
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72259
10.1038/s41598-024-72259-5
Article
An expert rule-based approach for identifying infantile-onset Pompe disease patients using retrospective electronic health records
Rustamov Jaloliddin 700043175@uaeu.ac.ae

1
Rustamov Zahiriddin 23
Mohamad Mohd Saberi 78
Zaki Nazar 23
Al Tenaiji Amal 4
Al Harbi Mariam 5
Al Jasmi Fatma aljasmif@uaeu.ac.ae

136
1 https://ror.org/01km6p862 grid.43519.3a 0000 0001 2193 6666 Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
2 https://ror.org/01km6p862 grid.43519.3a 0000 0001 2193 6666 Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
3 https://ror.org/01km6p862 grid.43519.3a 0000 0001 2193 6666 ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain, United Arab Emirates
4 https://ror.org/03gd1jf50 grid.415670.1 0000 0004 1773 3278 Department of Pediatrics, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
5 Research Department, SEHA-Corporate Medical and Clinical Affairs, Abu Dhabi, United Arab Emirates
6 Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
7 https://ror.org/01km6p862 grid.43519.3a 0000 0001 2193 6666 Health Data Science Lab, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
8 https://ror.org/04zrbnc33 grid.411865.f 0000 0000 8610 6308 Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
14 9 2024
14 9 2024
2024
14 2152313 4 2024
5 9 2024
© The Author(s) 2024
2024
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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

Pompe disease
Infantile onset Pompe disease
Electronic health records
Expert rule-based screening
PowerBI
Dashboard analytics
Subject terms

Computational biology and bioinformatics
Health care
Sanofi21M142 http://dx.doi.org/10.13039/100024160 ASPIRE VRI-20-10 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Pompe disease (OMIM #232300), also referred to as acid maltase deficiency (AMD) or glycogen storage disease type II (GSDII), is a rare and severe autosomal recessive lysosomal storage disorder arising from inadequate activity of the enzyme acid alpha-glucosidase (GAA)1,2. This deficiency, attributed to mutations in the GAA gene, disrupts the breakdown of glycogen into glucose within lysosomes, causing its accumulation across various tissues, prominently in skeletal and cardiac muscles and the liver and neurons. Characterized as a progressive lysosomal storage disorder, Pompe disease can be fatal, with the lysosomal build-up of glycogen primarily occurring in cardiac and skeletal muscles alongside the nervous system, thereby inducing cellular dysfunction and consequent muscle damage3–5

The incidence and prevalence of Pompe disease vary in reported estimates, with the traditional figure suggesting an overall incidence of 1 in 40,000 live births. Specifically, infantile-onset Pompe disease (IOPD) has an incidence of 1 in 52,000, while the combined incidence for both IOPD and late-onset Pompe disease (LOPD) is approximately 1 in 17,000. However, prospective trials indicate a potentially higher incidence of around 1 in 9000 for both forms of the disease. These statistics underscore the rarity of Pompe disease, with the traditional distribution indicating roughly 3/4 of cases as LOPD and 1/4 as IOPD3–8.

The Pompe disease exhibits two distinct clinical forms: IOPD, typically marked by symptoms emerging within the first year of life, including muscle weakness, hypertrophic cardiomyopathy, hypotonia, respiratory insufficiency, and a high risk of fatality without prompt Enzyme Replacement Therapy (ERT); and LOPD, which manifests after the age of one, often without cardiomyopathy, leading to a more variable and insidious progression involving skeletal muscles, potentially leading to respiratory failure and wheelchair confinement. This disorder’s clinical spectrum ranges from severe cardiac and muscle dysfunction in IOPD to primarily muscular involvement in LOPD, which can manifest from infancy to late adulthood3–5,7,8.

ERT using alglucosidase alfa, such as Myozyme®, is one of the methods of treating Pompe disease, which increases GAA activity in muscles, the heart, and the liver, resulting in glycogen clearance in cardiac and liver muscles. Its approval in 2006 for childhood forms and in 2010 for adult forms has significantly altered the disease’s prognosis, with clinical improvements observed post-treatment, albeit varying based on residual enzyme levels. Recent studies have shown the safety and efficacy of avalglucosidase alfa for IOPD9. While Pompe disease patients are not fully cured, ERT with recombinant human GAA is currently the sole specific treatment, demonstrating enhanced and ventilator-free survival, mainly when initiated earlier, as indicated by a pivotal 2006 study. Clinical trials continue to assess ERT’s safety and efficacy, underscoring its pivotal role in managing this challenging condition3–8.

