Combination of C-reactive protein/albumin ratio and time to castration resistance enhances prediction of prognosis for patients with metastatic castration-resistant prostate cancer.
Front Oncol
; 13: 1162820, 2023.
Article
em En
| MEDLINE
| ID: mdl-37333829
ABSTRACT
Objective:
This study aimed to identify the prediction accuracy of the combination of C-reactive protein (CRP) albumin ratio (CAR) and time to castration resistance (TTCR) for overall survival (OS) following development of metastatic castration-resistant prostate cancer (mCRPC).Methods:
Clinical data from 98 mCRPC patients treated at our institution from 2009 to 2021 were retrospectively evaluated. Optimal cutoff values for CAR and TTCR to predict lethality were generated by use of a receiver operating curve and Youden's index. The Kaplan-Meier method and Cox proportional hazard regression models for OS were used to analyze the prognostic capabilities of CAR and TTCR. Multiple multivariate Cox models were then constructed based on univariate analysis and their accuracy was validated using the concordance index.Results:
The optimal cutoff values for CAR at the time of mCRPC diagnosis and TTCR were 0.48 and 12 months, respectively. Kaplan-Meier curves indicated that patients with CAR >0.48 or TTCR <12 months had a significantly worse OS (both p < 0.005). Univariate analysis also identified age, hemoglobin, CRP, and performance status as candidate prognostic factors. Furthermore, a multivariate analysis model incorporating those factors and excluding CRP showed CAR and TTCR to be independent prognostic factors. This model had better prognostic accuracy as compared with that containing CRP instead of CAR. The results showed effective stratification of mCRPC patients in terms of OS based on CAR and TTCR (p < 0.0001).Conclusion:
Although further investigation is required, CAR and TTCR used in combination may more accurately predict mCRPC patient prognosis.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article