Your browser doesn't support javascript.
loading
Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.
Tinker, Rory J; Peterson, Josh; Bastarache, Lisa.
Afiliación
  • Tinker RJ; Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN.
  • Peterson J; Vanderbilt University Medical Center, Department of Medicine, Nashville, TN; Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN.
  • Bastarache L; Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN. Electronic address: Lisa.bastarache@vumc.org.
Genet Med ; 25(10): 100921, 2023 10.
Article en En | MEDLINE | ID: mdl-37337966
ABSTRACT

PURPOSE:

To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR).

METHODS:

We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders.

RESULTS:

We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10-4, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results.

CONCLUSION:

Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.
Asunto(s)
Palabras clave

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Algoritmos / Registros Electrónicos de Salud Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Genet Med Asunto de la revista: GENETICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Túnez

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Algoritmos / Registros Electrónicos de Salud Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Límite: Humans Idioma: En Revista: Genet Med Asunto de la revista: GENETICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Túnez