Newborn screening (NBS) has been implemented in some countries for early diagnosis of IOPD. While various governments have invested in fully or partially subsidized NBS programs across different regions globally, it remains unavailable universally. This limitation leaves a gap, failing to identify patients who might have been previously missed. Notably, there is a drawback to the NBS approach: the high rate of false positives, for example, due to the presence of pseudo-deficiency alleles10. Despite this drawback, NBS provides a significant advantage by ensuring there are no false negatives. Furthermore, NBS also poses several challenges for families. Initial communication about abnormal results can be distressing, and the waiting period for confirmatory diagnosis intensifies anxiety. Many parents seeking information turn to online sources, often encountering alarming data. The emotional impact is heightened for postpartum mothers, and the uncertainty post-diagnosis leads to parental hypervigilance about their child’s health.

Additionally, the early diagnosis can induce anticipatory grief, challenges in acceptance, and the medicalization of an asymptomatic child, complicating the family’s journey to normalcy11. Additionally, the challenge is magnified by the requirement to manually screen thousands of patients to pinpoint a single suspected individual12. This inefficiency is further complicated by the genetic variances in IOPD patients, wherein the disease manifestation correlates with their genotype, precisely the GAA activity level, challenging the accuracy of NBS programs13. Although NBS programs are offered in the UAE, newborns are not screened for Pompe disease14. Despite the substantial financial and resource investment in these programs, the risk of misdiagnosis remains, evidenced by the fact that IOPD patients can wait an average of 2.5 months for an accurate diagnosis12. Tragically, this delay can be life-threatening, given that the average age of death for these patients is reported at 8.7 months, with a mere 25.7% survival rate at 12 months15. There is, however, potential for leveraging automation to address this issue. EHRs encompass an extensive compilation of healthcare information about every patient. Consequently, EHRs are an abundant source of historical data that is already accessible within most healthcare facilities and falls in line with established protocols12. Automated screening using an expert rules-based approach and preprocessed EHRs offers the promise of large-scale, accurate IOPD patient screening. Such an approach could streamline patient selection efforts without altering existing workflows, potentially increasing cost-effectiveness and enhancing the timely discovery and diagnosis of IOPD patients. Considering the limited lifespan of untreated IOPD patients, the limitations of conventional diagnostic methods, and the absence of Pompe screening in the UAE, our study seeks to detail the primary demographic and clinical traits of UAE-based IOPD patients using EHRs. In addition, we aim to introduce expert rules created by expert clinicians and an easy-to-use dashboard that employs the expert rules to identify potential high-risk IOPD patients.Figure 1 Methodology of this study.

Given the paramount importance of early and accurate diagnosis of IOPD to enhance patient outcomes, we developed a robust methodology that leverages EHRs and expert rule-based algorithms. The subsequent section details the methodological framework employed in this study.

Methodology

We applied the rule-based algorithm curated by a domain expert. Based on the expert rules and data, we developed a PowerBI dashboard to visualize and evaluate the performance of the expert rules. The methodology followed in this study is shown in Fig. 1. All experimental work was conducted on a computer with the following specifications: Ryzen 9 5950X, RTX 3090 24GB, and 128GB DDR4 RAM.Table 1 EHR document types and their description.

Document type	Description of document	
Clinical	A detailed record of clinical events associated with specific patients during medical encounters	
Demographics	Patient demographic information, such as gender, nationality, and date of birth	
Diagnosis	The medical conditions diagnosed and the corresponding diagnostic codes assigned to a patient during an outpatient medical visit	
Drugs	Medications prescribed to patients by healthcare providers during a medical appointment	
Encounter	Individual patient hospital admission details, including the date the encounter began, the date it concluded, and any accompanying discharge notes	
Labs	Results of ordered lab tests	
Orders	A comprehensive record of various clinical orders placed for patients during their medical encounters	
Problems	The simultaneous record of a patient’s ongoing medical conditions, as documented by healthcare providers during medical appointments	
Procedures	Procedures ordered by clinicians for patients during an encounter	

Ethical statement

This study was approved by the Institutional Review Board of the Department of Health, Abu Dhabi; Approval number: DOH/CVDC/2021/406 as per the national regulation and Declaration of Helsinki. The requirement for informed consent from the study subjects was waived by the Institutional Review Board of the Department of Health, Abu Dhabi due to the retrospective study design.

