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Retrospective Database Research in Pediatric Cardiology and Congenital Heart Surgery: An Illustrative Example of Limitations and Possible Solutions

机译:小儿心脏病学和先天性心脏病手术回顾性数据库研究:局限性和可能解决方案的示例

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Background: Secondary use of data, whether from clinical information systems or registries, for carrying out clinical research in rare diseases is a common practice but is fraught with potential errors. We sought to elucidate some of the limitations of database research and describe possible solutions to overcome these limitations. Methods: Using a disease model of a rare postsurgical outcome, we evaluated the ability of four different data sources to correctly identify patients who had that outcome both as individual databases and also when used in conjunction with each other. These results were compared with manual chart review. Results: The sensitivity of the various databases to pick up a rare and specific outcome was poor (9.9%-37%), while the specificities were fairly good (91%-96.7%). By combining the databases, the sensitivity was increased to as much as 56.8% without a large decrease in the specificity (85.2%-91.6%). The electronic medical record (EMR) search engine had the highest sensitivity (96.9%) and a high specificity (89.3%) with a very high negative predictive value (99.4%). Conclusion: For rare and specific diseases or outcomes, a single data source search methodology can miss large numbers of patients and potentially bias study results. Combining overlapping databases can improve the ability to capture these rare diseases or outcomes. While chart review remains the most accurate way to obtain complete case capture, new tools like EMR search engines can facilitate the efficiency of this process without sacrificing search quality.
机译:背景:无论是从临床信息系统还是从登记处获得的数据,都被二次使用来进行罕见疾病的临床研究是一种常见的做法,但是充满了潜在的错误。我们试图阐明数据库研究的一些局限性,并描述克服这些局限性的可能解决方案。方法:使用罕见的术后预后的疾病模型,我们评估了四个不同数据源正确识别具有该预后的患者的能力,这些患者既可以作为独立数据库,也可以彼此结合使用。将这些结果与手动图表审查进行了比较。结果:各种数据库获取稀有特异性结果的敏感性较差(9.9%-37%),而特异性相当好(91%-96.7%)。通过合并数据库,灵敏度提高到了56.8%,而特异性却没有大幅度下降(85.2%-91.6%)。电子病历(EMR)搜索引擎具有最高的敏感性(96.9%)和高的特异性(89.3%),具有非常高的阴性预测值(99.4%)。结论:对于罕见和特定的疾病或结果,单一数据源搜索方法可能会遗漏大量患者,并可能使研究结果产生偏差。合并重叠的数据库可以提高捕获这些罕见疾病或结果的能力。尽管图表审查仍然是获得完整案例记录的最准确方法,但是EMR搜索引擎等新工具可以在不牺牲搜索质量的情况下提高此过程的效率。

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