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The prognostic performance of adding patient-reported outcomes to the MASCC risk index to identify low-risk febrile neutropenia patients with solid tumors and lymphomas.
Supportive Care in Cancer 2017 September
PURPOSE: This study aims to develop and validate a prognostic model (PROMASCC) by incorporating the Functional Assessment of Cancer Therapy-Neutropenia (FACT-N) elements, with the Multinational Association of Supportive Care in Cancer (MASCC) risk index, for identifying low-risk patients with febrile neutropenia (FN) for developing serious complications.
METHODS: This was a single-center, cross-sectional observational study. Either English or Chinese versions of the FACT-N were administered to the eligible patients according to their language preference within 7 days of FN onset. Univariate analyses and multivariate analyses were performed to construct the PROMASCC model. The prognostic performance was compared between the PROMASCC model and MASCC risk index. The internal validation of the PROMASCC model was examined by bootstrapping technique.
RESULTS: From August 2014 to April 2016, a total of 120 eligible patients were included in this study. In the univariate analyses, only the malaise subscale score has been significantly associated with the favorable outcome (without complications) (P = 0.024). Compared to the MASCC risk index, the PROMASCC model has shown advantages on the improved specificity (64.3 vs. 38.1%) and positive predictive value (81.0 vs. 73.7%), lower misclassification rate (24.2 vs. 25.8%), and increased area under receiver-operating characteristic curve (0.732 vs. 0.658). The bootstrapping procedure estimates the optimism-corrected area for the PROMASCC model to be 0.731 (95% CI 0.648 to 0.814).
CONCLUSIONS: This study has developed and validated a PROMASCC model and demonstrated that additional measurement on patient's fatigue level could improve the risk stratification of patients with FN.
METHODS: This was a single-center, cross-sectional observational study. Either English or Chinese versions of the FACT-N were administered to the eligible patients according to their language preference within 7 days of FN onset. Univariate analyses and multivariate analyses were performed to construct the PROMASCC model. The prognostic performance was compared between the PROMASCC model and MASCC risk index. The internal validation of the PROMASCC model was examined by bootstrapping technique.
RESULTS: From August 2014 to April 2016, a total of 120 eligible patients were included in this study. In the univariate analyses, only the malaise subscale score has been significantly associated with the favorable outcome (without complications) (P = 0.024). Compared to the MASCC risk index, the PROMASCC model has shown advantages on the improved specificity (64.3 vs. 38.1%) and positive predictive value (81.0 vs. 73.7%), lower misclassification rate (24.2 vs. 25.8%), and increased area under receiver-operating characteristic curve (0.732 vs. 0.658). The bootstrapping procedure estimates the optimism-corrected area for the PROMASCC model to be 0.731 (95% CI 0.648 to 0.814).
CONCLUSIONS: This study has developed and validated a PROMASCC model and demonstrated that additional measurement on patient's fatigue level could improve the risk stratification of patients with FN.
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