under the curve (AUC) = 0.985 (95% CI, 0.971-0.998). After internal verification by the Bootstrap method, the model still had a high discriminative ability (AUC = 0.979, 95% CI, 0.971-0.985). The calibration curve illustrated good agreement between the predicted and observed probability of severe pain. DCA verified that the model possesses significant predictive value.
Our nomogram predicts postoperative severe pain for HH with good discrimination and calibration based on the easily available risk factors.
开发一种最小绝对收缩与选择算子（LASSO）逻辑回归模型，以预测肝血管瘤（HH）热消融术后的严重疼痛。
回顾性纳入2014年1月至2024年3月期间接受热消融治疗的285例HH患者。将47例术后出现严重疼痛[视觉模拟评分（VAS）≥5分]的患者与94例轻度疼痛（VAS评分<5分）的患者按1:2进行匹配。采用LASSO和多因素逻辑回归确定HH热消融术后严重疼痛的独立危险因素。使用受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）评估模型性能。采用Bootstrap法进行内部验证。
消融时间（OR = 1.070，p = 0.046）、术后天冬氨酸氨基转移酶（AST）水平（OR = 1.012，p < 0.001）、乳酸脱氢酶（LDH）水平（OR = 1.009，p = 0.001）、中性粒细胞与淋巴细胞比值（NLR）（OR = 1.266，p = 0.034）是严重疼痛的独立危险因素。模型的曲线下面积（AUC）= 0.985（95%CI，0.971 - 0.998）。经Bootstrap法内部验证后，模型仍具有较高的判别能力（AUC = 0.979，95%CI，0.971 - 0.985）。校准曲线显示预测的严重疼痛概率与观察到的概率之间具有良好的一致性。DCA验证该模型具有显著的预测价值。
我们的列线图基于易于获得的危险因素，对HH术后严重疼痛具有良好的判别能力和校准效果。
