利用出生证明和国际疾病分类代码开发和验证唐氏综合症诊断算法。

IF 2 4区 医学 Q2 PEDIATRICS
Lin Ammar, Kristin Bird, Hui Nian, Angela Maxwell-Horn, Rees Lee, Tan Ding, Corinne Riddell, Tebeb Gebretsadik, Brittney Snyder, Tina Hartert, Pingsheng Wu
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引用次数: 0

摘要

目的我们旨在开发一种算法,利用管理数据准确识别患有唐氏综合征(DS)的儿童:我们对 2000 年至 2017 年间出生、加入田纳西州医疗补助计划(Tennessee Medicaid Program,TennCare)、出生证明上有唐氏综合征编码或使用国际疾病分类(ICD)代码列出诊断结果(疑似唐氏综合征)、在美国综合性学术医疗中心范德比尔特大学医学中心接受治疗的儿童进行了队列识别。疑似 DS 患儿的定义是:(a) 出生证明上有核型确诊的 DS;(b) 出生证明上有核型待定的 DS(如果未指定检测类型,则仅有 DS),且在出生后的前 6 年中至少有两次因 DS 就诊的经历;或 (c) 在出生后的前 6 年中至少有三次因 DS 就诊的经历,且第一次和最后一次至少相隔 30 天。报告了该算法的阳性预测值(PPV)和 95% 的置信区间(CI):结果:在411名疑似DS患儿中,有354名(86.1%)被该算法定义为患有DS。根据病历审查,该算法正确识别出 347 名 DS 患儿(PPV = 98%,95%CI:96.0-99.0%)。在该算法定义为不患有DS的57名儿童中,有50名(97.7%,95%CI:76.8-93.9%)经病历审查确认为不患有DS:利用出生证明数据和/或 ICD 编码准确识别 DS 患者的算法为利用管理数据研究 DS 提供了一种有价值的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes.

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data.

Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. The positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported.

Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV = 98%, 95%CI: 96.0-99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8-93.9%) were confirmed as not having DS by medical chart review.

Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data.

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来源期刊
Children-Basel
Children-Basel PEDIATRICS-
CiteScore
2.70
自引率
16.70%
发文量
1735
审稿时长
6 weeks
期刊介绍: Children is an international, open access journal dedicated to a streamlined, yet scientifically rigorous, dissemination of peer-reviewed science related to childhood health and disease in developed and developing countries. The publication focuses on sharing clinical, epidemiological and translational science relevant to children’s health. Moreover, the primary goals of the publication are to highlight under‑represented pediatric disciplines, to emphasize interdisciplinary research and to disseminate advances in knowledge in global child health. In addition to original research, the journal publishes expert editorials and commentaries, clinical case reports, and insightful communications reflecting the latest developments in pediatric medicine. By publishing meritorious articles as soon as the editorial review process is completed, rather than at predefined intervals, Children also permits rapid open access sharing of new information, allowing us to reach the broadest audience in the most expedient fashion.
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