Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions.
Donatella CoradduzzaSerenella MediciCarla ChessaAngelo ZinelluMassimo MadoniaAndrea AngiusCiriaco CarruMaria Rosaria De MiglioPublished in: Medicina (Kaunas, Lithuania) (2023)
Background and Objectives : The hemoglobin (Hb)/red cell distribution width (RDW) ratio has emerged as an accessible, repeatable, and inexpensive prognostic factor that may predict survival in cancer patients. The focus of this systematic review is to investigate the prognostic role of the Hb/RDW ratio in cancer and the implications for clinical practice. Materials and Methods : A literature search of PubMed, Scopus, and Web of Science databases was performed by an independent author between 18 March and 30 March 2023 to collect relevant literature that assessed the prognostic value of the Hb/RDW ratio in cancer. Overall survival (OS), progression-free survival (PFS), and the association of these with the Hb/RDW ratio were considered to be the main endpoints. Results : Thirteen retrospective studies, including 3818 cancer patients, were identified and involved in this review. It was observed that, when patients with a high vs. low Hb/RDW ratio were compared, those with a lower Hb/RDW ratio had significantly poorer outcomes ( p < 0.05). In lung cancer patients, a one-unit increase in the Hb/RDW ratio reduces mortality by 1.6 times, whilst in esophageal squamous-cell carcinoma patients, a lower Hb/RDW ratio results in a 1.416-times greater risk of mortality. Conclusions : A low Hb/RDW ratio was associated with poor OS and disease progression in patients with cancer. This blood parameter should be considered a standard biomarker in clinical practice for predicting OS and PFS in cancer patients. Future searches will be necessary to determine and standardize the Hb/RDW cut-off value and to assess whether the Hb/RDW ratio is optimal as an independent prognostic factor or if it requires incorporation into risk assessment models for predicting outcomes in cancer patients.
Keyphrases
- prognostic factors
- systematic review
- risk assessment
- clinical practice
- free survival
- end stage renal disease
- randomized controlled trial
- stem cells
- squamous cell carcinoma
- single cell
- risk factors
- chronic kidney disease
- machine learning
- coronary artery disease
- mesenchymal stem cells
- young adults
- cross sectional
- adipose tissue
- squamous cell
- deep learning
- bone marrow
- ejection fraction
- cardiovascular events
- cardiovascular disease
- cell therapy
- heavy metals
- meta analyses
- skeletal muscle
- weight loss
- lymph node metastasis
- childhood cancer