Data source and study setting

This study was conducted using EHR data extracted from the healthcare network of the Abu Dhabi Healthcare Company (SEHA), the largest and most comprehensive healthcare network in the UAE, comprising 14 hospitals and 70 clinics. The study population consisted of 6 patients diagnosed with IOPD and 93,365 subjects within SEHA’s healthcare network. The geographic location of SEHA encompasses the entirety of the Emirate of Abu Dhabi, ensuring a diverse patient population representative of the region. Data was extracted from the SEHA medical facility for patients who were born between 1st January 2021 and 31st October 2022, as the focus of this study was on IOPD patients, who are usually below the age of 1.

Data preparation and preprocessing

For this research, SEHA provided us with EHRs, segregated into various document types, each serving a distinct function within the clinical setting. The nuanced descriptions of these document types are outlined in Table 1. The structure of the extracted data was such that each document type had its dedicated folder. Individual patient records pertinent to that specific document type were compactly stored within each folder as .csv files inside a .zip archive. SEHA omitted all personal identifiers from the data to ensure patient confidentiality and preserve anonymity. We used Python programming language to aid with streamlining data preprocessing. We initiated this by programmatically unzipping and merging each document’s data into a single pickle file format, a more space-efficient file format than the traditional .csv file format, as this would allow us to make changes when necessary during the preprocessing stage and for later use. Based on the conditions/symptoms identified as important for the expert rules by the clinicians, we extracted data from diagnosis and laboratory documents to craft a dataset, which would be used for the main page of the dashboard, where the expert rules would be applied, as directly using from diagnosis and laboratory tables would add complexity to the development of the dashboard. We selected the latest entry for each patient’s creatine kinase level from the laboratory table. On the other hand, we filtered rows featuring specific ICD codes linked with cardiomyopathy, hypotonia, physiological developments, feeding difficulties, and respiratory infections from the diagnosis table for each patient, as these features are needed for the expert rules. Lastly, we stored the extracted and preprocessed data in the PostgreSQL database using Python programming language, as the database allows us to maintain the relationship within the data, and it is easy to use with dashboard development platforms such as PowerBI.

Expert rules and visualization

Expert knowledge in specific fields is often encapsulated in a series of predetermined, logical steps known as expert rules. These rules articulate specialists’ deep understanding within a particular area, structuring it in a way that allows for automated application to relevant data16. The rules for identifying high-risk infantile-onset Pompe disease were created by domain experts with deep knowledge and understanding of the disease’s intricacies and clinical manifestations. Recognizing the unique constellation of symptoms and conditions often accompanying this disease in its early stages, the experts meticulously selected a combination of diagnostic codes and clinical parameters to ensure a precise and accurate identification process. These rules encapsulate a comprehensive and evidence-based approach by focusing on specific conditions such as cardiomyopathy, hypotonia, and physiological developments, alongside measurable parameters like elevated creatine kinase levels. The inclusion of age as a primary criterion underscores the emphasis on early detection, given the disease’s onset in infancy. The resulting set of expert rules, thus, offers a reliable framework for the systematic identification of IOPD patients. We used accuracy17, sensitivity, and specificity18 metrics to evaluate the performance of the rule-based approach, as sensitivity and specificity indicate how well a method can identify positive and negative cases, respectively.

Dashboards offer a compelling visual representation of data, transforming intricate information into an accessible and comprehensible format. The dashboard was developed using PowerBI, a robust business analytics tool. PowerBI’s native integration capabilities allowed for seamless EHR data extraction and representation. The drag-and-drop functionality and rich visualization tools created an interactive and informative dashboard. Data integration was achieved by connecting PowerBI directly to the SQL database where the EHRs are stored, ensuring that the displayed data could be easily updated. Data Analysis Expressions (DAX) further enhanced the dashboard’s analytical capabilities, enabling complex calculations and data transformations required by the expert rules.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).

Statistical analyses

Categorical data will be presented through frequency and percentage distributions. Initially, we identified a series of symptoms associated with IOPD from existing literature. These symptoms will be cross-referenced with data points from our dataset, and their representation in both the positive and negative cases will be tabulated to facilitate direct comparison. To evaluate the potential associations between IOPD and the identified symptoms, we will employ the chi-square (χ2) test, establishing a significance threshold of p < 0.05. The Mann–Whitney U test will be utilized to analyze the distribution of symptoms between the negative and IOPD patient groups, using a similar threshold to denote statistical significance. To further investigate the strength of associations identified through the chi-square test, we plan to utilize Cramer’s V statistic, which will generate a heatmap to visualize the associations between the identified symptoms and IOPD, potentially revealing even moderate levels of association.

