Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers / Cartik KOTHARI in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers Type de document : texte imprimé Auteurs : Cartik KOTHARI, Auteur ; Siddharth SRIVASTAVA, Auteur ; Youssef KOUSA, Auteur ; Rima IZEM, Auteur ; Marcin GIERDALSKI, Auteur ; Dongkyu KIM, Auteur ; Amy GOOD, Auteur ; Kira A. DIES, Auteur ; Gregory GEISEL, Auteur ; Hiroki MORIZONO, Auteur ; Vittorio GALLO, Auteur ; Scott L. POMEROY, Auteur ; Gwenn A. GARDEN, Auteur ; Lisa GUAY-WOODFORD, Auteur ; Mustafa SAHIN, Auteur ; Paul AVILLACH, Auteur Langues : Anglais (eng) Mots-clés : Electronic Health Records  Genetic Testing  Hamartoma Syndrome, Multiple/diagnosis/genetics/pathology  Humans  PTEN Phosphohydrolase/genetics  Phenotype  Autism  Computational phenotype  Genetic disease  Rare disease  from GLG, Guidepoint (which connected to a client, Fortress Biotech), and  Novartis. 3. YK has no competing interests to declare. 4. RI has no competing  interests to declare. 5. MG has no competing interests to declare. 6. DK has no  competing interests to declare. 7. AG has no competing interests to declare. 8.  KAD has no competing interests to declare. 9. GG has no competing interests to  declare. 10. HM has no competing interests to declare. 11. VG has no competing  interests to declare. 12. SP has no competing interests to declare. 13. GAG has  no competing interests to declare. 14. LGW has no competing interests to declare.  15. MS reports grant support from Novartis, Roche, Biogen, Astellas, Aeovian,  Bridgebio, Aucta, and Quadrant Biosciences. He has served on Scientific Advisory  Boards for PTEN Research, Novartis, Roche, Celgene, Regenxbio, Alkermes, and  Takeda. 16. PA has no competing interests to declare Index. décimale : PER Périodiques Résumé : 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. En ligne : https://dx.doi.org/10.1186/s11689-022-09434-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers [texte imprimé] / Cartik KOTHARI, Auteur ; Siddharth SRIVASTAVA, Auteur ; Youssef KOUSA, Auteur ; Rima IZEM, Auteur ; Marcin GIERDALSKI, Auteur ; Dongkyu KIM, Auteur ; Amy GOOD, Auteur ; Kira A. DIES, Auteur ; Gregory GEISEL, Auteur ; Hiroki MORIZONO, Auteur ; Vittorio GALLO, Auteur ; Scott L. POMEROY, Auteur ; Gwenn A. GARDEN, Auteur ; Lisa GUAY-WOODFORD, Auteur ; Mustafa SAHIN, Auteur ; Paul AVILLACH, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Electronic Health Records  Genetic Testing  Hamartoma Syndrome, Multiple/diagnosis/genetics/pathology  Humans  PTEN Phosphohydrolase/genetics  Phenotype  Autism  Computational phenotype  Genetic disease  Rare disease  from GLG, Guidepoint (which connected to a client, Fortress Biotech), and  Novartis. 3. YK has no competing interests to declare. 4. RI has no competing  interests to declare. 5. MG has no competing interests to declare. 6. DK has no  competing interests to declare. 7. AG has no competing interests to declare. 8.  KAD has no competing interests to declare. 9. GG has no competing interests to  declare. 10. HM has no competing interests to declare. 11. VG has no competing  interests to declare. 12. SP has no competing interests to declare. 13. GAG has  no competing interests to declare. 14. LGW has no competing interests to declare.  15. MS reports grant support from Novartis, Roche, Biogen, Astellas, Aeovian,  Bridgebio, Aucta, and Quadrant Biosciences. He has served on Scientific Advisory  Boards for PTEN Research, Novartis, Roche, Celgene, Regenxbio, Alkermes, and  Takeda. 16. PA has no competing interests to declare Index. décimale : PER Périodiques Résumé : 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. En ligne : https://dx.doi.org/10.1186/s11689-022-09434-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

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