Your browser doesn't support javascript.
loading
Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study.
Liu, Leiyuan; Che, Wenqiang; Xu, Bingdong; Liu, Yujun; Lyu, Jun; Zhang, Yusheng.
Afiliación
  • Liu L; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Che W; Department of Neurology, The First Clinical Medical School of Jinan University, Guangzhou, China.
  • Xu B; Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Liu Y; Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Lyu J; Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Zhang Y; Department of Neurology, The First Clinical Medical School of Jinan University, Guangzhou, China.
Neurosurg Rev ; 47(1): 296, 2024 Jun 26.
Article en En | MEDLINE | ID: mdl-38922516
ABSTRACT
In previous literatures, we found that similar studies on the short-term prognosis of synchronous brain metastases (S-BM) from other systems are rare. Our aim was to evaluate the early mortality rate of patients with S-BM from the Surveillance, Epidemiology, and End Result (SEER) database and explore the risk factors for early mortality (≤ 1 year). We used Kaplan-Meier (KM) curves to evaluate early mortality in patients with S-BM from the SEER database. Logistic regression analyses were used to identify significant independent prognostic factors in patients with a follow-up time > 12 months. And the meaningful factors were used to construct a nomogram of overall early death. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, while the decision curve analysis (DCA) curve was used to validate the clinical application ability of the model. A total of 47,284 patients were used for univariate and multivariate logistic regression analysis to screen variables to constructing a nomogram. In the all-cause early mortality specific model, the area under the ROC (AUC) curve of the training set was 0.764 (95% confidence interval (CI) 0.758-0.769), and the AUC of the validation set was 0.761 (95% CI 0.752-0.770). The DCA calibration curves of the training set and validation set indicate that the 1-year early mortality rate predicted by this model is consistent with the actual situation. We found that the 1-year early mortality rate was 76.4%. We constructed a validated nomogram using these covariates to effectively predict 1-year early mortality in patients with S-BM. This nomogram can help clinical workers screen high-risk patients to develop more reasonable treatment plans.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Nomogramas Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Neurosurg Rev Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Nomogramas Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Neurosurg Rev Año: 2024 Tipo del documento: Article País de afiliación: China