An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities.
An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities.
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DOI：
10.1186/s11689-022-09447-9
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发表时间：
2022-06-11
影响因子：
4.9
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中科院分区：
文献类型：
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在超过一半的儿科病例中，ADHD存在合并症，并且通常不清楚引起损害的症状是由于合并症还是基础ADHD。合并症增加了更严重和持续病程的可能性，并使治疗决策复杂化。因此，建立一种识别ADHD和合并症的算法，以改善使用生物储存库和其他电子记录数据对ADHD的研究是非常重要的。使用旨在包括其他精神疾病的电子算法，可以准确区分ADHD与ADHD合并症。我们试图开发一种EHR表型算法，以更有效地将ADHD病例与合并症的ADHD病例隔离开来，以便在大型生物库中进行有效的未来搜索。我们开发了一种多源算法，可以更完整地查看患者的EHR，利用费城儿童医院（CHOP）应用基因组学中心（CAG）的生物库。我们使用国际疾病和相关健康问题统计分类（ICD）代码，用药史和ADHD特定的关键词以及共病精神疾病来挖掘2009年至2016年的EHR，以促进基因型-表型相关性工作。图表摘要和行为调查增加了支持精神病诊断的证据。最值得注意的是，该算法没有排除其他精神疾病，就像许多以前的算法一样。对照组没有精神和其他神经系统疾病。参与者在CHOP参加了各种CAG研究，并完成了广泛的知情同意书，包括同意对EHR进行前瞻性分析。我们创建并验证了一种基于EHR的算法，用于在儿科医疗保健网络中对ADHD和共病精神状态进行分类，以用于未来的遗传分析和基于发现的研究。在这项回顾性病例对照研究中，包括来自51，293名受试者的数据，发现了5840例ADHD病例，其中46.1%单独患有ADHD，53.9%患有ADHD与精神病合并症。我们的主要研究结果是检查该算法是否可以识别和区分ADHD排除病例和ADHD共病病例。结果表明，ICD编码加上药物搜索揭示了大多数病例。我们发现ADHD相关的关键字并没有增加产量。然而，我们发现包括ADHD特定药物在内的病例数增加了21%。ADHD病例的阳性预测值（PPV）为95%，对照组为93%。我们建立了一种新的算法，并证明了电子算法方法准确诊断ADHD和合并症的可行性，验证了我们大型生物储存库的效率，以进一步进行基于遗传发现的分析。ClinicalTrials.gov，NCT02286817。首次发布于2014年11月10日。ClinicalTrials.gov，NCT 02777931。首次发布于2016年5月19日。ClinicalTrials.gov，NCT 03006367。首次发布于2016年12月30日。ClinicalTrials.gov，NCT02895906。首次发布于2016年9月12日。在线版本包含补充材料，可通过10.1186/s11689-022-09447-9获得。
In over half of pediatric cases, ADHD presents with comorbidities, and often, it is unclear whether the symptoms causing impairment are due to the comorbidity or the underlying ADHD. Comorbid conditions increase the likelihood for a more severe and persistent course and complicate treatment decisions. Therefore, it is highly important to establish an algorithm that identifies ADHD and comorbidities in order to improve research on ADHD using biorepository and other electronic record data. It is feasible to accurately distinguish between ADHD in isolation from ADHD with comorbidities using an electronic algorithm designed to include other psychiatric disorders. We sought to develop an EHR phenotype algorithm to discriminate cases with ADHD in isolation from cases with ADHD with comorbidities more effectively for efficient future searches in large biorepositories. We developed a multi-source algorithm allowing for a more complete view of the patient’s EHR, leveraging the biobank of the Center for Applied Genomics (CAG) at Children’s Hospital of Philadelphia (CHOP). We mined EHRs from 2009 to 2016 using International Statistical Classification of Diseases and Related Health Problems (ICD) codes, medication history and keywords specific to ADHD, and comorbid psychiatric disorders to facilitate genotype-phenotype correlation efforts. Chart abstractions and behavioral surveys added evidence in support of the psychiatric diagnoses. Most notably, the algorithm did not exclude other psychiatric disorders, as is the case in many previous algorithms. Controls lacked psychiatric and other neurological disorders. Participants enrolled in various CAG studies at CHOP and completed a broad informed consent, including consent for prospective analyses of EHRs. We created and validated an EHR-based algorithm to classify ADHD and comorbid psychiatric status in a pediatric healthcare network to be used in future genetic analyses and discovery-based studies. In this retrospective case-control study that included data from 51,293 subjects, 5840 ADHD cases were discovered of which 46.1% had ADHD alone and 53.9% had ADHD with psychiatric comorbidities. Our primary study outcome was to examine whether the algorithm could identify and distinguish ADHD exclusive cases from ADHD comorbid cases. The results indicate ICD codes coupled with medication searches revealed the most cases. We discovered ADHD-related keywords did not increase yield. However, we found including ADHD-specific medications increased our number of cases by 21%. Positive predictive values (PPVs) were 95% for ADHD cases and 93% for controls. We established a new algorithm and demonstrated the feasibility of the electronic algorithm approach to accurately diagnose ADHD and comorbid conditions, verifying the efficiency of our large biorepository for further genetic discovery-based analyses. ClinicalTrials.gov, NCT02286817. First posted on 10 November 2014. ClinicalTrials.gov, NCT02777931. First posted on 19 May 2016. ClinicalTrials.gov, NCT03006367. First posted on 30 December 2016. ClinicalTrials.gov, NCT02895906. First posted on 12 September 2016. The online version contains supplementary material available at 10.1186/s11689-022-09447-9.
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DOI：
10.1136/amiajnl-2013-001935
发表时间：
2014-03
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Shivade C;Raghavan P;Fosler-Lussier E;Embi PJ;Elhadad N;Johnson SB;Lai AM
通讯作者：
Lai AM
影响因子：
2.9
作者：
Bush, Ruth A.;Connelly, Cynthia D.;Chiang, George J.
通讯作者：
Chiang, George J.
影响因子：
6.4
作者：
Reale, Laura;Bartoli, Beatrice;Bonati, Maurizio
通讯作者：
Bonati, Maurizio
影响因子：
3
作者：
Daley, Matthew F.;Newton, Douglas A.;Bussing, Regina
通讯作者：
Bussing, Regina
DOI：
10.1080/15374416.2017.1417860
发表时间：
2018-03
期刊：
Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53
影响因子：
--
作者：
Danielson ML;Bitsko RH;Ghandour RM;Holbrook JR;Kogan MD;Blumberg SJ
通讯作者：
Blumberg SJ