We applied the Random Forest (RF) feature selection technique to identify the most important symptoms related to IOPD within our dataset. This ensemble learning method constructs multiple decision trees during training time and outputs the class, which is the mode of the classes from individual trees for categorical data. Feature importance is determined based on the average impurity decrease computed from all decision trees in the forest. This study will employ a cross-validation approach (i.e., RepeatedKFold) to increase the robustness of the feature importance results. The data will be divided into K-folds (or subsets) (i.e., 5), and the model is trained K times, each time using a different subset. The K-fold is repeated n times (i.e., 3), which helps in reducing the variance in the reported results.

This analysis is expected to yield a set of most common features among IOPD patients. Although the knowledge about the disease’s symptoms is very well known, we analyzed it to find out what is common within our dataset. As a subsequent step, we plan to investigate the association between these identified features and other symptoms prevalent to those features in the dataset, excluding Pompe diagnosis within the dataset, to avoid redundancy. All statistical analyses will be conducted using Python, utilizing various statistical packages to facilitate data analysis and visualization. The results will be deemed statistically significant if the p-value is less than 0.05.

Upon establishing a comprehensive methodological framework, we applied these techniques to our dataset to evaluate their efficacy. The results of these analyses and their subsequent discussion are presented in the following section.Figure 3 Comparison of CK levels between IOPD and screened patients.

Results and discussion

The study comprises 6 IOPD patients and screened 93,365 subjects. First, exploring the data presents the gender distribution of patients, revealing three males and three females among the IOPD group. In contrast, the screened subjects consisted of 48789 males and 44,404 females. Figure 2 presents a world map showing the population distribution of patients in this study, where the star marker represents IOPD patients. As can be seen from the map, the patient population shows homogeneity, as the majority of the study population appears to be from the West Asia region.

We reviewed past literature to explore symptoms of IOPD mentioned by medical field experts to explore and understand the clinical manifestations. Our findings show that IOPD has a broad spectrum of symptoms. However, the following symptoms were the most common symptoms identified and discussed in the past literature19–28: cardiomegaly, hypotonia, feeding difficulties, respiratory infections, ventilator dependence (breathing difficulties), cardiomyopathy, muscle weakness, and elevated levels of CK.

We explored the data to analyze the distribution of the symptoms mentioned above for patients in our data. Our findings are summarized in Figs. 3 and 4. Figure 3 compares the CK levels in IOPD patients versus screened patients, which revealed significant differences, underscoring the profound impact of this genetic disorder on muscular integrity. The mean CK level in IOPD patients was markedly higher (418.0 U/L) than in the screened group (245.17 U/L), indicating an elevated muscle damage or turnover baseline in the IOPD patients, which is consistent with the known pathophysiology of IOPD, where lysosomal dysfunction leads to glycogen accumulation in muscle tissues, resulting in cellular damage29.

Figure 4 presents the prevalence of symptoms identified in the reviewed literature in our dataset. It can be noted that cardiomegaly and cardiomyopathy are the most prevalent symptoms in IOPD patients, which aligns with IOPD’s characteristic of causing hypertrophic cardiomyopathy. Feeding difficulties, hypotonia, and respiratory infections are also significantly more common in IOPD patients compared to screened subjects. In this study, muscle weakness and ventilator dependence were not observed among the IOPD patients. This absence of reported cases is because none of the patients diagnosed with IOPD presented with these specific symptoms during our research period and data.Figure 4 Prevalence of symptoms in IOPD and screened patients.

In addition to the χ2 test, we further analyzed the associations between IOPD and various symptoms, presented in ICD-10 code format, using Cramer’s V statistic, a measure derived from the χ2 statistic that quantifies the strength of association between categorical variables. The results, presented as a heatmap of Cramer’s V values in Fig. 5, offer an insightful visualization of the potential associations between clinical features and Pompe disease. The values range from 0 to 1, where 0 indicates no association and 1 indicates a perfect association.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≤0.05).

Although Pompe’s disease symptoms are very well known in the existing literature, we wanted to explore the strength of the relationship between symptoms and Pompe diagnosis within our dataset by employing the Cramer’s V statistic. As shown in Fig. 5, there is a strong correlation between Pompe and congenital hypotonia [P94.2], unspecified cardiomyopathy [I42.9], and hypertrophic cardiomyopathy [I42.2]. These findings affirm the dataset’s alignment with established clinical understandings of Pompe disease. Statistically significant associations (p≤0.05) are denoted by asterisks in the figure. It’s worth noting that some relationships may not appear as strong as expected due to limitations in the dataset’s comprehensiveness, despite their known clinical significance in Pompe disease.

