Wei Qian-Qian, Chen Yongping, Chen Xueping, Cao Bei, Ou RuWei, Zhang Lingyu, Hou Yanbing, Shang Huifang
Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Aging Dis. 2018 Dec 4;9(6):965-975. doi: 10.14336/AD.2017.1016. eCollection 2018 Dec.
Better understanding of survival factors in amyotrophic lateral sclerosis (ALS) could help physicians and patients schedule therapeutic interventions. We conducted a study to evaluate the predictive factors associated with longer survival and construct prognostic nomogram in ALS patients. A total of 553 ALS patients were enrolled and divided into 2 groups: a training set and a validation set. Risk factors for survival were identified using logistic regression analysis, and a nomogram created by R program was performed to predict the probability of longer survival in the training set; then receiver operating characteristic (ROC) analysis was applied to assess predictive value of the nomogram model. The median survival time was 3.2 years for all patients. Multivariate analyses revealed that age of onset, rate of disease progression, hemoglobin A1c (HbA1c) level, body mass index, creatinine, creatine kinase (CK), and non-invasive positive pressure ventilation (NIPPV) were independent predictors of longer survival. A nomogram based on the above seven predictive factors was developed to predict the possibility of longer survival. The ROC curve of the nomogram demonstrated good discrimination ability with an AUC of 0.92 (95% CI: 0.88-0.96) in the validation set. In ALS, serum CK, creatinine and HbA1c levels at baseline were independent biomarkers of longer survival. The prognostic nomogram model that integrated all significant independent factors for those who survived longer than 3 years provides an effective way to predict the probability of longer survival and can help doctors evaluate the disease progression and give personalized treatment recommendations.
更好地了解肌萎缩侧索硬化症(ALS)的生存因素有助于医生和患者安排治疗干预措施。我们开展了一项研究,以评估与较长生存期相关的预测因素,并构建ALS患者的预后列线图。共纳入553例ALS患者,分为两组:训练集和验证集。使用逻辑回归分析确定生存的危险因素,并通过R程序创建列线图以预测训练集中较长生存期的概率;然后应用受试者工作特征(ROC)分析来评估列线图模型的预测价值。所有患者的中位生存期为3.2年。多因素分析显示,发病年龄、疾病进展速率、糖化血红蛋白(HbA1c)水平、体重指数、肌酐、肌酸激酶(CK)和无创正压通气(NIPPV)是较长生存期的独立预测因素。基于上述七个预测因素开发了一个列线图,以预测较长生存期的可能性。列线图的ROC曲线在验证集中显示出良好的区分能力,AUC为0.92(95%CI:0.88 - 0.96)。在ALS中,基线时的血清CK、肌酐和HbA1c水平是较长生存期的独立生物标志物。整合了所有显著独立因素的预后列线图模型为生存期超过3年的患者提供了一种有效的方法来预测较长生存期的概率,并可帮助医生评估疾病进展并给出个性化的治疗建议。