Validity of Using Japanese Administrative Data to Identify Inpatients With Acute Pulmonary Embolism: Referencing the COMMAND VTE Registry

利用日本行政数据识别急性肺栓塞住院患者的有效性:以COMMAND VTE注册研究为例

阅读:1

Abstract

BACKGROUND: Acute pulmonary embolism (PE) is a life-threatening in-hospital complication. Recently, several studies have reported the clinical characteristics of PE among Japanese patients using the diagnostic procedure combination (DPC)/per diem payment system database. However, the validity of PE identification algorithms for Japanese administrative data is not yet clear. The purpose of this study was to evaluate the validity of using DPC data to identify acute PE inpatients. METHODS: The reference standard was symptomatic/asymptomatic PE patients included in the COntemporary ManageMent AND outcomes in patients with Venous ThromboEmbolism (COMMAND VTE) registry, which is a cohort study of acute symptomatic venous thromboembolism (VTE) patients in Japan. The validation cohort included all patients discharged from the six hospitals included in both the registry and DPC database. The identification algorithms comprised diagnosis, anticoagulation therapy, thrombolysis therapy, and inferior vena cava filter placement. Each algorithm's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were estimated. RESULTS: A total of 43.4% of the validation cohort was female, with a mean age of 67.3 years. The diagnosis-based algorithm showed a sensitivity of 90.2% (222/246; 95% confidence interval [CI], 85.8-93.6%), a specificity of 99.8% (228,485/229,027; 95% CI, 99.7-99.8%), a PPV of 29.1% (222/764; 95% CI, 25.9-32.4%) and an NPV of 99.9% (228,485/229,509; 95% CI, 99.9-99.9%) for identifying symptomatic/asymptomatic PE. Additionally, 94.6% (159/168; 95% CI, 90.1-97.5%) of symptomatic PE patients were identified using the diagnosis-based algorithm. CONCLUSION: The diagnosis-based algorithm may be a relatively sensitive method for identifying acute PE inpatients in the Japanese DPC database.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。