基于住院患者的急性失代偿性心力衰竭的可计算表型的推导和验证。
Derivation and validation of a computable phenotype for acute decompensated heart failure in hospitalized patients.
机构信息
Multidisciplinary Epidemiological and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
Department of Anesthesiology & Perioperative Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
出版信息
BMC Med Inform Decis Mak. 2020 May 7;20(1):85. doi: 10.1186/s12911-020-1092-5.
BACKGROUND
With higher adoption of electronic health records at health-care centers, electronic search algorithms (computable phenotype) for identifying acute decompensated heart failure (ADHF) among hospitalized patients can be an invaluable tool to enhance data abstraction accuracy and efficacy in order to improve clinical research accrual and patient centered outcomes. We aimed to derive and validate a computable phenotype for ADHF in hospitalized patients.
METHODS
We screened 256, 443 eligible (age > 18 years and with prior research authorization) individuals who were admitted to Mayo Clinic Hospital in Rochester, MN, from January 1, 2006, through December 31, 2014. Using a randomly selected derivation cohort of 938 patients, several iterations of a free-text electronic search were developed and refined. The computable phenotype was subsequently validated in an independent cohort 100 patients. The sensitivity and specificity of the computable phenotype were compared to the gold standard (expert review of charts) and International Classification of Diseases-9 (ICD-9) codes for Acute Heart Failure.
RESULTS
In the derivation cohort, the computable phenotype achieved a sensitivity of 97.5%, and specificity of 100%, whereas ICD-9 codes for Acute Heart Failure achieved a sensitivity of 47.5% and specificity of 96.7%. When all Heart Failure codes (ICD-9) were used, sensitivity and specificity were 97.5 and 86.6%, respectively. In the validation cohort, the sensitivity and specificity of the computable phenotype were 100 and 98.5%. The sensitivity and specificity for the ICD-9 codes (Acute Heart Failure) were 42 and 98.5%. Upon use of all Heart Failure codes (ICD-9), sensitivity and specificity were 96.8 and 91.3%.
CONCLUSIONS
Our results suggest that using computable phenotype to ascertain ADHF from the clinical notes contained within the electronic medical record are feasible and reliable. Our computable phenotype outperformed ICD-9 codes for the detection of ADHF.
背景
随着医疗保健中心电子病历的广泛采用，用于识别住院患者中急性失代偿性心力衰竭（ADHF）的电子搜索算法（可计算表型）可以成为提高数据提取准确性和效率的宝贵工具，从而提高临床研究的入组率和以患者为中心的结果。我们旨在为住院患者中 ADHF 开发和验证一种可计算的表型。
方法
我们筛选了 2006 年 1 月 1 日至 2014 年 12 月 31 日期间在明尼苏达州罗切斯特市梅奥诊所医院住院的 256443 名符合条件的（年龄>18 岁且有事先研究授权）个体。使用随机选择的 938 名患者的推导队列，开发并改进了几个迭代的自由文本电子搜索。随后在独立队列的 100 名患者中验证了可计算的表型。将可计算表型的敏感性和特异性与专家审查图表的金标准和国际疾病分类第 9 版（ICD-9）急性心力衰竭代码进行比较。
结果
在推导队列中，可计算的表型达到了 97.5%的敏感性和 100%的特异性，而 ICD-9 急性心力衰竭代码的敏感性为 47.5%，特异性为 96.7%。当使用所有心力衰竭代码（ICD-9）时，敏感性和特异性分别为 97.5%和 86.6%。在验证队列中，可计算表型的敏感性和特异性分别为 100%和 98.5%。ICD-9 代码（急性心力衰竭）的敏感性和特异性分别为 42%和 98.5%。使用所有心力衰竭代码（ICD-9）时，敏感性和特异性分别为 96.8%和 91.3%。
结论
我们的结果表明，使用可计算表型从电子病历中的临床记录中确定 ADHF 是可行且可靠的。我们的可计算表型在检测 ADHF 方面优于 ICD-9 代码。
Zotero 插件