Prediction of outcome in cancer patients with febrile neutropenia: a prospective validation of the Multinational Association for Supportive Care in Cancer risk index in a Chinese population and comparison with the Talcott model and artificial neural network
- 437 Downloads
We aimed to validate the Multinational Association for Supportive Care in Cancer (MASCC) risk index, and compare it with the Talcott model and artificial neural network (ANN) in predicting the outcome of febrile neutropenia in a Chinese population.
We prospectively enrolled adult cancer patients who developed febrile neutropenia after chemotherapy and risk classified them according to MASCC score and Talcott model. ANN models were constructed and temporally validated in prospectively collected cohorts.
From October 2005 to February 2008, 227 consecutive patients were enrolled. Serious medical complications occurred in 22% of patients and 4% died. The positive predictive value of low risk prediction was 86% (95% CI = 81–90%) for MASCC score ≥ 21, 84% (79–89%) for Talcott model, and 85% (78–93%) for the best ANN model. The sensitivity, specificity, negative predictive value, and misclassification rate were 81%, 60%, 52%, and 24%, respectively, for MASCC score ≥ 21; and 50%, 72%, 33%, and 44%, respectively, for Talcott model; and 84%, 60%, 58%, and 22%, respectively, for ANN model. The area under the receiver-operating characteristic curve was 0.808 (95% CI = 0.717–0.899) for MASCC, 0.573 (0.455–0.691) for Talcott, and 0.737 (0.633–0.841) for ANN model. In the low risk group identified by MASCC score ≥ 21 (70% of all patients), 12.5% developed complications and 1.9% died, compared with 43.3%, and 9.0%, respectively, in the high risk group (p < 0.0001).
The MASCC risk index is prospectively validated in a Chinese population. It demonstrates a better overall performance than the Talcott model and is equivalent to ANN model.
KeywordsFebrile neutropenia Risk prediction model Artificial neural networks Discriminatory accuracy Receiver-operating characteristics curve
This study was funded by the Health and Health Services Research Fund (HHSRF project No: 03040081), Food and Health Bureau, Hong Kong SAR Government. We thank the research staff at the Comprehensive Cancer Trials Unit (Jane Koh, Karin Tse, and Angel Lee) and all the patients included in this study for their contributions.
Conflict of interest
- 7.Cheung M, Online calculator: calculate risk in febrile neutropenia (MASCC). QxMD Hematology http://www.qxmd.com/hematology/MASCC-Febrile-Neutropenia-Risk.php
- 16.Jimeno A, Garcia-Velasco A, del Val O, Gonzalez-Billalabeitia E, Hernando S, Hernandez R, Sanchez-Munoz A, Lopez-Martin A, Duran I, Robles L, Cortes-Funes H, Paz-Ares L (2004) Assessment of procalcitonin as a diagnostic and prognostic marker in patients with solid tumors and febrile neutropenia. Cancer 100:2462–2469PubMedCrossRefGoogle Scholar
- 19.Klastersky J, Paesmans M, Rubenstein EB, Boyer M, Elting L, Feld R, Gallagher J, Herrstedt J, Rapoport B, Rolston K, Talcott J (2000) The Multinational Association for Supportive Care in Cancer risk index: a multinational scoring system for identifying low-risk febrile neutropenic cancer patients. J Clin Oncol 18:3038–3051PubMedGoogle Scholar
- 22.Paesmans M, Rapoport B, Maertens J, Slabber C, Ferrant A, Wingard J, Aoun M, Dubreucq L, Plehiers B, Klastersky J (2003) Multicentric prospective validation of the mascc risk-index score for identification of febrile neutropenic cancer patients at low-risk for serious medical complications. Proc Am Soc Clin Oncol 22:556, abstr 2235Google Scholar
- 32.Uys A, Rapoport BL, Fickl H, Meyer PW, Anderson R (2007) Prediction of outcome in cancer patients with febrile neutropenia: comparison of the Multinational Association of Supportive Care in Cancer risk-index score with procalcitonin, C-reactive protein, serum amyloid A, and interleukins-1beta, -6, -8 and -10. Eur J Cancer Care (Engl) 16:475–483CrossRefGoogle Scholar