Studying Multimorbidity through Advanced Tools

Multimorbidity refers to when a person has more than one health condition at the same time. This increasing trend is a major challenge for healthcare systems worldwide. Understanding how these various diseases interact can help us better treat and manage patients with multiple conditions. Different diseases may influence each other's symptoms, severity, and responses to treatments. By studying these interactions, we can learn about shared factors that contribute to multiple diseases, which may lead to better prevention and treatment options.

#The Role of Electronic Health Records

Electronic health records (EHRs) are digital versions of patients' paper charts. They contain detailed health information, making it easier for doctors and researchers to access and analyze patient data. EHR systems have become valuable tools in studying multimorbidity. They allow researchers to look at large amounts of real-world data, helping identify patterns in how diseases occur together in different populations.

#Analyzing Multimorbidity Patterns

One method used to study multimorbidity is network analysis. This approach helps to visualize and understand the connections between various diseases. In recent work, researchers found that combining data from different EHR systems can help confirm findings about multimorbidity patterns. By comparing results across different hospitals or institutions, researchers can gain a more complete picture of how diseases interact.

Despite the advantages of using EHRs, there are still challenges in measuring and analyzing multimorbidity patterns. Current standards for how to define and study these patterns need further development. The lack of clear guidelines makes it hard to compare findings from different studies.

#Introduction of PheMIME

To address these challenges, a new online tool called the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME) has been developed. PheMIME is designed to help researchers explore and compare multimorbidity patterns using information from several large EHR databases. This tool allows users to access and analyze data from three major sources: Vanderbilt University Medical Center, Massachusetts General Brigham Hospital, and UK Biobank.

With PheMIME, researchers can select a specific health condition they want to study, such as diabetes or heart disease. They can then compare how this condition interacts with other diseases across different institutions. The tool not only shows these relationships but also ensures that the findings are statistically significant.

#Features of PheMIME

PheMIME is interactive and user-friendly, with several key features:

1. Disease Selection: Users can search for specific disease codes related to different health conditions. This module allows for easy exploration of diseases.

2. Multimorbidity Consistency Inspection: This feature helps users assess how consistent the findings are across multiple institutions. Researchers can see which disease combinations are most significant.

3. Multimorbidity Network Visualization: Users can view visual representations of the relationships between diseases. This feature allows for exploring subgroups and clusters of diseases based on their interactions.

4. Reproducible Multimorbidities Exploration: Here, users can focus on specific disease combinations that show consistent patterns across institutions. This way, researchers can identify which disease pairs are more common.

5. Multimorbidity Similarities Exploration: Similar to the previous module, this feature helps visualize connections between diseases based on similarity measures. Users can compare findings between different institutions.

#Case Study: Schizophrenia

To demonstrate the usefulness of PheMIME, a case study focused on schizophrenia was conducted. Researchers explored how schizophrenia interacts with other diseases. When researchers selected the schizophrenia disease code, they were provided with visual tools such as a Manhattan plot. This plot shows the strength of the relationship between schizophrenia and other diseases.

Users can also generate scatter plots to compare results from different healthcare systems. These visual tools help highlight which disease pairs show strong connections and consistent patterns across institutions.

Additionally, the tool generates a data table displaying details about other diseases related to schizophrenia, including their interaction strength. Users can interact with this table, selecting and emphasizing specific diseases of interest.

#Dynamic Network Analysis

One of the most exciting features of PheMIME is its dynamic network analysis capabilities. Using a method called associationSubgraphs, users can examine subgroups of diseases related to schizophrenia. The network analysis visually presents which diseases are closely connected and which ones belong to larger categories, like mental health or infectious diseases.

For example, researchers found that schizophrenia has notable connections to viral hepatitis B and C. This finding aligns with prior research. However, when diving deeper into the data, it became clear that the strength of these connections varies between different populations. The patterns were stronger in patients from Vanderbilt University Medical Center and Massachusetts General Brigham compared to the broader population represented by UK Biobank.

#Implication of Findings

The differences in disease interactions observed among various patient groups hint at the complexity of multimorbidity. Factors like demographics and lifestyle can influence how diseases relate to one another. Understanding these differences is crucial for tailoring effective treatments and interventions.

Even though the link between schizophrenia and viral hepatitis is not fully understood, the patterns observed can lead to further research. These insights can help scientists and healthcare providers develop new strategies for prevention and treatment.

#Conclusion

PheMIME is a pioneering tool that facilitates the study of multimorbidity across different institutions. By providing access to a large multimorbidity knowledge base and interactive visual tools, it allows researchers to detect meaningful disease relationships and compare findings effectively. It represents a significant step forward in our understanding of how diseases interact and how this knowledge can inform better patient care.

The development of PheMIME highlights the potential of EHRs and advanced analytics in modern healthcare. As researchers continue to study and analyze multimorbidity patterns, tools like PheMIME will play a critical role in shaping future investigations, promoting better health outcomes for patients with multiple conditions.