A validation of clinical data captured from a novel Cancer Care Quality Program directly integrated with administrative claims data.

PRAGMATIC AND OBSERVATIONAL RESEARCH(2017)

引用 12|浏览2
暂无评分
摘要
Background: Data from a Cancer Care Quality Program are directly integrated with administrative claims data to provide a level of clinical detail not available in claims-based studies, and referred to as the HealthCore Integrated Research Environment (HIRE)-Oncology data. This study evaluated the validity of the HIRE-Oncology data compared with medical records of breast, lung, and colorectal cancer patients. Methods: Data elements included cancer type, stage, histology (lung only), and biomarkers. A sample of 300 breast, 200 lung, and 200 colorectal cancer patients within the HIRE-Oncology data were identified for medical record review. Statistical measures of validity (agreement, positive predictive value [PPV], negative predictive value [NPV], sensitivity, specificity) were used to compare clinical information between data sources, with medical record data considered the gold standard. Results: All 300 breast cancer records reviewed were confirmed breast cancer, while 197 lung and 197 colorectal records were confirmed (PPV =0.99 for each). The agreement of disease stage was 85% for breast, 90% for lung, and 94% for colorectal cancer. The agreement of lung cancer histology (small cell vs non-small cell) was 97%. Agreement of progesterone receptor, estrogen receptor, and human epidermal growth factor receptor 2 status biomarkers in breast cancer was 92%, 97%, and 92%, respectively; epidermal growth factor receptor and anaplastic lymphoma kinase agreement in lung was 97% and 92%, respectively; and agreement of KRAS status in colorectal cancer was 95%. Measures of PPV, NPV, sensitivity, and specificity showed similarly strong evidence of validity. Conclusion: Good agreement between the HIRE-Oncology data and medical records supports the validity of these data for research.
更多
查看译文
关键词
validation,administrative claims,breast cancer,lung cancer,colorectal cancer,oncology
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要