Wang Xiao Jun, Goh Denise Yun Ting, Dorajoo Sreemanee Raaj, Chan Alexandre
Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore, 117543, Singapore.
Department of Pharmacy, National Cancer Centre Singapore, Singapore, Singapore.
Support Care Cancer. 2017 Sep;25(9):2815-2822. doi: 10.1007/s00520-017-3696-6. Epub 2017 Apr 11.
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.
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.
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).
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.
本研究旨在通过将癌症治疗中性粒细胞减少功能评估(FACT-N)要素与多国癌症支持治疗协会(MASCC)风险指数相结合,开发并验证一种预后模型(PROMASCC),以识别发热性中性粒细胞减少(FN)的低风险患者,这些患者发生严重并发症的风险较低。
这是一项单中心横断面观察性研究。根据符合条件的患者在FN发作7天内的语言偏好,向他们发放英文或中文版本的FACT-N。进行单因素分析和多因素分析以构建PROMASCC模型。比较PROMASCC模型和MASCC风险指数的预后性能。通过自举技术对PROMASCC模型进行内部验证。
2014年8月至2016年4月,本研究共纳入120例符合条件的患者。在单因素分析中,只有不适子量表评分与良好结局(无并发症)显著相关(P = 0.024)。与MASCC风险指数相比,PROMASCC模型在提高特异性(64.3%对38.1%)和阳性预测值(81.0%对73.7%)、降低错误分类率(24.2%对25.8%)以及增加受试者工作特征曲线下面积(0.732对0.658)方面显示出优势。自举程序估计PROMASCC模型的乐观校正面积为0.731(95%CI 0.648至0.814)。
本研究开发并验证了PROMASCC模型,并表明额外测量患者的疲劳水平可以改善FN患者的风险分层。