Esophageal squamous cell carcinoma (ESCC) is a major global health issue, with postoperative hospital length of stay (LOS) being a critical factor influencing patient outcomes and healthcare costs. This study evaluates the impact of preoperative albumin-to-fibrinogen ratio (AFR) and albumin-to-D-II aggregates ratio (ADR) on LOS in patients with locally advanced ESCC undergoing neoadjuvant therapy. A retrospective study of 135 patients with locally advanced ESCC who underwent esophagectomy after neoadjuvant therapy (July 2013–November 2020). Demographic, clinical, and preoperative blood data were analyzed. LOS was defined from surgery to discharge. AFR and ADR values were calculated, and ROC curves identified optimal cutoffs. Multivariate Cox proportional hazards models and Kaplan–Meier analysis were used to assess relationships between AFR and LOS. The optimal AFR cutoff was 10.34, demonstrating better predictive accuracy for LOS than ADR. High AFR was associated with significantly shorter LOS. Multivariate analysis revealed high AFR, and cholesterol were linked to shorter stays, while older age and high globulin levels were associated with longer stays. Kaplan–Meier analysis confirmed the relationship. Preoperative AFR is a reliable predictor of LOS in advanced ESCC patients after neoadjuvant therapy, offering potential for improved clinical management and resource allocation.

Background: Esophageal squamous cell carcinoma (ESCC) remains a major global health challenge, and reliable prognostic markers. We developed and validated the Neoadjuvant Esophageal (NAE) score, derived from treatment response, to predict overall survival (OS) and postoperative complications in locally advanced ESCC. Methods: We retrospectively analyzed 411 patients with ESCC from four hospitals who underwent esophagectomy following neoadjuvant therapy between July 2013 and November 2020. Patients were stratified into low-, intermediate-, and high-score groups. OS was assessed using Kaplan–Meier and Cox regression analyses, and predictive models were constructed with nomograms. Model performance was evaluated using concordance index (C-index), ROC curves, calibration plots, and decision curve analysis. Results: The NAE score effectively stratified survival outcomes (5-year OS, p < 0.001), with higher scores indicating worse prognosis. Adjuvant therapy provided a significant OS benefit only in the high-NAE group ( p = 0.044), but not in the low- or intermediate-score groups. Multivariable Cox analysis confirmed the NAE score, perineural invasion, lymphovascular invasion, and neutrophil count as independent prognostic factors. A survival nomogram incorporating these variables demonstrated strong discrimination (C-index=0.742) and superior predictive accuracy compared with TNM staging (AUC 0.673–0.835 vs . 0.618–0.725). In addition, a complication-prediction nomogram integrating NAE score, surgical approach, and alcohol consumption reliably predicted major postoperative complications (C-index=0.789). Conclusion: The NAE score is a robust prognostic tool for patients with locally advanced ESCC, capable of predicting survival, guiding adjuvant therapy, and estimating risk of severe postoperative complications. Its integration into clinical practice could refine risk stratification and support personalized treatment strategies, with prospective validation warranted.

The prognostic role of nutrition-inflammation indices in esophageal squamous cell carcinoma (ESCC) requires further investigation. We developed the HALP-CONUT Integrated Score (HCIS) and evaluated its ability to predict survival and postoperative complications in locally advanced ESCC patients undergoing neoadjuvant therapy. This multicenter retrospective study analyzed 410 patients. HCIS was calculated by integrating the HALP index and CONUT score. Patients were stratified into risk groups. Survival was analyzed using Kaplan-Meier and Cox regression. Logistic regression assessed associations with complications and pathologic complete response (pCR). Predictive performance was evaluated using ROC curves, C-index, and decision curve analysis. Patients with high HCIS scores had significantly worse overall survival (OS) compared with those in lower-risk groups (p < 0.001). Multivariate Cox analysis confirmed HCIS as an independent prognostic factor for OS (HR 4.59, 95% CI 2.64-7.97, p < 0.001). Higher HCIS scores were also independently associated with increased risk of major postoperative complications (OR 7.144, 95% CI 3.698-13.813, p < 0.001) and a lower likelihood of achieving pCR (OR 6.470, 95% CI 3.577-12.40, p = 0.005). Predictive models incorporating HCIS demonstrated superior discrimination compared with models based on HALP or CONUT alone (AUC for OS: 0.789 vs 0.711 and 0.703, respectively). HCIS is a robust biomarker that integrates hematologic, nutritional, and inflammatory parameters. It independently predicts survival, postoperative complications, and pCR, providing a valuable tool for risk stratification and individualized treatment planning in locally advanced ESCC.

Background The prognostic role of nutrition–inflammation indices in esophageal squamous cell carcinoma (ESCC) requires further investigation. We developed the HALP–CONUT Integrated Score (HCIS) and evaluated its ability to predict survival and postoperative complications in locally advanced ESCC patients undergoing neoadjuvant therapy. Methods This multicenter retrospective study analyzed 410 patients. HCIS was calculated by integrating the HALP index and CONUT score. Patients were stratified into risk groups. Survival was analyzed using Kaplan–Meier and Cox regression. Logistic regression assessed associations with complications and pathologic complete response (pCR). Predictive performance was evaluated using ROC curves, C-index, and decision curve analysis. Results Patients with high HCIS scores had significantly worse overall survival (OS) compared with those in lower-risk groups (p < 0.001). Multivariate Cox analysis confirmed HCIS as an independent prognostic factor for OS (HR 4.59, 95% CI 2.64–7.97, p < 0.001). Higher HCIS scores were also independently associated with increased risk of major postoperative complications (OR 7.144, 95% CI 3.698–13.813, p < 0.001) and a lower likelihood of achieving pCR (OR 6.470, 95% CI 3.577–12.40, p = 0.005). Predictive models incorporating HCIS demonstrated superior discrimination compared with models based on HALP or CONUT alone (AUC for OS: 0.789 vs 0.711 and 0.703, respectively). Conclusion HCIS is a robust biomarker that integrates hematologic, nutritional, and inflammatory parameters. It independently predicts survival, postoperative complications, and pCR, providing a valuable tool for risk stratification and individualized treatment planning in locally advanced ESCC.