A Novel Hematological Inflammation-Nutrition Score (HINS) and Its Related Nomogram Model to Predict Survival Outcome in Advanced Gastric Cancer Patients Receiving First-Line Palliative Chemotherapy

PURPOSE: This study aims to construct a novel hematological inflammation-nutrition score (HINS) and investigate its prognostic value in patients with advanced gastric cancer (AGC). We investigated the risk stratification performance of HINS and developed a HINS-based nomogram model to predict overall survival by combining traditional predictors. PATIENTS AND METHODS: We conducted a retrospective study on 812 AGC patients who received first-line platinum- or fluoropyrimidine-containing chemotherapy at The First Affiliated Hospital of Zhengzhou University Hospital between 2014 and 2019. Patients were randomly divided into a training cohort (N=609) and a validation cohort (N=203). HINS (0–2) was constructed based on a pre-chemotherapy systemic immune-inflammation index (SII) and albumin (ALB). Prognostic factors were screened by univariate and multivariate COX proportional regression models. Significant factors were used to construct a nomogram model. Internal validation was performed by calibration curves, time-dependent receiver operating characteristics (ROC) curves, and decision curve analysis (DCA), evaluating its prediction consistency, discrimination ability, and clinical net benefit. RESULTS: HINS was constructed based on SII and ALB. HINS showed a better stratification ability than JCOG prognostic index, with significant differences between groups. Multivariate analysis showed that ECOG ≥1 (HR: 1.379; P=0.005), Stage IV (HR: 1.581; P <0.001), diffuse-type histology (HR: 1.586; P <0.001), number of metastases ≥2 (HR: 1.274; P=0.038), without prior gastrectomy (HR: 1.830; P <0.001), ALP ≥ULN (HR: 1.335; P=0.034), HINS (P <0.001) were independent factors of OS. We successfully established a HINS-based nomogram model that showed a strong discriminative ability, accuracy, and clinical utility in training and validation cohorts. CONCLUSION: HINS shows a superior risk stratification ability, which might be a potential prognostic biomarker for AGC patients receiving palliative first-line palliative chemotherapy. The HINS-based nomogram model is a convenient and efficient tool for managing prognosis and follow-up treatments.