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    生长激素型垂体腺瘤经鼻蝶入路术后长期疗效的列线图预测模型的构建和验证

    Development and validation of a nomogram prediction model for long-term efficacy of growth hormone-type pituitary adenoma patients after transsphenoidal endoscopic surgery

    • 摘要: 目的 分析生长激素(GH)型垂体腺瘤患者神经内镜下经鼻蝶入路手术后长期预后因素,建立GH型垂体腺瘤术后的列线图预测模型。方法 选取2014年1月—2020年1月于徐州医科大学附属医院经鼻蝶手术治疗的GH型垂体腺瘤患者作为研究对象,收集临床资料进行分析。运用LASSO回归分析筛选最佳预测因子,将预测因子进行多因素Logistic回归分析进一步筛选,构建列线图预测模型。采用一致性指数(C-index)、受试者工作特征曲线(ROC)、校准曲线以及临床决策曲线(DCA)从区分度、校准度和临床效用3个方面验证该模型的性能。结果 共连续纳入96例GH型垂体腺瘤患者。在最后的随访中,61例(63.5%)达到缓解标准。以影响术后缓解的4个危险因素,即术前随机GH、术前胰岛素生长因子(IGF-1)、Knosp分级和肿瘤最大直径(MPAD)构建术后长期未缓解的预测模型。该列线图模型的 C-index为0.806,提示该模型的区分度较高,ROC曲线下面积AUC为0.897 95%CI (0.828, 0.967),灵敏度为97.1%,特异度为73.8%。校准曲线证实该模型预测结果与观察结果吻合较好(P=0.615)。DCA 决策曲线显示该模型具有较好的临床效用。结论 术前随机GH、术前IGF-1、Knosp分级和MPAD是预测GH型垂体腺瘤患者经鼻蝶术后长期疗效的独立预测因素。该列线图预测模型显示良好的区分度和核准度,可以帮助临床医生预测术后缓解情况疗效,指导制定个体化的随访计划和治疗方案。

       

      Abstract: Objective To analyze the long-time prognostic factors of patients with growth hormone (GH)-type pituitary adenoma after transsphenoidal endoscopic surgery, and establish a nomogram prediction model for GH-type pituitary adenoma. Methods Patients with GH-type pituitary adenoma who were admitted to the Affiliated Hospital of Xuzhou Medical University from January 2014 to January 2020 were enrolled and their clinical data were analyzed. The optimal predictors were screened by LASSO regression analysis, and the risk predictors were further screened by multivariate Logistic regression analysis. A nomogram prediction model was established based on risk predictors. The discriminative performance of the model was evaluated by the area under the receiver operating curve (ROC), and the calibration curve of the model was tested using Hosmer-Lemeshow goodness of fit test. The calibration curve was evaluated by the discriminant consistency index (C-index) and internal validation of the bootstrap strategy. The decision curve analysis (DCA) was performed to assess the clinical validity of the model. Results A total of 96 patients with GH-type pituitary adenoma were consecutively included. At the final follow-up, 61 patients (63.5%) met the remission criteria. A prediction model for long-term non-remission after surgery was constructed based on four risk factors affecting postoperative remission (preoperative random GH, preoperative IGF-1, Knosp grade and MPAD). The area under the ROC curve of the model was 0.897 95%CI (0.828, 0.967), indicating that the model had good discriminative performance. The calibration curve of the model showed the similar trend with the actual prediction curve, after the Hosmer-Lemeshow goodness of fit test (P=0.615). The internal verification of the prediction model through the Bootstrap method showed that the one-time index (C-index) was 0.930, indicating that the model had a high degree of discrimination. Finally, a decision curve analysis (DCA) of the predictive model was plotted to determine the clinical validity of the model. Conclusions Preoperative random GH, preoperative IGF-1, Knosp grade, and MPAD are the independent predictors of long-term remission after transsphenoidal endscopic surgery in patients with GH-type pituitary adenomas. The constructed nomogram prediction model shows good discrimination performance and precision performance, which is helpful to predict postoperative remission and guide the development of individualized follow-up plans and treatment regimens.

       

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