The Utility of Glasgow Prognostic Score and Palliative Prognostic Index in Patients With Head and Neck Squamous Cell Carcinoma Under Palliative Care

Utility of palliative prognostic index and neutrophil-to-lymphocyte ratio in predicting prognosis of end-stage squamous cell carcinoma of head and neck

BACKGROUND

The purpose of this study was to examine the factors influencing the prognosis of end-stage head and neck squamous cell carcinoma (HNSCC) and to develop a more useful prognostic index.

PATIENTS AND METHODS

This retrospective observational study included 79 patients with end-stage HNSCC. Patients were grouped by patient factors and prognostic indicators, and overall survival from the start of Best Supportive Care was compared for each group. In addition, overall survival was compared between patients with palliative prognostic index (PPI) ≥6 and neutrophil-to-lymphocyte ratio (NLR) ≥10 and patients with PPI <6 or NLR <10.

RESULTS

PPI ≥6 and NLR ≥10 were associated with significantly more frequent poor prognoses (p = 0.01 and p = 0.002, respectively). The median survival was 32 days in cases with PPI ≥6 and NLR ≥10, and 64 days for PPI <6 or NLR <10 (p < 0.001).

CONCLUSION

The combination of PPI and NLR is a useful indicator for predicting the prognosis of patients with end-stage HNSCC.

Survival prediction in advanced cancer patients - a narrative review

PURPOSE OF REVIEW

The exploration for accurate ways to predict survival for advanced cancer patients continues to be a significant theme despite the advent of objective criteria and their combination with clinical criteria. The purpose of this article was to review some of the latest studies relating to prognostication and the capacity to predict survival during the terminal cancer stage.

RECENT FINDINGS

Recent studies show notable prognostication approaches using genetic tests and advanced computation methods such as machine learning, which we will summarize.

SUMMARY

Significant effort has been made to improve the accuracy of survival estimation for advanced cancer patients. The main goals are to optimize individualized patient management and uses of resources. Advanced techniques, including genetic markers and machine learning techniques, may improve the accuracy of prediction.