Interestingly, our analysis revealed that several symptoms exhibit statistically significant associations (p≤0.05, denoted by asterisks in the figure) with Pompe disease, even when their Cramer’s V values are relatively small (< 0.1). This phenomenon is not uncommon in large datasets or complex medical conditions and underscores the importance of considering both effect size (Cramer’s V) and statistical significance (p-value) in interpretation. For instance, cardiomegaly [I51.7] shows a Cramer’s V of 0.052 but is statistically significant, indicating a weak but non-random association with Pompe disease.

Building upon the insights from Cramer’s V analysis, we progressed to a more focused feature selection phase to identify potential markers strongly associated with Pompe disease. In our study, we used the importance of the RF feature to find the most important symptoms (features) associated with IOPD. This method helped us pinpoint which symptoms are most strongly linked to the disease. RF feature importance technique is good at identifying the most important features without getting confused by the less important ones. The most important features identified through this method were Cardiomyopathy, Unspecified [I42.9], Other Hypertrophic Cardiomyopathy [I42.2], Pneumonia Due to Other Specified Bacteria [J15.8], Congenital Hypotonia [P94.2], Enterovirus Infection, Unspecified [B34.1], Other Viral Infections of Unspecified Site [B34.8], Chronic Systolic (Congestive) Heart Failure [I50.22], Acute On Chronic Combined Systolic (Congestive) And Diastolic (Congestive) Heart Failure [I50.43], Dilated Cardiomyopathy [I42.0], Unspecified Lack of Expected Normal Physiological Development in Childhood [R62.50].

Figure 6 presents an analytical delineation of symptoms associated with IOPD identified by an RF feature importance analysis. The symptoms are represented by bars, with the intensity of the green color distinguishing between the commonality of symptoms and their identification by the RF analysis alone. The dark green bars denote symptoms identified by the Random Forest feature importance method and are widely reported in the literature, affirming their established presence in IOPD clinical profiles. Conditions such as “Cardiomyopathy” and “Other Hypertrophic Cardiomyopathy” exhibit the most significant prominence, underscoring their significance in the disease’s symptomatology.Figure 6 Top 20 important features identified by Random Forest feature selection.

We further applied the RF feature importance test to each unique symptom identified through RF to explore their associations with other symptoms. Figures 7,  8 and 9 presents network diagrams to illustrate the relationship between all identified symptoms in the feature importance analysis and all other symptoms available in the dataset. The dark blue nodes, as central nodes, represent the primary symptoms of IOPD as identified through feature importance analysis. The yellow nodes are secondary symptoms associated with the primary IOPD symptoms, as identified through the same analysis. Their connection to the central nodes suggests a clinical association that may be consequential or contributory to the presentation of the primary symptoms. These associations may not be causal but could indicate common comorbidities or complications arising from the primary symptoms of IOPD. The complete network diagram can be accessed here https://zahir2000.github.io/pompe.github.io/network.

The network diagram depicted in Fig. 7 represents the complex cardiac complications that can arise due to the progressive nature of this lysosomal storage disorder. IOPD often leads to cardiac hypertrophy and progressively impairs cardiac function. The central node, “Unspecified Combined Systolic and Diastolic Heart Failure”, indicates the co-occurrence of both systolic (the heart’s ability to pump blood) and diastolic (the heart’s ability to fill with blood) dysfunction, which is particularly challenging to manage in IOPD due to the glycogen accumulation in the heart muscle. Adjacent to this is “Acute on Chronic Combined Systolic and Diastolic Heart Failure”, denoting an acute decompensation superimposed on chronic heart failure. The progression to “Chronic Systolic Heart Failure” reflects a deterioration of the heart’s pumping ability, a common consequence of the disease’s impact on cardiac muscle. Moreover, “Secondary Pulmonary Arterial Hypertension” can develop as a result of chronic heart failure, leading to increased pressure in the pulmonary arteries, and is linked to both chronic systolic heart failure and the central node, suggesting it is a common sequela in IOPD patients. This condition can further exacerbate the burden on the heart and complicate the clinical management of patients. Lastly, the node “Acute on Chronic Systolic Heart Failure” captures the episodic worsening of heart function that children with IOPD may experience.Figure 7 Symptomatic networks in infantile onset Pompe disease: A Random Forest feature importance analysis identifying core symptoms and related conditions (Cluster 1).

