*Agency for Healthcare Research and Quality, Rockville, MD †Epidemiology and Public Health, School of Medicine ‡IMPAQ International, LLC, Columbia, MD §Pharmaceutical Health Services Research, School of Pharmacy, University of Maryland, Baltimore, MD ∥School of Medicine, University of New Mexico, Albuquerque, NM.
Med Care. 2014 Jun;52(6):500-10. doi: 10.1097/MLR.0000000000000122.
BACKGROUND: In prior research, we developed a claims-based prediction model for poor patient disability status (DS), a proxy measure for performance status, commonly used by oncologists to summarize patient functional status and assess ability of a patient to tolerate aggressive treatment. In this study, we implemented and validated the DS measure in 4 cohorts of cancer patients: early and advanced non-small cell lung cancers (NSCLC), stage IV estrogen receptor-negative (ER-) breast cancer, and myelodysplastic syndromes (MDS). DATA AND METHODS: SEER-Medicare data (1999-2007) for the 4 cohorts of cancer patients. Bivariate and multivariate logistic regression tested the association of the DS measure with designated cancer-directed treatments: early NSCLC (surgery), advanced NSCLC (chemotherapy), stage IV ER- breast cancer (chemotherapy), and MDS (erythropoiesis-stimulating agents). Treatment model fit was compared across model iterations. RESULTS: In both unadjusted and adjusted results, predicted poor DS was strongly associated with a lower likelihood of cancer treatment receipt in all 4 cohorts [early NSCLC (N=20,280), advanced NSCLC (N=31,341), stage IV ER- breast cancer (N=1519), and MDS (N=6058)] independent of other patient, contextual, and disease characteristics, as well as the Charlson Comorbidity Index. Inclusion of the DS measure into models already controlling for other variables did not significantly improve model fit across the cohorts. CONCLUSIONS: The DS measure is a significant independent predictor of cancer-directed treatment. Small changes in model fit associated with both DS and the Charlson Comorbidity Index suggest that unobserved factors continue to play a role in determining cancer treatments.
背景：在之前的研究中，我们开发了一种基于索赔的预测模型，用于预测患者较差的残疾状况（DS），这是一种常用于肿瘤学家评估患者功能状态和评估患者耐受强化治疗能力的绩效状态替代指标。在这项研究中，我们在 4 组癌症患者中实施和验证了 DS 测量方法：早期和晚期非小细胞肺癌（NSCLC）、IV 期雌激素受体阴性（ER-）乳腺癌和骨髓增生异常综合征（MDS）。 数据和方法：使用 SEER-Medicare 数据（1999-2007 年）对 4 组癌症患者进行分析。双变量和多变量逻辑回归测试了 DS 测量方法与指定的癌症定向治疗之间的关联：早期 NSCLC（手术）、晚期 NSCLC（化疗）、IV 期 ER-乳腺癌（化疗）和 MDS（促红细胞生成素刺激剂）。比较了不同模型迭代之间的治疗模型拟合度。 结果：在未调整和调整后的结果中，预测的较差 DS 与所有 4 组癌症治疗接受率较低密切相关[早期 NSCLC（N=20,280）、晚期 NSCLC（N=31,341）、IV 期 ER-乳腺癌（N=1,519）和 MDS（N=6,058）]，独立于其他患者、背景和疾病特征以及 Charlson 合并症指数。在已经控制了其他变量的模型中包含 DS 测量方法并没有显著提高各队列模型的拟合度。 结论：DS 测量方法是癌症定向治疗的重要独立预测指标。DS 和 Charlson 合并症指数与模型拟合度的微小变化表明，未观察到的因素继续在确定癌症治疗中发挥作用。
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