Department of Medicine, Duke University, Durham, North Carolina 27710, USA.
Cancer. 2011 May 1;117(9):1917-27. doi: 10.1002/cncr.25691. Epub 2010 Nov 29.
A prospective cohort study was undertaken to develop and validate a risk model for neutropenic complications in cancer patients receiving chemotherapy.
The study population consisted of 3760 patients with common solid tumors or malignant lymphoma who were beginning a new chemotherapy regimen at 115 practice sites throughout the United States. A regression model for neutropenic complications was developed and then validated by using a random split-sample selection process.
No significant differences in the derivation and validation populations were observed. The risk of neutropenic complications was greatest in cycle 1 with no significant difference in predicted risk between the 2 cohorts in univariate analysis. After adjustment for cancer type and age, major independent risk factors in multivariate analysis included: prior chemotherapy, abnormal hepatic and renal function, low white blood count, chemotherapy and planned delivery ≥85%. At a predicted risk cutpoint of 10%, model test performance included: sensitivity 90%, specificity 59%, and predictive value positive and negative of 34% and 96%, respectively. Further analysis confirmed model discrimination for risk of febrile neutropenia over multiple chemotherapy cycles.
A risk model for neutropenic complications was developed and validated in a large prospective cohort of patients who were beginning cancer chemotherapy that may guide the effective and cost-effective use of available supportive care.
本前瞻性队列研究旨在建立并验证一个用于预测接受化疗的癌症患者发生中性粒细胞减少并发症风险的模型。
研究人群包括在美国 115 个临床实践站点开始新化疗方案的 3760 例常见实体瘤或恶性淋巴瘤患者。通过随机拆分样本选择过程建立了中性粒细胞减少并发症风险的回归模型,并对其进行了验证。
在推导人群和验证人群中未观察到显著差异。中性粒细胞减少并发症的风险在第 1 周期最大,在单变量分析中,2 个队列之间的预测风险无显著差异。在调整了癌症类型和年龄后,多变量分析中的主要独立危险因素包括:既往化疗、肝肾功能异常、白细胞计数低、化疗和计划分娩≥85%。在预测风险切点为 10%时,模型测试性能包括:敏感性 90%,特异性 59%,阳性预测值和阴性预测值分别为 34%和 96%。进一步分析证实了该模型在多个化疗周期中预测发热性中性粒细胞减少风险的区分能力。
本研究在开始癌症化疗的大量前瞻性队列患者中建立并验证了一个用于预测中性粒细胞减少并发症风险的模型,该模型可能有助于指导有效和具有成本效益的支持性护理的应用。