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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
The Nomogram for Early Death in Patients with Bone and Soft Tissue Tumors
Yao Xu1, Guijun Xu1,2, Haixiao Wu1
1Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Abstract:
Objectives: The present study aimed to evaluate the early mortality rate and associated factors for early death in bone and soft tissue tumors, and to construct predictive nomogram. Methods: Patients diagnosed between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) dataset were enrolled. The early death (survival time ≤ 3 months) rate was calculated and associated risk factors were evaluated by the logistic regression models. The significant factors were used to construct predictive nomograms. Results: A total of 2,003 (8.5%) patients died within 3 months after cancer diagnosis, among whom 1,146 (4.9%) patients died from cancer-specific cause. Older age (19-50 and >50 years), grade (III and IV), reginal or distant stage were associated with higher odds of total, cancer-specific and non-cancer-specific early death. T2 stage, metastasis to brain and lung were risk factors for total and cancer-specific early death. Surgical interventions significantly decreased the odds of total, cancer-specific and non-cancer-specific early death. Female and black race were associated with lower odds of non-cancer-specific early death. The area under the curve (AUC) of the nomograms for total early death, cancer-specific and non-cancer-specific early death prediction was 88.0%, 89.0% and 83.2%, respectively. Conclusions: A total of 8.5% patients with bone and soft tissue tumors suffered early death. Several risk factors were associated with higher odds of early death while surgery can decrease the possibility of early death. Nomograms based on all related factors can be used to estimate the early death in bone and soft tissue tumors.
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