Albumin-to-alkaline phosphatase ratio serves as a prognostic indicator in unresectable pancreatic ductal adenocarcinoma: a propensity score matching analysis.


Journal

BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800

Informations de publication

Date de publication:
09 Jun 2020
Historique:
received: 30 03 2020
accepted: 28 05 2020
entrez: 11 6 2020
pubmed: 11 6 2020
medline: 1 1 2021
Statut: epublish

Résumé

Recent evidence suggests that albumin-to-Alkaline Phosphatase Ratio (AAPR) functions as a novel prognostic marker in several malignancies. However, whether it can predict the prognosis of unresectable pancreatic ductal adenocarcinoma (PDAC) remains unclear. Herein, we seek to investigate this possibility by a propensity score matching (PSM) analysis. This was a retrospective cohort study in which 419 patients diagnosed with unresectable PDAC and receiving chemotherapy were recruited. Patients were stratified based on the cutoff value of AAPR. The PSM analysis was performed to identify 156 well-balanced patients in each group for overall survival (OS) comparison and subgroup analysis. Univariate and multivariate analyses were carried out to examine the potential of AAPR to indicate the prognosis of unresectable PDAC. The prediction performance of conventional model and combined model including AAPR was compared using the Akaike Information Criterion (AIC) and concordance index (C-index). We identified an AAPR of 0.4 to be the optimal cutoff for OS prediction. Patients with AAPR≤0.4 had significantly shorter OS compared with patients with AAPR> 0.4 (6.4 versus 9.3 months; P < 0.001). Based on the PSM cohort and entire cohort, multivariate Cox analysis revealed that high pretreatment for AAPR was an independent marker predicting favorable survival in unresectable PDAC (hazard ratio, 0.556; 95% confidence interval, 0.408 to 0.757; P < 0.001). Significant differences in OS were observed in all subgroups except for the group of patients age ≤ 60. Combined prognostic model including AAPR had lower AIC and higher C-index than conventional prognostic model. Pretreatment AAPR servers as an independent prognostic indicator for patients with unresectable PDAC. Inclusion of AAPR improved the prediction performance of conventional prognostic model, potentially helping clinicians to identify patients at high risk and guide individualized treatment.

Sections du résumé

BACKGROUND BACKGROUND
Recent evidence suggests that albumin-to-Alkaline Phosphatase Ratio (AAPR) functions as a novel prognostic marker in several malignancies. However, whether it can predict the prognosis of unresectable pancreatic ductal adenocarcinoma (PDAC) remains unclear. Herein, we seek to investigate this possibility by a propensity score matching (PSM) analysis.
METHODS METHODS
This was a retrospective cohort study in which 419 patients diagnosed with unresectable PDAC and receiving chemotherapy were recruited. Patients were stratified based on the cutoff value of AAPR. The PSM analysis was performed to identify 156 well-balanced patients in each group for overall survival (OS) comparison and subgroup analysis. Univariate and multivariate analyses were carried out to examine the potential of AAPR to indicate the prognosis of unresectable PDAC. The prediction performance of conventional model and combined model including AAPR was compared using the Akaike Information Criterion (AIC) and concordance index (C-index).
RESULTS RESULTS
We identified an AAPR of 0.4 to be the optimal cutoff for OS prediction. Patients with AAPR≤0.4 had significantly shorter OS compared with patients with AAPR> 0.4 (6.4 versus 9.3 months; P < 0.001). Based on the PSM cohort and entire cohort, multivariate Cox analysis revealed that high pretreatment for AAPR was an independent marker predicting favorable survival in unresectable PDAC (hazard ratio, 0.556; 95% confidence interval, 0.408 to 0.757; P < 0.001). Significant differences in OS were observed in all subgroups except for the group of patients age ≤ 60. Combined prognostic model including AAPR had lower AIC and higher C-index than conventional prognostic model.
CONCLUSIONS CONCLUSIONS
Pretreatment AAPR servers as an independent prognostic indicator for patients with unresectable PDAC. Inclusion of AAPR improved the prediction performance of conventional prognostic model, potentially helping clinicians to identify patients at high risk and guide individualized treatment.

Identifiants

pubmed: 32517802
doi: 10.1186/s12885-020-07023-9
pii: 10.1186/s12885-020-07023-9
pmc: PMC7285790
doi:

Substances chimiques

Biomarkers, Tumor 0
Serum Albumin 0
Alkaline Phosphatase EC 3.1.3.1

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

541

Subventions

Organisme : National Nature Science Foundation of China
ID : 81930115
Organisme : National Nature Science Foundation of China
ID : 81973616

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Auteurs

Ke Zhang (K)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Shu Dong (S)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Yan-Hua Jing (YH)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Hui-Feng Gao (HF)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Lian-Yu Chen (LY)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Yong-Qiang Hua (YQ)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.

Hao Chen (H)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. chengkll@sina.com.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. chengkll@sina.com.

Zhen Chen (Z)

Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zchenzl@fudan.edu.cn.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. zchenzl@fudan.edu.cn.

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Classifications MeSH