Preoperative Serum Sodium Level as a Prognostic and Predictive Biomarker for Adjuvant Therapy in Esophageal Cancer

Association between preoperative serum sodium and postoperative 30-day mortality in adult patients with tumor craniotomy

BACKGROUND

Limited data exist regarding preoperative serum sodium (Na) and 30-day mortality in adult patients with tumor craniotomy. Therefore, this study investigates their relationship.

METHODS

A secondary retrospective analysis was performed using data from the ACS NSQIP database (2012-2015). The principal exposure was preoperative Na. The outcome measure was 30-day postoperative mortality. Binary logistic regression modeling was conducted to explore the link between them, and a generalized additive model and smooth curve fitting were applied to evaluate the potential association and its explicit curve shape. We also conducted sensitivity analyses and subgroup analyses.

RESULTS

A total of 17,844 patients (47.59% male) were included in our analysis. The mean preoperative Na was 138.63 ± 3.23 mmol/L. The 30-day mortality was 2.54% (455/17,844). After adjusting for covariates, we found that preoperative Na was negative associated with 30-day mortality. (OR = 0.967, 95% CI:0.941, 0.994). For patients with Na ≤ 140, each increase Na was related to a 7.1% decreased 30-day mortality (OR = 0.929, 95% CI:0.898, 0.961); for cases with Na > 140, each increased Na unit was related to a 8.8% increase 30-day mortality (OR = 1.088, 95% CI:1.019, 1.162). The sensitivity analysis and subgroup analysis indicated that the results were robust.

CONCLUSIONS

This study shows a positive and nonlinear association between preoperative Na and postoperative 30-day mortality in adult patients with tumor craniotomy. Appropriate preoperative Na management and maintenance of serum Na near the inflection point (140) may reduce 30-day mortality.

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

Albumin-To-Alkaline Phosphatase Ratio as a Novel and Promising Prognostic Biomarker in Patients Undergoing Esophagectomy for Carcinoma: A Propensity Score Matching Study

Background

Albumin-to-alkaline phosphatase ratio (AAPR) has been reported as a novel prognostic predictor for numerous solid tumors. We aimed to assess the prognostic role of preoperative AAPR in surgically resectable esophageal squamous cell carcinoma (ESCC) by a propensity score matching (PSM) analysis with predictive nomograms.

Methods

Our study was conducted in a single-center prospective database between June 2009 and December 2012. Kaplan-Meier analysis was used to distinguish the difference in survival outcomes between patients stratified by an AAPR threshold. Multivariable Cox proportional hazards regression model was finally generated to specify independent prognostic markers for the entire and PSM cohorts.

Results

A total of 497 patients with ESCC were included in this study. An AAPR of 0.50 was determined as the optimal cutoff point for prognostic outcome stratification. Patients with AAPR<0.50 had significantly worse overall survival (OS), and progression-free survival (PFS) compared to those with AAPR≥0.50 (Log-rank P<0.001). This significant difference remained stable in the PSM analysis. Multivariable analyses based on the entire and PSM cohorts consistently showed that AAPR<0.50 might be one of the most predominant prognostic factors resulting in unfavorable OS and PFS of ESCC patients undergoing esophagectomy (P<0.001). The nomograms consisting of AAPR and other independent prognostic factors further demonstrated a plausible predictive accuracy of postoperative OS and PFS.

Conclusion

AAPR can be considered as a simple, convenient and noninvasive biomarker with a significant prognostic effect in surgically resected ESCC.