Prognostic Significance of Preoperative Albumin to Alkaline Phosphatase Ratio in Patients with Glioblastoma

Albumin-to-alkaline phosphatase ratio as a novel prognostic indicator in patients undergoing peritoneal dialysis: a propensity score matching analysis

Background

Though the albumin-to-alkaline phosphatase ratio (AAPR) is used as a biomarker in various diseases, little is known about its effect on outcomes after peritoneal dialysis (PD).

Methods

This multicenter retrospective study comprised 357 incident PD patients stratified according to the AAPR. Propensity score matching (PSM) was performed to identify 85 patients for a well-matched comparison of all-cause and cardiovascular mortality. Using Cox regression, we performed univariate and multivariate analyses to investigate the prognostic value of the AAPR and established a Kaplan-Meier curve-predicted nomogram to estimate expected overall survival (OS). We assessed the predictive accuracy using the concordance index (c-index).

Results

We found that the optimal cut-off of the AAPR to predict mortality was 0.36. In the present cohort of patients undergoing PD, a low AAPR strongly correlated with worse OS. In the multivariate analysis, the AAPR was shown to be an independent marker predicting reduced OS both before [hazard ratio (HR) 1.68, 95% confidence interval (CI) 1.08-2.60, P = 0.020] and after PSM (HR 1.96, 95% CI 1.06-3.62, P = 0.020). We also observed significant differences in OS in several subgroups, but not the group of patients with comorbidities. A nomogram was established to predict overall survival, with a c-index for prediction accuracy was 0.71 after PSM.

Conclusion

AAPR has potential as an independent prognostic biomarker in patients undergoing PD.

Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients

Background

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients.

Methods

A total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a high PNI (≥53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusion

The pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients.

Interpretations of the Role of Plasma Albumin in Prognostic Indices: A Literature Review

This review assesses how publications interpret factors that influence the serum or plasma albumin (PA) level in prognostic indices, focusing on inflammation and nutrition. On PubMed, a search for "albumin AND prognosis" yielded 23,919 results. From these records, prognostic indices were retrieved, and their names were used as search strings on PubMed. Indices found in 10 or more original research articles were included. The same search strings, restricted to "Review" or "Systematic review", retrieved yielded on the indices. The data comprised the 10 latest original research articles and up to 10 of the latest reviews. Thirty indices had 294 original research articles (6 covering two indices) and 131 reviews, most of which were from recent years. A total of 106 articles related the PA level to inflammation, and 136 related the PA level to nutrition. For the reviews, the equivalent numbers were 54 and 65. In conclusion, more publications mention the PA level as a marker of nutrition rather than inflammation. This is in contrast to several general reviews on albumin and nutritional guidelines, which state that the PA level is a marker of inflammation but not nutrition. Hypoalbuminemia should prompt clinicians to focus on the inflammatory aspects in their patients.

Development and validation of a nomogram prediction model based on albumin-to-alkaline phosphatase ratio for predicting the prognosis of gallbladder carcinoma

Gallbladder carcinoma (GBC) is a rare biliary tract cancer with a high recurrence rate and a poor prognosis. Albumin-alkaline phosphatase ratio (AAPR) has been demonstrated to be a prognostic predictor for several cancers, but its predictive value for GBC patients remains unknown. The aim of this study was to investigate the predictive role of AAPR in GBC patients and to develop a novel nomogram prediction model for GBC patients. We retrospectively collected data from 80 patients who underwent surgery at the Hospital of 81st Group Army PLA as a training cohort. Data were collected from 70 patients with the same diagnosis who underwent surgery at the First Affiliated Hospital of Hebei North University as an external verification cohort. The optimal cut-off value of AAPR was determined using X-tile software. A nomogram for the overall survival (OS) based on multivariate Cox regression analysis was developed and validated using calibration curves, Harrell's concordance index, the receiver operating characteristic curves, and decisive curve analyses. The optimal cut-off value of AAPR was .20. Univariate and multivariate Cox regression analyses demonstrated that BMI (p = .043), R0 resection (p = .001), TNM stage (p = .005), and AAPR (p = .017) were independent risk factors for GBC patients. In terms of consistency, discrimination, and net benefit, the nomogram incorporating these four independent risk factors performed admirably. AAPR is an independent predictor of GBC patients undergoing surgery, and a novel nomogram prediction model based on AAPR showed superior predictive ability.

Albumin-to-Alkaline Phosphatase Ratio as a Prognostic Biomarker for Spinal Fusion in Lumbar Degenerative Diseases Patients Undergoing Lumbar Spinal Fusion

Objective: To determine if preoperative albumin-alkaline phosphatase ratio (AAPR) is predictive of clinical outcomes in patients with degenerative lumbar diseases undergoing lumbar fusion. Method: 326 patients undergoing posterior lumbar decompression and fusion were retrospectively analyzed. The cumulative grade was calculated by summing the Pfirrmann grades of all lumbar discs. Grouping was based on the 50th percentile of cumulative grade. The relationship between AAPR, intervertebral disc degeneration (IDD) severity, and fusion rate was explored using correlation analyses and logistic regression models. Meanwhile, the ROC curve evaluated the discrimination ability of AAPR in predicting severe degeneration and non-fusion. Results: High AAPR levels were significantly negatively correlated with severe degeneration and non-fusion rate. A multivariate binary logistic analysis revealed that high preoperative AAPR was an independent predictor of severe degeneration and postoperative non-fusion (OR: 0.114; 95% CI: 0.027−0.482; p = 0.003; OR: 0.003; 95% CI: 0.0003−0.022; p < 0.001). The models showed excellent discrimination and calibration. The areas under the curve (AUC) of severe degeneration and non-fusion identified by AAPR were 0.635 and 0.643. Conclusion: The AAPR can help predict the severity of disc degeneration and the likelihood of non-fusion.