Modi Parth K, Kaufman Samuel R, Qi Ji, Lane Brian R, Cher Michael L, Miller David C, Hollenbeck Brent K, Shahinian Vahakn B, Dupree James M
Dow Division of Health Services Research, Department of Urology, University of Michigan, Ann Arbor, MI.
Urologic Oncology, Spectrum Health, Grand Rapids, MI.
Urology. 2018 Oct;120:96-102. doi: 10.1016/j.urology.2018.06.037. Epub 2018 Jul 7.
To better describe the real-world use of active surveillance. Active surveillance is a preferred management option for low-risk prostate cancer, yet its use outside of high-volume institutions is poorly understood. We created multiple claims-based algorithms, validated them using a robust clinical registry, and applied them to Medicare claims to describe national utilization.
We identified men with prostate cancer from 2012-2014 in a 100% sample of Michigan Medicare data and linked them with the Michigan Urologic Surgery Improvement Collaborative (MUSIC) registry. Using MUSIC treatment assignment as the standard, we determined the performance of 8 claims-based algorithms to identify men on active surveillance. We selected 3 algorithms (the most sensitive, the most specific, and a balanced algorithm incorporating age and comorbidity) and applied them to a 20% national Medicare sample to describe national trends.
We identified 1186 men with incident prostate cancer and completely linked data. Eight algorithms were tested with sensitivity ranging from 23.5% to 88.2% and specificity ranging from 93.5% to 99.1%. We found that the use of surveillance for men with incident prostate cancer increased from 2007 to 2014, nationally. However, among all men in the population, there was a large decrease in the rate of prostate cancer diagnosis and an increased or stable rate in the use of active surveillance, depending on the algorithm used. Less than 25% of men on active surveillance underwent a confirmatory prostate biopsy.
We describe the performance of claims-based algorithms to identify active surveillance.
更好地描述主动监测在现实世界中的应用情况。主动监测是低风险前列腺癌的首选管理方案,但在大型机构之外的应用情况却鲜为人知。我们创建了多种基于索赔的算法,使用强大的临床登记系统对其进行验证,并将其应用于医疗保险索赔数据以描述全国范围内的使用情况。
我们从密歇根州医疗保险数据的100%样本中识别出2012 - 2014年患有前列腺癌的男性,并将他们与密歇根泌尿外科手术改进协作组织(MUSIC)登记系统相链接。以MUSIC的治疗分配作为标准,我们确定了8种基于索赔的算法识别接受主动监测男性的性能。我们选择了3种算法(最敏感的、最特异的以及一种纳入年龄和合并症的平衡算法),并将它们应用于20%的全国医疗保险样本以描述全国趋势。
我们识别出1186例患有前列腺癌的男性并获得了完整的关联数据。对8种算法进行了测试,其灵敏度范围为23.5%至88.2%,特异度范围为93.5%至99.1%。我们发现,全国范围内,2007年至2014年期间,新诊断前列腺癌男性中主动监测的使用有所增加。然而,在总体人群中的所有男性中,前列腺癌诊断率大幅下降,主动监测的使用率则根据所使用的算法而有所增加或保持稳定。接受主动监测的男性中,不到25%进行了确诊性前列腺活检。
我们描述了基于索赔的算法识别主动监测的性能。