Predictive role of a novel nutrition and inflammation-based model in the short-term clinical outcomes and health-related quality of life of patients undergoing radical gastrectomy: a retrospective cohort study

TL;DR

This study shows that a model using nutrition and inflammation indicators can predict short-term outcomes and quality of life in gastric cancer surgery patients.

Contribution

A novel predictive model combining nutrition and inflammation indicators for post-surgery outcomes in gastric cancer patients.

Findings

BMI, PNI, NLR, PLR, and SII are independent risk factors for poor short-term clinical outcomes.

The prediction model showed high accuracy in identifying high-risk patients.

These indicators are positively correlated with health-related quality of life scores.

Abstract

To explore the predictive value of nutritional and inflammatory indicators in the short-term clinical outcomes and health-related quality of life (HRQoL) of patients undergoing radical gastrectomy.

A total of 360 patients who underwent radical resection for gastric cancer in our hospital from May 2021 to May 2023 were selected as study subjects. According to the short-term clinical outcomes recorded in the medical record system, patients were divided into the good outcome group and the poor outcome group. The general demographic data, nutritional indicators, inflammatory indicators and HRQoL scores of the two groups of patients were collected and compared. The difference indicators were included in the Logistic regression model to analyze the related factors for poor short-term clinical outcomes of patients undergoing radical resection for gastric cancer, and the Pearson correlation analysis method was used to analyze the relationship between nutritional indicators, inflammatory indicators and q HRQoL.

Among 360 patients who underwent radical resection for gastric cancer, 108 were divided into the poor outcome group and 252 into the good outcome group. The body mass index (BMI), prognostic nutritional index (PNI), peripheral blood neutrophil to lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), systemic immune-inflammation index (SII) and HRQoL score between the two groups were statistically significant (P <0.05). Logistic regression analysis showed that BMI, PNI, NLR, PLR, and SII were independent risk factors for poor short-term clinical outcomes. A prediction model including the above five predictors was established based on Logistic regression. The net benefit rate of the prediction model in the threshold range was high, indicating that the prediction model had good accuracy. Pearson correlation analysis showed that BMI, PNI, NLR, PLR, and SII were positively correlated with HRQoL score (P <0.05).

BMI, PNI, NLR, PLR, and SII are important independent risk factors for poor short-term clinical outcomes in patients undergoing radical gastrectomy, and are closely related to the patients’ HRQoL. The construction of a prognostic prediction model can effectively screen high-risk groups and provide a theoretical basis for clinical intervention measures.

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