FDrisk: development of a validated risk assessment tool for Fabry disease utilizing electronic health record data.
FDrisk: development of a validated risk assessment tool for Fabry disease utilizing electronic health record data.
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DOI：
10.1007/s44162-023-00026-7
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发表时间：
2024
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Fabry病(FD)是一种罕见的X连锁溶酶体蓄积性疾病，其临床表现具有很大的变异性和进行性的多系统器官损害。缺乏对FD的认识和频繁的误诊会导致长时间的诊断延误。为了解决对早期诊断的迫切需求，我们创建了一个在线风险评估评分工具FDRisk，用于预测个人患FD的风险，并促进诊断测试和临床评估。利用电子健康记录，从在Emory Lysosomal Storage疾病中心治疗的随机选择的、身份明确的FD患者中回顾收集数据。确定的阴性对照是从Fabry疾病诊断测试和教育项目数据库中随机选择的，该数据库是美国肾脏患者协会患者教育和研究中心的一个项目。FD的诊断是通过Gla的致病变异体和/或白细胞α-GalA异常水平的证据来证明的。最初确定了FD的30个特征临床特征，随后将其归类为16个临床协变量，作为FD风险的预测因素。建立了一个总体预测模型和两个性别预测模型。用260个样本(病例组和对照组)对风险预测模型进行训练。197个独立样本(30个病例，167个对照)被用于检验模型的性能。使用0.5的阈值评估预测准确性，以确定预测病例与对照。总体风险预测模型的敏感性为80%，特异性为83.8%，阳性预测值为47.1%。男性模型的敏感性为75%，特异性为95.8%，阳性预测值为75%。女性模型的敏感性为83.3%，特异性为81.3%，阳性预测值为45.5%。风险评分在50%或以上的患者被归类为FD的“风险”，应该被送去进行诊断性测试。我们开发了一个统计风险预测模型FDRisk，这是一个经过验证的、临床医生友好的在线风险评估评分工具，用于预测个人患FD的风险，并促进诊断测试和临床评估。作为一种易于使用、用户友好的评分工具，我们相信实施FDRisk将显著减少诊断时间，并允许更早启动针对FD的治疗。
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. 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. 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. 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.