Mean Platelet Volume-to-Albumin Ratio as a Predictor of Mortality in Patients with Febrile Neutropenia: An Observational Study - Jelena Dimitrijević; Marina Čalamać; Ognjen Đurmez; Marko Stojanović (Medicina)
Background and Objectives: Febrile neutropenia (FN) is a life-threatening complication in cancer patients, necessitating early risk stratification for optimal management. While the Multinational Association for Supportive Care in Cancer (MASCC) risk index is commonly used for mortality prediction, novel biomarkers that integrate inflammatory and nutritional status may improve prognostic accuracy. This study evaluates the mean platelet volume (MPV)-to-albumin (ALB) ratio as a predictor of mortality in patients with febrile neutropenia. Materials and Methods: A prospective study was conducted on patients diagnosed with febrile neutropenia. The MPV/ALB ratio was calculated at admission, and its predictive value for mortality was assessed using receiver operating characteristic (ROC) curve analysis. The results were compared with the MASCC risk index for predicting mortality both during the FN episode and within 28 days. Statistical analysis included sensitivity, specificity, and negative predictive value assessments. Results: The MPV/ALB ratio was significantly lower in non-survivors compared to survivors. A threshold of 0.245 effectively stratified patients at high risk, demonstrating strong specificity and high negative predictive value. The MPV/ALB ratio outperformed the MASCC risk index in predicting mortality during febrile neutropenia, whereas the MASCC index showed slightly better performance for 28-day mortality prediction. Conclusions: The MPV/ALB ratio is a promising biomarker for mortality prediction in febrile neutropenia, offering a simple and accessible tool for early risk assessment. Its superior performance in predicting mortality during FN highlights its potential clinical utility. Further prospective studies are needed to validate these findings and explore the integration of the MPV/ALB ratio into existing risk assessment models.