Early Death Incidence and Prediction in Stage IV Breast Cancer

Abstract

Background The early death of patients is a global cancer issue. We aimed to identify the risk factors for early death in stage IV breast cancer. Predictive nomograms for early death evaluation were generated based on the risk factors. Material/Methods Based on the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with IV breast cancer were selected. The risk factors for early death (survival time ≤1 year) were identified using logistic regression model analysis. Predictive nomograms were constructed and internal validation was performed. Results A total of 5998 (32.6%) breast cancer patients were diagnosed as early death in the construction cohort. Age older than 50 years, unmarried status, black race, uninsured status, triple-negative type, grade (II and III), tumor size >5 cm, and metastasis to lung, liver, and brain were risk factors for total early death, while Luminal B subtype, N1 stage, and surgical interventions were associated with lower risk of early death. As for cancer-specific and non-cancer-specific early death, several factors were not consistent between the 2 groups. Nomograms for all-cause, cancer-specific, and non-cancer-specific early death were constructed. The calibration curve showed satisfactory agreement. The areas under the ROC curve (AUC) were 78.3% (95% CI: 77.7–78.9%), 75.8% (75.1–76.4%), and 72.3% (71.6–72.9%), respectively. In the validation cohort, a total of 689 (19.3%) patients were diagnosed as early death and the calibration curve showed satisfactory agreement. The AUCs of the all-cause, cancer-specific, and non-cancer-specific early death prediction were 74.0% (95% CI: 72.5–75.4%), 73.5% (72.0–74.9%), and 68.6% (67.0–70.1%), respectively. Conclusions Nomograms were generated to predict early death, with good calibration and discrimination. The predictive model can provide a reference for identifying cases with high risk of early death among stage IV breast cancer patients and play an auxiliary role in guiding individual treatment.

The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database

“…All statistical analysis below was performed using the R programming language and environment ( http://www.r-project.org/ ). The “regplot” software package was used to construct a nomogram of independent factors predicting early death of patients with LCBM [ 14 ]. For calibration, the nomogram predicted probabilities were contrasted with the actual probabilities by bootstrapping with 1000 resamples.…”

A Predictive Nomogram for Early Death in Pheochromocytoma and Paraganglioma

“…Patients diagnosed in the current era have more access to health care besides the advancement in the health system worldwide and patient care. In addition, the patients who are married and insured will have more support mentally and financially (21,22). Nomograms are effective tools in quantifying risk factors with subsequent risk stratification and prediction in multiple cancers (21,(23)(24)(25)(26).…”

“…In addition, the patients who are married and insured will have more support mentally and financially (21,22). Nomograms are effective tools in quantifying risk factors with subsequent risk stratification and prediction in multiple cancers (21,(23)(24)(25)(26). They are supposed to provide clinicians with a feasible screening tool to improve clinical outcomes, decisionmaking, and treatment regimens for PPGL patients.…”

Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study

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