title: 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.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China, huipun@cuhk.edu.hk.
authors.forename: Edwin Pun
authors.initials: EP
authors.lastname: Hui
authors.forename: Linda K S
authors.initials: LK
authors.lastname: Leung
authors.forename: Terence C W
authors.initials: TC
authors.lastname: Poon
authors.forename: Frankie
authors.initials: F
authors.lastname: Mo
authors.forename: Vicky T C
authors.initials: VT
authors.lastname: Chan
authors.forename: Ada T W
authors.initials: AT
authors.lastname: Ma
authors.forename: Annette
authors.initials: A
authors.lastname: Poon
authors.forename: Eugenie K
authors.initials: EK
authors.lastname: Hui
authors.forename: So-Shan
authors.initials: SS
authors.lastname: Mak
authors.forename: Maria
authors.initials: M
authors.lastname: Lai
authors.forename: Kenny I K
authors.initials: KI
authors.lastname: Lei
authors.forename: Brigette B Y
authors.initials: BB
authors.lastname: Ma
authors.forename: Tony S K
authors.initials: TS
authors.lastname: Mok
authors.forename: Winnie
authors.initials: W
authors.lastname: Yeo
authors.forename: Benny C Y
authors.initials: BC
authors.lastname: Zee
authors.forename: Anthony T C
authors.initials: AT
authors.lastname: Chan
mesh_terms: D000328:Adult; D000970:Antineoplastic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D002681:China; D015331:Cohort Studies; D005260:Female; D005334:Fever / Q000139:chemically induced; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy; D016571:Neural Networks, Computer; D009503:Neutropenia / Q000139:chemically induced* / Q000208:ethnology; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents
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