Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates.
Sci Rep. 2024 Sep 14;14(1):21523. doi: 10.1038/s41598-024-72259-5.
Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann-Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD.
庞贝病（OMIM #232300）是一种罕见的遗传疾病，由于酶缺乏，导致体内糖原堆积，尤其对心脏和肌肉造成损害。婴儿型庞贝病（IOPD）需要紧急治疗以防止死亡，但由于这些方法无法获得，往往会延迟诊断。我们的研究旨在使用电子健康记录（EHR）简化阿联酋的 IOPD 诊断，以便更快、更准确地发现疾病并及时开始治疗。本研究利用阿联酋阿布扎比医疗保健公司（SEHA）医疗网络的电子健康记录，开发了一种通过仪表板实现的基于专家规则的筛选方法。该研究共纳入 6 名确诊的 IOPD 患者，并对 93365 名受试者进行了筛查。根据患者的年龄、特定症状和肌酸激酶水平，制定了专家规则来识别潜在的高危 IOPD 患者。采用准确性、敏感性和特异性对提出的方法进行了评估。该方法准确地识别了 5 个真阳性、1 个假阴性和 4 个假阳性 IOPD 病例。假阴性病例涉及同时患有庞贝病和先天性心脏病的患者。由于没有测量肌酸激酶，对 CHD 的关注导致对 Pompe 病的忽视。假阳性病例被诊断为线粒体 DNA 耗竭综合征 12-A（SLC25A4 基因）、免疫缺陷-71（ARPC1B 突变）、尼曼-匹克病 C 型（NPC1 基因突变导致移码）和 B 组链球菌脑膜炎。通过将专家规则与仪表板集成的方法，方便了数据的有效可视化和患者的自动筛选，有助于早期发现 Pompe 病。鼓励未来的研究调查机器学习方法的应用，以进一步提高识别 IOPD 患者的精度和效率。
