Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers
Abstract Background Computational phenotypes are most often combinations of patient billing codes that are highly predictive of disease using electronic health records (EHR). In the case of rare diseases that can only be diagnosed by genetic testing, computational phenotypes identify patient cohorts...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | Journal of Neurodevelopmental Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s11689-022-09434-0 |
| _version_ | 1811331934179033088 |
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| author | Cartik Kothari Siddharth Srivastava Youssef Kousa Rima Izem Marcin Gierdalski Dongkyu Kim Amy Good Kira A. Dies Gregory Geisel Hiroki Morizono Vittorio Gallo Scott L. Pomeroy Gwenn A. Garden Lisa Guay-Woodford Mustafa Sahin Paul Avillach |
| author_facet | Cartik Kothari Siddharth Srivastava Youssef Kousa Rima Izem Marcin Gierdalski Dongkyu Kim Amy Good Kira A. Dies Gregory Geisel Hiroki Morizono Vittorio Gallo Scott L. Pomeroy Gwenn A. Garden Lisa Guay-Woodford Mustafa Sahin Paul Avillach |
| author_sort | Cartik Kothari |
| collection | DOAJ |
| description | Abstract Background Computational phenotypes are most often combinations of patient billing codes that are highly predictive of disease using electronic health records (EHR). In the case of rare diseases that can only be diagnosed by genetic testing, computational phenotypes identify patient cohorts for genetic testing and possible diagnosis. This article details the validation of a computational phenotype for PTEN hamartoma tumor syndrome (PHTS) against the EHR of patients at three collaborating clinical research centers: Boston Children's Hospital, Children's National Hospital, and the University of Washington. Methods A combination of billing codes from the International Classification of Diseases versions 9 and 10 (ICD-9 and ICD-10) for diagnostic criteria postulated by a research team at Cleveland Clinic was used to identify patient cohorts for genetic testing from the clinical data warehouses at the three research centers. Subsequently, the EHR—including billing codes, clinical notes, and genetic reports—of these patients were reviewed by clinical experts to identify patients with PHTS. Results The PTEN genetic testing yield of the computational phenotype, the number of patients who needed to be genetically tested for incidence of pathogenic PTEN gene variants, ranged from 82 to 94% at the three centers. Conclusions Computational phenotypes have the potential to enable the timely and accurate diagnosis of rare genetic diseases such as PHTS by identifying patient cohorts for genetic sequencing and testing. |
| first_indexed | 2024-04-13T16:28:20Z |
| format | Article |
| id | doaj.art-073fec6cc62f415fa02521360854397b |
| institution | Directory Open Access Journal |
| issn | 1866-1947 1866-1955 |
| language | English |
| last_indexed | 2024-04-13T16:28:20Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Neurodevelopmental Disorders |
| spelling | doaj.art-073fec6cc62f415fa02521360854397b2022-12-22T02:39:40ZengBMCJournal of Neurodevelopmental Disorders1866-19471866-19552022-03-011411810.1186/s11689-022-09434-0Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centersCartik Kothari0Siddharth Srivastava1Youssef Kousa2Rima Izem3Marcin Gierdalski4Dongkyu Kim5Amy Good6Kira A. Dies7Gregory Geisel8Hiroki Morizono9Vittorio Gallo10Scott L. Pomeroy11Gwenn A. Garden12Lisa Guay-Woodford13Mustafa Sahin14Paul Avillach15Department of Biomedical Informatics, Harvard Medical SchoolDepartment of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical SchoolDivision of Neurology, Children’s National HospitalDivision of Biostatistics and Study Methodology, Children’s National Research InstituteDivision of Biostatistics and Study Methodology, Children’s National HospitalDivision of Biostatistics and Study Methodology, Children’s National HospitalInstitute for Translational Health Sciences, University of WashingtonDepartment of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical SchoolDepartment of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical SchoolCenter for Genetic Medicine Research, Children’s National HospitalCenter for Neuroscience Research, Children’s National Research Institute, Children’s National HospitalDepartment of Neurology, Boston Children’s Hospital, Harvard Medical SchoolDepartment of Neurology and Center on Human Development and Disability, University of WashingtonCenter for Translational Research, Children’s National HospitalDepartment of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, Harvard Medical SchoolDepartment of Biomedical Informatics, Harvard Medical SchoolAbstract Background Computational phenotypes are most often combinations of patient billing codes that are highly predictive of disease using electronic health records (EHR). In the case of rare diseases that can only be diagnosed by genetic testing, computational phenotypes identify patient cohorts for genetic testing and possible diagnosis. This article details the validation of a computational phenotype for PTEN hamartoma tumor syndrome (PHTS) against the EHR of patients at three collaborating clinical research centers: Boston Children's Hospital, Children's National Hospital, and the University of Washington. Methods A combination of billing codes from the International Classification of Diseases versions 9 and 10 (ICD-9 and ICD-10) for diagnostic criteria postulated by a research team at Cleveland Clinic was used to identify patient cohorts for genetic testing from the clinical data warehouses at the three research centers. Subsequently, the EHR—including billing codes, clinical notes, and genetic reports—of these patients were reviewed by clinical experts to identify patients with PHTS. Results The PTEN genetic testing yield of the computational phenotype, the number of patients who needed to be genetically tested for incidence of pathogenic PTEN gene variants, ranged from 82 to 94% at the three centers. Conclusions Computational phenotypes have the potential to enable the timely and accurate diagnosis of rare genetic diseases such as PHTS by identifying patient cohorts for genetic sequencing and testing.https://doi.org/10.1186/s11689-022-09434-0Rare diseaseGenetic diseaseComputational phenotypeElectronic health recordsAutism |
| spellingShingle | Cartik Kothari Siddharth Srivastava Youssef Kousa Rima Izem Marcin Gierdalski Dongkyu Kim Amy Good Kira A. Dies Gregory Geisel Hiroki Morizono Vittorio Gallo Scott L. Pomeroy Gwenn A. Garden Lisa Guay-Woodford Mustafa Sahin Paul Avillach Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers Journal of Neurodevelopmental Disorders Rare disease Genetic disease Computational phenotype Electronic health records Autism |
| title | Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers |
| title_full | Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers |
| title_fullStr | Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers |
| title_full_unstemmed | Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers |
| title_short | Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers |
| title_sort | validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic pten variants across three centers |
| topic | Rare disease Genetic disease Computational phenotype Electronic health records Autism |
| url | https://doi.org/10.1186/s11689-022-09434-0 |
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