The network diagram presented in Fig. 8 underscores the susceptibility of IOPD patients to respiratory infections due to compromised diaphragmatic and intercostal muscle function. The central node, representing ‘Other Viral Infections of Unspecified Site’, could indicate the various respiratory viruses that patients with IOPD are at risk for, considering their already weakened respiratory systems. Connections to specific conditions such as adenovirus infection, which can lead to acute upper respiratory infections and enterovirus infection, highlight the complexity and severity of managing IOPD patients who may develop these infections. The further association with conditions such as ‘Unspecified Asthma with Exacerbation’ and ‘Unspecified Bacterial Pneumonia’ demonstrates the potential for viral infections to exacerbate underlying respiratory conditions or lead to secondary bacterial infections.Figure 8 Symptomatic networks in infantile onset Pompe disease: A Random Forest feature importance analysis identifying core symptoms and related conditions (Cluster 2).

The central node in the network diagram shown in Fig. 9, “Cardiomyopathy”, is significant as IOPD often involves cardiac issues such as hypertrophic cardiomyopathy due to glycogen accumulation in the heart muscle. This can progress to dilated cardiomyopathy, as represented in the diagram, and be associated with conditions like myocarditis, which can complicate cardiomyopathy. The link to “Congenital Hypotonia” underscores the muscle weakness seen in IOPD, which can lead to motor function delays and other developmental issues. The diagram also includes respiratory conditions like pneumonia due to Streptococcus pneumoniae and other specific bacteria, for which patients with IOPD are at increased risk due to compromised respiratory muscles. Acute bronchitis and acute respiratory failure are connected, highlighting the respiratory complications that are a common cause of morbidity in IOPD. The presence of “Wheezing” further denotes respiratory distress. Furthermore, the nodes related to family indicate the hereditary nature of IOPD, and the inclusion of gastrointestinal appliance fitting suggests the feeding difficulties and nutritional support often required by these patients.

Acknowledging the rapidly progressive nature of IOPD, which often results in mortality within the first year of life due to cardiac and ventilatory failure, our study developed a new set of expert-derived rules. The expert rules are designed to augment the efficiency of physicians by refining the patient review process within EHRs. By preemptively identifying and flagging potential cases of IOPD, these rules enable healthcare providers to concentrate their review efforts on a targeted subset of patients. This alleviates the workload for clinicians by diminishing the need for extensive manual chart reviews and significantly conserves time-a critical factor considering the urgent need for rapid treatment initiation in IOPD. Early intervention is critical, as the condition is aggressively progressive, often resulting in mortality within the first year of life if left untreated. Therefore, implementing these expert rules is not merely an administrative convenience but a vital measure to expedite the diagnostic process, enhancing the likelihood of timely therapeutic intervention for IOPD patients. Algorithm 1 presents the logical flow of the expert rules. For a patient to fall under consideration based on these rules, they must be 12 months of age or younger, which aligns with the “infantile onset” classification of Pompe disease, marking a period where the disease’s severe symptoms typically emerge. Rule 1 focuses on the co-occurrence of cardiomyopathy and hypotonia, which are hallmark features of IOPD, reflecting the disease’s profound impact on cardiac and skeletal muscle function. Rule 2’s emphasis on delayed physiological development alongside cardiomyopathy captures the broad spectrum of clinical manifestations, recognizing how Pompe disease can affect overall infant growth and development. Rule 3 leverages the diagnostic value of elevated creatine kinase levels-a marker of muscle damage-combined with cardiomyopathy to identify potential cases based on biochemical evidence of muscle dysfunction. Rules 4 and 5 extend the diagnostic framework to include feeding difficulties and recurrent respiratory or chest infections, respectively, in the aforementioned conditions. These additions acknowledge the multi-systemic nature of IOPD, where feeding and respiratory challenges often compound the primary muscular and cardiac symptoms.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.

The efficacy of the expert rules is further evident when we explore the associations presented in the network diagrams based on the data used in this study in Figs. 7,  8 and 9. As shown in Fig. 9, there is a direct association between cardiomyopathy and congenital hypotonia. This combination of conditions is the first rule in the expert-derived rules. The second rule is the indirect association between an unspecified lack of expected normal physiological development in childhood and cardiomyopathy conditions. Next, pneumonia due to other specified bacteria, a respiratory infection, is directly associated with other hypertrophic cardiomyopathy, the last rule in the expert-derived rules.

