An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records

Abstract

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.

Keywords: Dashboard analytics; Electronic health records; Expert rule-based screening; Infantile onset Pompe disease; Pompe disease; PowerBI.

Figure 1

Methodology of this study.

Figure 2

Population count and Pompe disease distribution by country (Transformed using Square Root). This map was generated using Python 3.10 with pandas, geopandas, matplotlib, and numpy libraries. Map data source: World Food Programme (UN agency), accessed via OpenDataSoft (https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries).

Figure 3

Comparison of CK levels between IOPD and screened patients.

Figure 4

Prevalence of symptoms in IOPD and screened patients.

Figure 5

Cramer’s V correlation matrix of symptoms. The heatmap displays the strength of association between various symptoms and IOPD, with values ranging from 0 (no association) to 1 (perfect association). Asterisks (*) indicate statistically significant associations ($p\le 0.05$).

Figure 6

Top 20 important features identified by Random Forest feature selection.

Figure 7

Symptomatic networks in infantile onset Pompe disease: A Random Forest feature importance analysis identifying core symptoms and related conditions (Cluster 1).

Figure 8

Symptomatic networks in infantile onset Pompe disease: A Random Forest feature importance analysis identifying core symptoms and related conditions (Cluster 2).

Figure 9

Symptomatic networks in infantile onset Pompe disease: A Random Forest feature importance analysis identifying core symptoms and related conditions (Cluster 3).

Algorithm 1

Expert-rules to identify high-risk IOPD patients.

Figure 10

Developed dashboard with integrated expert rules.