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基于索赔数据的算法用于识别冠心病事件高风险的医疗保险受益人:一项横断面研究。

Claims-based algorithms for identifying Medicare beneficiaries at high estimated risk for coronary heart disease events: a cross-sectional study.

作者信息

Thacker Evan L, Muntner Paul, Zhao Hong, Safford Monika M, Curtis Jeffrey R, Delzell Elizabeth, Bittner Vera, Brown Todd M, Levitan Emily B

机构信息

Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294-0022, USA.

出版信息

BMC Health Serv Res. 2014 Apr 29;14:195. doi: 10.1186/1472-6963-14-195.

Abstract

BACKGROUND

Databases of medical claims can be valuable resources for cardiovascular research, such as comparative effectiveness and pharmacovigilance studies of cardiovascular medications. However, claims data do not include all of the factors used for risk stratification in clinical care. We sought to develop claims-based algorithms to identify individuals at high estimated risk for coronary heart disease (CHD) events, and to identify uncontrolled low-density lipoprotein (LDL) cholesterol among statin users at high risk for CHD events.

METHODS

We conducted a cross-sectional analysis of 6,615 participants ≥66 years old using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study baseline visit in 2003-2007 linked to Medicare claims data. Using REGARDS data we defined high risk for CHD events as having a history of CHD, at least 1 risk equivalent, or Framingham CHD risk score >20%. Among statin users at high risk for CHD events we defined uncontrolled LDL cholesterol as LDL cholesterol ≥100 mg/dL. Using Medicare claims-based variables for diagnoses, procedures, and healthcare utilization, we developed algorithms for high CHD event risk and uncontrolled LDL cholesterol.

RESULTS

REGARDS data indicated that 49% of participants were at high risk for CHD events. A claims-based algorithm identified high risk for CHD events with a positive predictive value of 87% (95% CI: 85%, 88%), sensitivity of 69% (95% CI: 67%, 70%), and specificity of 90% (95% CI: 89%, 91%). Among statin users at high risk for CHD events, 30% had LDL cholesterol ≥100 mg/dL. A claims-based algorithm identified LDL cholesterol ≥100 mg/dL with a positive predictive value of 43% (95% CI: 38%, 49%), sensitivity of 19% (95% CI: 15%, 22%), and specificity of 89% (95% CI: 86%, 90%).

CONCLUSIONS

Although the sensitivity was low, the high positive predictive value of our algorithm for high risk for CHD events supports the use of claims to identify Medicare beneficiaries at high risk for CHD events.

摘要

背景

医疗索赔数据库对于心血管研究而言可能是宝贵的资源,比如心血管药物的比较疗效研究和药物警戒研究。然而,索赔数据并不包含临床护理中用于风险分层的所有因素。我们试图开发基于索赔的算法,以识别冠心病(CHD)事件估计风险高的个体,并在CHD事件高风险的他汀类药物使用者中识别未得到控制的低密度脂蛋白(LDL)胆固醇。

方法

我们使用2003年至2007年中风地理和种族差异原因(REGARDS)研究基线访视的数据与医疗保险索赔数据相链接,对6615名年龄≥66岁的参与者进行了横断面分析。利用REGARDS数据,我们将CHD事件高风险定义为有CHD病史、至少1个风险等同因素或弗雷明汉姆CHD风险评分>20%。在CHD事件高风险的他汀类药物使用者中,我们将未得到控制的LDL胆固醇定义为LDL胆固醇≥100mg/dL。利用基于医疗保险索赔的诊断、程序和医疗保健利用变量,我们开发了CHD事件高风险和未得到控制的LDL胆固醇的算法。

结果

REGARDS数据表明,49%的参与者有CHD事件高风险。一种基于索赔的算法识别CHD事件高风险的阳性预测值为87%(95%CI:85%,88%),敏感性为69%(95%CI:67%,70%),特异性为90%(95%CI:89%,91%)。在CHD事件高风险的他汀类药物使用者中,30%的人LDL胆固醇≥100mg/dL。一种基于索赔的算法识别LDL胆固醇≥100mg/dL的阳性预测值为43%(95%CI:38%,49%),敏感性为19%(95%CI:15%,22%),特异性为89%(95%CI:86%,90%)。

结论

尽管敏感性较低,但我们的算法对CHD事件高风险的高阳性预测值支持使用索赔来识别医疗保险中CHD事件高风险的受益人。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1a0/4101858/fc2f23880f5e/1472-6963-14-195-1.jpg

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