American College of Radiology Clinical Research Center, Philadelphia, PA; Boston Medical Center/Boston University School of Medicine, Boston, MA; The University of Texas MD Anderson Cancer Center, Houston, TX; Valley Radiotherapy Associates at Center for Radiation Therapy, Beverly Hills, CA; and Medical College of Wisconsin, Milwaukee, WI
American College of Radiology Clinical Research Center, Philadelphia, PA; Boston Medical Center/Boston University School of Medicine, Boston, MA; The University of Texas MD Anderson Cancer Center, Houston, TX; Valley Radiotherapy Associates at Center for Radiation Therapy, Beverly Hills, CA; and Medical College of Wisconsin, Milwaukee, WI.
J Oncol Pract. 2014 May;10(3):e175-81. doi: 10.1200/JOP.2013.001143. Epub 2014 Mar 18.
Patient comorbidities may affect the applicability of performance measures that are inherent in multidisciplinary cancer treatment guidelines. This article describes the distribution of common comorbid conditions by disease site and by patient and facility characteristics in patients who received radiation therapy as part of treatment for cancer of the breast, cervix, lung, prostate, and stomach, and investigates the association of comorbidities with treatment decisions.
Stratified two-stage cluster sampling provided a random sample of radiation oncology facilities. Eligible patients were randomly sampled from each participating facility for each disease site, and data were abstracted from medical records. The Adult Comorbidity Evaluation Index (ACE-27) was used to measure comorbid conditions and their severity. National estimates were calculated using SUDAAN statistical software.
Multivariable logistic regression models predicted the dependent variable "treatment changed or contraindicated due to comorbidities." The final model showed that ACE-27 was highly associated with change in treatment for patients with severe or moderate index values compared to those with none or mild (P < .001). Two other covariates, age and medical coverage, had no (age) or little (medical coverage) significant contribution to predicting treatment change in the multivariable model. Disease site was associated with treatment change after adjusting for other covariates in the model.
ACE-27 is highly predictive of treatment modifications for patients treated for these cancers who receive radiation as part of their care. A standardized tool identifying patients who should be excluded from clinical performance measures allows more accurate use of these measures.
患者合并症可能会影响多学科癌症治疗指南中固有的绩效评估指标的适用性。本文通过疾病部位以及患者和医疗机构特征,描述了接受乳腺癌、宫颈癌、肺癌、前列腺癌和胃癌放射治疗患者中常见合并症的分布情况,并调查了合并症与治疗决策的关联。
分层两阶段聚类抽样为放射肿瘤学机构提供了随机抽样。从每个参与机构的每个疾病部位随机抽取符合条件的患者作为抽样对象,并从病历中提取数据。采用成人合并症评估指数(ACE-27)来衡量合并症及其严重程度。采用 SUDAAN 统计软件计算全国估计数。
多变量逻辑回归模型预测了因合并症而改变或禁忌治疗的因变量。最终模型表明,与无或轻度 ACE-27 指数值的患者相比,严重或中度 ACE-27 指数值的患者治疗改变的可能性更高(P<0.001)。另外两个协变量(年龄和医疗保障)在多变量模型中对预测治疗改变的作用不大(年龄)或几乎没有(医疗保障)。在调整模型中的其他协变量后,疾病部位与治疗改变相关。
ACE-27 高度预测了接受放射治疗的这些癌症患者的治疗改变。确定应排除在临床绩效评估指标之外的患者的标准化工具,可更准确地使用这些指标。