Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers

Cartik Kothari, Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
Siddharth Srivastava, Department of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Youssef Kousa, Division of Neurology, Children's National Hospital, Washington, DC, 20010, USA.
Rima Izem, Division of Biostatistics and Study Methodology, Children's National Research Institute, Silver Spring, MD, 20910, USA.
Marcin Gierdalski, Division of Biostatistics and Study Methodology, Children's National Hospital, Washington, DC, 20010, USA.
Dongkyu Kim, Division of Biostatistics and Study Methodology, Children's National Hospital, Washington, DC, 20010, USA.
Amy Good, Institute for Translational Health Sciences, University of Washington, Seattle, WA, 98195, USA.
Kira A. Dies, Department of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Gregory Geisel, Department of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Hiroki Morizono, Center for Genetic Medicine Research, Children's National Hospital, Washington, DC, 20010, USA.
Vittorio Gallo, Center for Neuroscience Research, Children's National Research Institute, Children's National Hospital, Washington, DC, 20010, USA.
Scott L. Pomeroy, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Gwenn A. Garden, Department of Neurology and Center on Human Development and Disability, University of Washington, Seattle, WA, 98195, USA.
Lisa Guay-Woodford, Center for Translational Research, Children's National Hospital, Washington, DC, 20010, USA.
Mustafa Sahin, Department of Neurology, Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Paul Avillach, Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA. paul_avillach@hms.harvard.edu.

Document Type

Journal Article

Publication Date

3-23-2022

Journal

Journal of neurodevelopmental disorders

Volume

14

Issue

1

DOI

10.1186/s11689-022-09434-0

Keywords

Autism; Computational phenotype; Electronic health records; Genetic disease; Rare disease

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.

Department

Genomics and Precision Medicine

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