Improving Prognostic Accuracy of MASCC Score with Lactate and CRP Measurements in Febrile Neutropenic Patients

Abstract

Objectives: Febrile neutropenia is a common oncologic emergency with significant morbidity and mortality. Although the MASCC (Multinational Association for Supportive Care in Cancer) score is widely used for risk stratification, its limited sensitivity and lack of laboratory parameters reduce its prognostic utility. This study aimed to evaluate whether incorporating serum lactate and CRP measurements into the MASCC score enhances its predictive performance for hospital admission and the 30-day mortality. Methods: This retrospective diagnostic accuracy study included adult patients diagnosed with febrile neutropenia in the emergency department of a tertiary care hospital between January 2021 and December 2024. The original MASCC score was calculated, and three modified models were derived: the MASCC-L (lactate/MASCC), MASCC-C (CRP/MASCC) and MASCC-LC models (CRP × lactate/MASCC). The predictive accuracy for hospital admission and the 30-day all-cause mortality was assessed using ROC analysis. Results: A total of 269 patients (mean age: 67.6 ± 12.4 years) were included; the 30-day mortality was 3.0%. The MASCC-LC model demonstrated the highest discriminative ability for mortality prediction (area under the curve (AUC): 0.995; sensitivity: 100%; specificity: 98%). For hospital admission prediction, the MASCC-C model had the highest specificity (81%), while the MASCC-LC model showed the best balance of sensitivity and specificity (both 73%). All the modified models outperformed the original MASCC score regarding both endpoints. Conclusions: Integrating lactate and CRP measurements into the MASCC score significantly improves its prognostic accuracy for both mortality and hospital admission in febrile neutropenic patients. The MASCC-LC model, relying on only three objective parameters, may serve as a practical and efficient tool for early risk stratification in emergency settings.

Keywords: MASCC score; febrile neutropenia; risk stratification.

Figure 1

ROC curves comparing the performance of the MASCC and modified models for hospital admission prediction. AUC: area under the curve; MASCC-L = lactate/MASCC; MASCC-C = CRP/MASCC; MASCC-LC = (CRP × lactate)/MASCC. Sensitivity represents the true positive rate; 1–specificity represents the false positive rate.

Figure 2

ROC curves comparing the performance of the MASCC and modified models for 30-day mortality prediction. AUC: area under the curve; MASCC-L = lactate/MASCC; MASCC-C = CRP/MASCC; MASCC-LC = (CRP × lactate)/MASCC. Sensitivity represents the true positive rate; 1–specificity represents the false positive rate.