RESUMEN
Purpose: Fabry disease (FD) is a rare, X-linked, lysosomal storage disease characterized by great variability in clinical presentation and progressive multisystemic organ damage. Lack of awareness of FD and frequent misdiagnoses cause long diagnostic delays. To address the urgent need for earlier diagnosis, we created an online, risk-assessment scoring tool, the FDrisk, for predicting an individual's risk for FD and prompting diagnostic testing and clinical evaluation. Methods: Utilizing electronic health records, data were collected retrospectively from randomly selected, deidentified patients with FD treated at the Emory Lysosomal Storage Disease Center. Deidentified, negative controls were randomly selected from the Fabry Disease Diagnostic Testing and Education project database, a program within the American Association of Kidney Patients Center for Patient Education and Research. Diagnosis of FD was documented by evidence of a pathogenic variant in GLA and/or an abnormal level of leukocyte α-Gal A. Thirty characteristic clinical features of FD were initially identified and subsequently curated into 16 clinical covariates used as predictors for the risk of FD. An overall prediction model and two sex-specific prediction models were built. Two-hundred and sixty samples (130 cases, 130 controls) were used to train the risk prediction models. One-hundred and ninety-seven independent samples (30 cases, 167 controls) were used for testing model performance. Prediction accuracy was evaluated using a threshold of 0.5 to determine a predicted case vs. control. Results: The overall risk prediction model demonstrated 80% sensitivity, 83.8% specificity, and positive predictive value of 47.1%. The male model demonstrated 75% sensitivity, 95.8% specificity, and positive predictive value of 75%. The female model demonstrated 83.3% sensitivity, 81.3% specificity, and positive predictive value of 45.5%. Patients with risk scores at or above 50% are categorized as "at risk" for FD and should be sent for diagnostic testing. Conclusion: We have developed a statistical risk prediction model, the FDrisk, a validated, clinician-friendly, online, risk-assessment scoring tool for predicting an individual's risk for FD and prompting diagnostic testing and clinical evaluation. As an easily accessible, user-friendly scoring tool, we believe implementing the FDrisk will significantly decrease the time to diagnosis and allow earlier initiation of FD-specific therapy.
RESUMEN
Background: Hereditary angioedema (HAE) is a genetic condition characterized by dysregulation of the contact (kallikrein-bradykinin) pathway, leading to recurrent episodes of angioedema. Objective: This project sought to determine whether a suspicion index screening tool using electronic health record (EHR) data can identify patients with an increased likelihood of a diagnosis of HAE. Methods: A suspicion index screening tool for HAE was created and validated by using known patients with HAE from the medical literature as well as positive and negative controls from HAE-focused centers. Through the use of key features of medical and family history, a series of logistic regression models for 5 known genetic causes of HAE were created. Top variables populated the digital suspicion scoring system and were run against deidentified EHR data. Patients at 2 diverse sites were categorized as being at increased, possible, or no increased risk of HAE. Results: Prediction scoring using the strongest 13 variables on the "real-world" EHR-positive control data identified all but 1 patient with C1 inhibitor deficiency and patient with non-C1 inhibitor deficiency without false-positive results. The 2 missed patients had no documented family history of HAE in their EHR. When the prediction scoring variables were expanded to 25, the screening algorithm approached 100% sensitivity and specificity. The 25-variable algorithm run on general population EHR data identified 26 patients at the medical centers as being at increased risk for HAE. Conclusions: These results suggest that development, validation, and implementation of suspicion index screening tools can be useful to aid providers in identifying patients with rare genetic conditions.
