一种基于医疗保险理赔数据的算法用于识别筛查乳腺钼靶检查中发现的乳腺癌的验证
Validation of a Medicare Claims-based Algorithm for Identifying Breast Cancers Detected at Screening Mammography.
作者信息
Fenton Joshua J, Onega Tracy, Zhu Weiwei, Balch Steven, Smith-Bindman Rebecca, Henderson Louise, Sprague Brian L, Kerlikowske Karla, Hubbard Rebecca A
机构信息
*Department of Family and Community Medicine, Center for Healthcare Research and Policy, and the Comprehensive Cancer Center, University of California, Davis, Sacramento, CA †Section of Biostatistics and Epidemiology, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH ‡Group Health Research Institute, Seattle, WA Departments of §Radiology ∥Epidemiology and Biostatistics, University of California, San Francisco, CA ¶Department of Radiology, University of North Carolina, Chapel Hill, NC #Health Promotion Research, University of Vermont, Burlington, VT **Department of Medicine, University of California, San Francisco, CA ††Department of Biostatistics, University of Washington, Seattle, WA.
出版信息
Med Care. 2016 Mar;54(3):e15-22. doi: 10.1097/MLR.0b013e3182a303d7.
BACKGROUND
The breast cancer detection rate is a benchmark measure of screening mammography quality, but its computation requires linkage of mammography interpretive performance information with cancer incidence data. A Medicare claims-based measure of detected breast cancers could simplify measurement of this benchmark and facilitate mammography quality assessment and research.
OBJECTIVES
To validate a claims-based algorithm that can identify with high positive predictive value (PPV) incident breast cancers that were detected at screening mammography.
RESEARCH DESIGN
Development of a claims-derived algorithm using classification and regression tree analyses within a random half-sample of Medicare screening mammography claims followed by validation of the algorithm in the remaining half-sample using clinical data on mammography results and cancer incidence from the Breast Cancer Surveillance Consortium (BCSC).
SUBJECTS
Female fee-for-service Medicare enrollees aged 68 years and older who underwent screening mammography from 2001 to 2005 within BCSC registries in 4 states (CA, NC, NH, and VT), enabling linkage of claims and BCSC mammography data (N=233,044 mammograms obtained by 104,997 women).
MEASURES
Sensitivity, specificity, and PPV of algorithmic identification of incident breast cancers that were detected by radiologists relative to a reference standard based on BCSC mammography and cancer incidence data.
RESULTS
An algorithm based on subsequent codes for breast cancer diagnoses and treatments and follow-up mammography identified incident screen-detected breast cancers with 92.9% sensitivity [95% confidence interval (CI), 91.0%-94.8%], 99.9% specificity (95% CI, 99.9%-99.9%), and a PPV of 88.0% (95% CI, 85.7%-90.4%).
CONCLUSIONS
A simple claims-based algorithm can accurately identify incident breast cancers detected at screening mammography among Medicare enrollees. The algorithm may enable mammography quality assessment using Medicare claims alone.
背景
乳腺癌检出率是乳腺钼靶筛查质量的一项基准指标，但其计算需要将乳腺钼靶解读性能信息与癌症发病率数据相联系。基于医疗保险理赔数据的已检出乳腺癌指标可简化该基准指标的测量，并有助于乳腺钼靶质量评估和研究。
目的
验证一种基于理赔数据的算法，该算法能够以高阳性预测值（PPV）识别在乳腺钼靶筛查中检出的新发乳腺癌。
研究设计
在医疗保险乳腺钼靶筛查理赔数据的随机一半样本中，使用分类和回归树分析开发一种基于理赔数据的算法，随后在另一半样本中使用来自乳腺癌监测联盟（BCSC）的乳腺钼靶检查结果和癌症发病率的临床数据对该算法进行验证。
研究对象
2001年至2005年期间在4个州（加利福尼亚州、北卡罗来纳州、新罕布什尔州和佛蒙特州）的BCSC登记处接受乳腺钼靶筛查的68岁及以上按服务收费的女性医疗保险参保者，从而实现理赔数据与BCSC乳腺钼靶数据的关联（104,997名女性获得了233,044次乳腺钼靶检查）。
测量指标
相对于基于BCSC乳腺钼靶和癌症发病率数据的参考标准，算法识别放射科医生检出的新发乳腺癌的灵敏度、特异度和PPV。
结果
一种基于后续乳腺癌诊断、治疗编码以及随访乳腺钼靶检查的算法识别出筛查时检出的新发乳腺癌，其灵敏度为92.9% [95%置信区间（CI），91.0% - 94.8%]，特异度为99.9%（95% CI，99.9% - 99.9%），PPV为88.0%（95% CI，85.7% - 90.4%）。
结论
一种简单的基于理赔数据的算法能够准确识别医疗保险参保者在乳腺钼靶筛查中检出的新发乳腺癌。该算法可能仅使用医疗保险理赔数据就能实现乳腺钼靶质量评估。
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