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

Abstract

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.

Keywords

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