The study employed a thoroughly designed dashboard to expedite the identification process of IOPD patients using a set of expert-derived rules. Purpose-built to enhance user experience, the dashboard’s layout allows for the seamless visualization of complex patient data, facilitating quick and informed decision-making. As presented in Fig. 10, the dashboard features an organized table that displays patient identifiers, associated diagnostic codes, creatine kinase levels, and the outcomes predicted by the expert rules. It also includes a confusion matrix that explains the performance of the expert rules. Data categories within the dashboard are deliberately chosen for relevance to IOPD diagnosis, including a range of symptoms and biochemical markers such as creatine kinase levels. The expert rules, previously delineated, are integrated within the dashboard, providing a structured approach to patient screening. The performance of these rules is quantified using accuracy metrics-specifically, accuracy, specificity, and sensitivity measures-which are critical in evaluating the reliability of the diagnostic criteria. Descriptive analytics enrich the dashboard, offering a count of patients meeting each symptom criterion and a confusion matrix providing a breakdown of true positives, false positives, true negatives, and false negatives. Enhanced interactivity is a hallmark of this tool; users can filter patient data by age or specific conditions, toggle the display to focus on various predictive outcomes and identify which patients satisfy any given rule. This adaptability makes the dashboard an invaluable asset in navigating the data landscape. As detailed in the methodology, the dashboard development utilized PowerBI for its robust data visualization capabilities and SQL for data manipulation, ensuring high precision and user engagement. The implications of such a tool for research are profound. The dashboard streamlines the data analysis process and provides a flexible platform for generating insights and flagging patients for review. It embodies a significant advancement in the utilization of clinical data, offering a potent means to leverage expert knowledge in the pursuit of improved patient outcomes.Figure 10 Developed dashboard with integrated expert rules.

Upon implementing the expert rules into the dashboard, our study delineated five true positives (TP) instances of IOPD, one false negative (FN), and four high-risk patients for Pompe disease out of the patients within the extracted EHRs. The expert physician manually reviewed the flagged patients. The true positive cases reaffirm the efficacy of the expert rules in accurately detecting IOPD. Conversely, the false negatives represent instances where the rules did not flag the diagnosis. The case pertained to a patient with concomitant Pompe disease and congenital heart disease, manifesting as ventricular septal defect, patent ductus arteriosus, and atrial septal defect, a clinical emphasis on CHD, overshadowing the Pompe disease diagnosis. Notably, an absence of CK measurement further contributed to the oversight. The high-risk cases for Pompe disease were evaluated and found to have the following diseases: Mitochondrial DNA depletion syndrome 12-A linked to the SLC25A4 gene, Immunodeficiency-71 characterized by a mutation in ARPC1B c.392+1G>C, Niemann-Pick disease type C resulting from a mutation in the NPC1 gene (c.2972_2973delAG) leading to a frameshift (p.Gln99Argfs*15), and Group B Streptococcus (GBS) meningitis. Next, using the dashboard, we identified a single patient with elevated CK levels, hypotonia, and physiological development. Upon reviewing the patient’s records, it was found that the patient had suffered severe hypoxic-ischemic encephalopathy, indicative of significant oxygen deprivation and consequential brain injury. Afterward, we identified patients with cardiomyopathy (true negative cases only) using the dashboard, as cardiomyopathy is the most common and obvious marker. The expert physician manually reviewed the records of the identified patients, and the review results are presented in Table 2. The review shows that none of the (true negative) patients with cardiomyopathy alone have IOPD disease, further proving the efficacy of the expert rules.

Our expert rule-based method has significant potential to enhance clinical practice through its integration into existing EHR systems. The system can automatically analyze patient data against predefined criteria for identifying IOPD by embedding these rules using programming languages such as Python or SQL. When patient data meets these criteria, the system generates alerts within the EHR, notifying the attending physician or clinical team to review the case. This automation not only streamlines the identification process but also ensures timely intervention. Furthermore, the expert rules can be incorporated into Clinical Decision Support (CDS) tools, which provide real-time feedback and suggestions to clinicians based on patient data. These tools can link to relevant clinical guidelines and research articles, enhancing the clinician’s understanding and supporting informed decision-making. Training sessions will ensure clinicians and staff can interpret and act on these alerts effectively. This training can be integrated into routine clinical education programs. The successful implementation of this method depends significantly on the quality and comprehensiveness of the hospital’s data. The expert rules are most effective when the hospital’s EHR contains detailed patient records, including the symptoms specified in the expert rules. There is no predefined method for applying these rules; implementing the logic that underpins them correctly is key. Each healthcare institution may need to tailor the rules to fit their specific data structures and clinical workflows. Implementing this method requires a collaborative effort between IT staff, clinical informaticians, and healthcare providers to ensure accurate integration and clinical relevance. Continuous monitoring, feedback collection, and regular audits will be crucial for refining the rules and improving their performance. This iterative process will help optimize the system’s effectiveness in identifying and managing IOPD cases. By leveraging this expert rule-based approach, healthcare institutions can improve the accuracy and timeliness of IOPD diagnosis and treatment, ultimately leading to better patient outcomes and more efficient use of healthcare resources.

