背景:戈谢病(GD)是一种罕见的常染色体隐性疾病,与脾肿大等临床特征相关,肝肿大,贫血,血小板减少症,和骨骼异常。根据神经体征的不存在(1型,GD1)或存在(2型和3型),已定义了GD的三种临床形式。早期诊断可以减少严重的可能性,往往是不可逆的并发症。这项研究的目的是验证Gaucher早期诊断共识(GED-C)评分系统中的因素使用来自MaccabiHealthcareServices的电子患者病历的真实数据来区分GD1患者和对照组的能力。以色列第二大国家授权的医疗保健提供者。
方法:我们将GED-C评分系统应用于265例确诊的GD和3445例非GD对照的出生年份匹配,性别,和1998年至2022年确定的社会经济地位。分析基于两个数据库:(1)所有可用数据和(2)除自由文本注释外的所有数据。从适用于GD1的GED-C评分系统中提取每个个体的特征。比较患者和对照组的具体特征比例和总体GED-C评分。训练决策树和随机森林模型以识别区分GD和非GD对照的主要特征。
结果:使用两个数据库,GED-C评分将GD患者与对照组区分开来。数据库的决策树模型显示出良好的准确性(数据库1为0.96[95%CI0.95-0.97];数据库2为0.95[95%CI0.94-0.96]),数据库1的高特异性(0.99[95%CI0.99-1]);数据库2的1.0[95%CI0.99-1]),但敏感性相对较低(数据库1为0.53[95%CI0.46-0.59];数据库2为0.32[95%CI0.25-0.38]))。脾肿大的临床特征,血小板减少症(<50×109/L),在两个数据库中,高铁蛋白血症(300-1000ng/mL)是GD的三个最准确的分类器。
结论:在对真实世界患者数据的分析中,与总评分相比,GED-C评分的某些个体特征在GD患者和对照组之间的区分更成功.增强的诊断模型可能会导致更早,戈谢病的可靠诊断,旨在尽量减少与这种疾病相关的严重并发症。
BACKGROUND: Gaucher disease (GD) is a rare autosomal recessive condition associated with clinical features such as
splenomegaly, hepatomegaly, anemia, thrombocytopenia, and bone abnormalities. Three clinical forms of GD have been defined based on the absence (type 1, GD1) or presence (types 2 and 3) of neurological signs. Early diagnosis can reduce the likelihood of severe, often irreversible complications. The aim of this study was to validate the ability of factors from the Gaucher Earlier Diagnosis
Consensus (GED-C) scoring system to discriminate between patients with GD1 and controls using real-world data from electronic patient medical records from Maccabi Healthcare Services, Israel\'s second-largest state-mandated healthcare provider.
METHODS: We applied the GED-C scoring system to 265 confirmed cases of GD and 3445 non-GD controls matched for year of birth, sex, and socioeconomic status identified from 1998 to 2022. The analyses were based on two databases: (1) all available data and (2) all data except free-text notes. Features from the GED-C scoring system applicable to GD1 were extracted for each individual. Patients and controls were compared for the proportion of the specific features and overall GED-C scores. Decision tree and random forest models were trained to identify the main features distinguishing GD from non-GD controls.
RESULTS: The GED-C scoring distinguished individuals with GD from controls using both databases. Decision tree models for the databases showed good accuracy (0.96 [95% CI 0.95-0.97] for Database 1; 0.95 [95% CI 0.94-0.96] for Database 2), high specificity (0.99 [95% CI 0.99-1]) for Database 1; 1.0 [95% CI 0.99-1] for Database 2), but relatively low sensitivity (0.53 [95% CI 0.46-0.59] for Database 1; 0.32 [95% CI 0.25-0.38]) for Database 2). The clinical features of
splenomegaly, thrombocytopenia (< 50 × 109/L), and hyperferritinemia (300-1000 ng/mL) were found to be the three most accurate classifiers of GD in both databases.
CONCLUSIONS: In this analysis of real-world patient data, certain individual features of the GED-C score discriminate more successfully between patients with GD and controls than the overall score. An enhanced diagnostic model may lead to earlier, reliable diagnoses of Gaucher disease, aiming to minimize the severe complications associated with this disease.