Abstract
Handgrip strength (HGS) and the high-sensitivity modified Glasgow prognostic score (HS-mGPS) are associated with the survival of patients with cancer. However, no studies have investigated the combined effect of HGS and HS-mGPS on the overall survival (OS) of patients with colon cancer. Prospective follow-up data of colon cancer patients undergoing radical resection from April, 2016 to September, 2019 were retrospectively collected. We combined the HGS and HS-mGPS to create a new composite index, HGS-HS-mGPS. The hazard ratio (HR) and 95% confidence interval (CI) were calculated using Cox regression models to assess the association between variables and OS. Risk factors on OS rates were investigated by Cox analyses and the nomogram was constructed using significant predictors and HGS-HS-mGPS. The predictive performance of the nomogram was evaluated by receiver operating characteristic curve and calibration curve. This study included a total of 811 patients, of which 446 (55.0%) were male. The HGS optimal cut-off values of male and female patients were 28.8 and 19.72 kg, respectively. Multivariate analysis revealed that low HGS and high HS-mGPS were independent risk factors of colon cancer after adjusting confounders (adjusted HR = 3.20; 95% CI: 2.27-4.50; p < 0.001 and adjusted HR = 1.55; 95% CI: 1.12-2.14; p = 0.008 respectively). Patients with low HGS and high HS-mGPS had a 10.76-fold higher mortality risk than those with neither (adjusted HR = 10.76; 95% CI: 5.38-21.54; p < 0.001). A nomogram predicting 1-, 3-, and 5 year OS was constructed based on three clinicopathologic prognostic factors. Importantly, incorporating HGS-HS-mGPS into the nomogram model meaningfully improved the predictive performance. The decision curve analyses demonstrated the application value of the HGS-HS-mGPS nomogram for predicting OS of patients with colon cancer. HGS-HS-mGPS is associated with the survival of patients with colon cancer. These findings indicate the usefulness of HGS and HS-mGPS measurements in clinical practice for improving patient assessment, cancer prognosis, and precise intervention.
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