Construction and validation of a risk nomogram prediction model for myasthenia gravis in patients after thymoma resection
10.3760/cma.j.cn115682-20210906-04029
- VernacularTitle:胸腺瘤切除术后患者发生重症肌无力的风险列线图预测模型构建与验证
- Author:
Xiaohuan HEI
1
;
Dan LU
;
Xiaofei WANG
Author Information
1. 首都医科大学附属北京同仁医院胸外科，北京 100730
- Keywords:
Thymoma;
Myasthenia gravis;
Risk factors;
Nomogram;
Prediction model
- From:
Chinese Journal of Modern Nursing
2022;28(21):2884-2890
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a risk nomogram early prediction model for myasthenia gravis (MG) after thymoma resection and to verify the predictive performance of the model.Methods:Using the convenient sampling method, a total of 477 patients undergoing thymoma resection who were admitted to Department of Thoracic Surgery in Beijing Tongren Hospital Affiliated to Capital Medical University from March 2018 to February 2021 were selected as the training set. A total of 62 patients who underwent thymoma resection from March 2017 to February 2018 were set as the validation set for retrospective analysis. The clinical data of the included patients were analyzed. Logistic regression analysis was used to explore the independent risk factors of MG after thymoma resection and a risk nomogram prediction model was constructed.Results:The incidence of postoperative MG in 477 patients who underwent thymoma resection was confirmed by chest X-ray, CT and related tests, which was 14.05% (67/477) . There were statistical differences between the two groups of patients with MG and those without MG in terms of combination of immune diseases, preoperative course of disease, surgical route, complete tumor resection, WHO pathological classification, postoperative pulmonary infection, postoperative radiotherapy and chemotherapy and other data ( P<0.05) . Logistic regression analysis showed that combined immune disease, thoracotomy, incomplete tumor resection, WHO pathological classification of A+AB, postoperative pulmonary infection and no postoperative chemoradiotherapy were independent risk factors for MG after thymoma resection ( P<0.05) . Based on 6 independent risk factors, a risk nomogram prediction model of MG after thymoma resection was established. The results showed that the C- index of the training set and the validation set were 0.837 (95% CI: 0.807-0.867) and 0.817 (95% CI: 0.807-0.867) , respectively. The calibration curves for both sets showed good fit to the ideal curve, with areas under the receiver operating characteristic curves of 0.834 (95% CI: 0.794-0.874) and 0.825 (95% CI: 0.789-0.861) , respectively. Conclusions:Combination of immune diseases, surgical approach of thoracotomy, incomplete tumor resection, WHO pathological classification of A+AB, postoperative pulmonary infection and no postoperative radiotherapy and chemotherapy are independent risk factors for MG after thymoma resection. The risk nomogram prediction model established based on the above risk factors can accurately assess and quantify the risk of MG after thymoma resection, which has good predictive ability.
