Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies
Abstract Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies...
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Nature Portfolio
2021
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oai:doaj.org-article:bd5ab1833d77497f9aa19329c049a17d2021-12-02T16:17:26ZQuantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies10.1038/s41746-021-00488-32398-6352https://doaj.org/article/bd5ab1833d77497f9aa19329c049a17d2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00488-3https://doaj.org/toc/2398-6352Abstract Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.Danqing XuChen WangAtlas KhanNing ShangZihuai HeAdam GordonIftikhar J. KulloShawn MurphyYizhao NiWei-Qi WeiAli GharaviKrzysztof KirylukChunhua WengIuliana Ionita-LazaNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
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EN |
| topic |
Computer applications to medicine. Medical informatics R858-859.7 |
| spellingShingle |
Computer applications to medicine. Medical informatics R858-859.7 Danqing Xu Chen Wang Atlas Khan Ning Shang Zihuai He Adam Gordon Iftikhar J. Kullo Shawn Murphy Yizhao Ni Wei-Qi Wei Ali Gharavi Krzysztof Kiryluk Chunhua Weng Iuliana Ionita-Laza Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| description |
Abstract Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification. |
| format |
article |
| author |
Danqing Xu Chen Wang Atlas Khan Ning Shang Zihuai He Adam Gordon Iftikhar J. Kullo Shawn Murphy Yizhao Ni Wei-Qi Wei Ali Gharavi Krzysztof Kiryluk Chunhua Weng Iuliana Ionita-Laza |
| author_facet |
Danqing Xu Chen Wang Atlas Khan Ning Shang Zihuai He Adam Gordon Iftikhar J. Kullo Shawn Murphy Yizhao Ni Wei-Qi Wei Ali Gharavi Krzysztof Kiryluk Chunhua Weng Iuliana Ionita-Laza |
| author_sort |
Danqing Xu |
| title |
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| title_short |
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| title_full |
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| title_fullStr |
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| title_full_unstemmed |
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies |
| title_sort |
quantitative disease risk scores from ehr with applications to clinical risk stratification and genetic studies |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/bd5ab1833d77497f9aa19329c049a17d |
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