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Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

列线图 医学 置信区间 接收机工作特性 内科学 一致性 肿瘤科 比例危险模型 腺癌 队列 曲线下面积 人口 危险系数 外科 癌症 环境卫生
作者
Bo Wu,Jianhui Chen,Xiang Zhang,Nan Feng,Zhongtian Xiang,Yiping Wei,Junping Xie,Wenxiong Zhang
出处
期刊:Medicine [Wolters Kluwer]
卷期号:101 (49): e32217-e32217 被引量:10
标识
DOI:10.1097/md.0000000000032217
摘要

The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability.
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