As discussed above, our study’s findings highlight significant insights into the early detection and diagnosis of IOPD using EHR-based expert rules. The concluding section succinctly summarizes these results and their implications.Table 2 List of patients with cardiomyopathy and their actual diagnosis.

Review of patients with cardiomyopathy	
1. PTPN11 gene p.(Met508Val) heterozygous	
2. NKX2-5 c.544G>T p.(Val182Phe) heterozygous	
3. Resolved, small PFO	
4. Viral myocarditis	
5. Complex heart disease, ? CHARGE syndrome	
6. Supraventricular tachycardia possible ectopic atrial tachycardia	
7. Single visit for second opinion for cardiomyopathy, no further follow up	
8. MYH7 c.1608G>T p.(Glu536Asp) heterozygous	
9. ALPK3 c.639G>A p.(Trp213*) homozygous	
10. Viral myocarditis	
11. Resolved cardiomyopathy secondary to gestational diabetes	
12. LAMP2 gene c.184-1G>A hemizygous	
13. ASD (atrial septal defect) and small apical VSD (ventricular septal defect).	
14. Congenital CMV infection	
15. End stage renal disease secondary to atypical hemolytic uremic syndrome	
16. NRAP c.3568G>T p.(Glu1190*) homozygous variant	
17. ALPK3 c.1481_1484del p.(Leu494fs*) homozygous variant	
18. TTN gene c.42151+2T>C heterozygous variant	
19. Alstrom Syndrome, dilated cardiomyopathy, homozygous mutations in ALMS1	
20. Dilated cardiomyopathy presented at 22 month, no follow up	

Conclusion

Our study embarked on developing and implementing an expert rules-based dashboard to enhance the identification of IOPD patients within the SEHA healthcare network in the UAE. The expert rules-based dashboard identified five true positive cases of IOPD using the EHRs, underscoring the effectiveness of the expert rules in capturing clinically verified instances of the disease. Despite the success in identifying true positives, the expert rules also yielded one false negative, illustrating the system’s limitations in detecting instances where a patient has two genetic diseases. Four potential cases were identified, representing differential diagnoses that phenotypically mimic IOPD, thus presenting a challenge to the specificity of the rule-based system. The manual review of flagged cases by expert physicians confirmed the reliability of the expert rules. Nevertheless, it also revealed the critical role of comprehensive follow-up and the importance of incorporating a range of diagnostic measures to ensure accuracy. The dashboard, designed with user-centric principles and constructed via PowerBI, proved instrumental in visualizing complex patient data and the expert rules’ performance. This tool enabled efficient data interaction, allowing healthcare providers to filter and review patient information promptly. Statistical analyses reinforced the utility of the dashboard, with cardiomyopathy and hypotonia emerging as significant indicators of IOPD, as evidenced by elevated creatine kinase levels and the presence of specific cardiomyopathies. Incorporating machine learning techniques such as Random Forest feature importance analysis provided further depth to our understanding, identifying a spectrum of symptoms highly indicative of IOPD. In conclusion, our research demonstrates the transformative potential of a rule-based dashboard in the realm of IOPD diagnosis. This study contributes to the prompt recognition and treatment of rare genetic disorders within a regional healthcare system by streamlining the identification process and providing a model for future diagnostic frameworks. We have to mention that our rule-based algorithm is not superior to NBS, as it requires the patient to have specific symptoms. However, it is an alternative method of detection of IOPD in regions where NBS for IOPD is not offered. In the future, a machine learning approach would be interesting to experiment with as it may offer a more nuanced ability to discern patterns within large datasets, potentially improving the accuracy and efficiency of IOPD diagnosis beyond the capabilities of traditional rule-based systems. However, the data imbalance within the dataset will be a considerable challenge when experimenting with machine learning.

A common challenge in researching rare diseases is the notably limited sample size of positive cases, which complicates the conduct of statistical analyses. On the other hand, manipulating such a large dataset (due to the large number of screened patients) was computationally exhaustive for a personal computer, as it was evident from the duration between command and code execution in developing and using the PowerBI dashboard and Python programming.

Acknowledgements

This research is supported by ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD) award grant number VRI-20-10. We also express our profound gratitude to Sanofi for their financial support of our research (grant fund code 21M142).

Author contributions

J.R., Z.R., and F.A. conceived the experiment(s). J.R. and Z.R. conducted the experiment(s). J.R. and Z.R. analysed the results. All authors reviewed the manuscript.

Data availability

The data used in this study are not publicly available due to privacy and legal restrictions, but they are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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