A predictive web-based nomogram for the early death of patients with lung adenocarcinoma and bone metastasis: a population-based study. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Li, Zhehong; Wei, Junqiang; Cao, Haiying; Song, Mingze; Zhang, Yafang; Jin, Yu
• Is Part Of: Journal of international medical research. Volume 49:Issue 9(2021)
• Publication Date: 2021-09
• Subjects: Lung adenocarcinoma -- bone metastasis -- early death -- web-based nomogram -- Surveillance -- Epidemiology -- and End Results -- risk factor; Medicine -- Periodicals; Pharmacology -- Periodicals; 610.5
• DOI: 10.1177/03000605211047771
• Languages: English
• ISSNs: 0300-0605

Abstract

Objective: To identify risk factors and develop predictive web-based nomograms for the early death of patients with bone metastasis of lung adenocarcinoma (LUAD). Methods: Patients in the Surveillance, Epidemiology, and End Results database diagnosed with bone metastasis of LUAD between 2010 and 2016 were included and randomly divided into training and validation sets. Early death-related risk factors (survival time ≤7 months) were evaluated by logistic regression. Two predictive nomograms were established and validated by calibration curves, receiver operating characteristic curves, and decision curve analysis. Results: A total of 9189 patients (56.59%) died from all causes within 7 months of being diagnosed, including 8585 patients (56.67%) who died from cancer-specific causes. Age >65 years, sex (men), T stage (T3 and T4), N stage (N2 and N3), brain metastasis, and liver metastasis were risk factors for all-cause and cancer-specific early death. The area under the curves of the nomograms for all-cause and cancer-specific early death prediction were 0.754 and 0.753 (training set) and 0.747 and 0.754 (validation set), respectively. Further analysis showed that the two nomograms performed well. Conclusions: Our two web-based nomograms for all-cause and cancer-specific early death provide valuable tools for predicting early death in these patients.