Loggers Elizabeth T, Gao Hongyuan, Gold Laura S, Kessler Larry, Etzioni Ruth, Buist Diana Sm
Fred Hutchinson Cancer Research Center, Clinical Research Division, 1100 Fairview Ave, D5-380, Seattle, WA 98109, USA.
Group Health Research Institute, Seattle, WA, USA.
J Comp Eff Res. 2015 May;4(3):215-226. doi: 10.2217/cer.15.1. Epub 2015 May 11.
Investigate how the results of predictive models of preoperative MRI for breast cancer change based on available data.
MATERIALS & METHODS: A total of 1919 insured women aged ≥18 with stage 0-III breast cancer diagnosed 2002-2009. Four models were compared using nested multivariable logistic, backwards stepwise regression; model fit was assessed via area under the curve (AUC), R.
MRI recipients (n = 245) were more recently diagnosed, younger, less comorbid, with higher stage disease. Significant variables included: Model 1/Claims (AUC = 0.76, R = 0.10): year, age, location, income; Model 2/Cancer Registry (AUC = 0.78, R = 0.12): stage, breast density, imaging indication; Model 3/Medical Record (AUC = 0.80, R = 0.13): radiologic recommendations; Model 4/Risk Factor Survey (AUC = 0.81, R = 0.14): procedure count.
Clinical variables accounted for little of the observed variability compared with claims data.
研究基于可用数据，乳腺癌术前MRI预测模型的结果如何变化。
共有1919名年龄≥18岁、2002年至2009年被诊断为0-III期乳腺癌的参保女性。使用嵌套多变量逻辑回归、向后逐步回归比较四个模型；通过曲线下面积（AUC）、R评估模型拟合度。
接受MRI检查的患者（n = 245）诊断时间更近、年龄更小、合并症更少、疾病分期更高。显著变量包括：模型1/理赔数据（AUC = 0.76，R = 0.10）：年份、年龄、位置、收入；模型2/癌症登记处数据（AUC = 0.78，R = 0.12）：分期、乳腺密度、成像指征；模型3/病历数据（AUC = 0.80，R = 0.13）：放射学建议；模型4/风险因素调查数据（AUC = 0.81，R = 0.14）：手术次数。
与理赔数据相比，临床变量对观察到的变异性影响较小。
