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Comorbidities significantly impact the socioeconomic costs of neurodegenerative diseases (NDs). Network analysis of electronic health records identified key co-occurring conditions, aiding in better patient care and resource allocation.

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Area of Science:

  • Medical Informatics
  • Network Science
  • Public Health

Background:

  • Neurodegenerative diseases (NDs) impose substantial socioeconomic burdens.
  • Comorbidities significantly exacerbate these costs and impact patient outcomes.
  • Understanding disease associations is crucial for effective healthcare management.

Purpose of the Study:

  • To analyze prevalent comorbidities associated with neurodegenerative diseases (NDs) using network analysis.
  • To identify key diseases that frequently co-occur with NDs.
  • To provide a framework for improved resource allocation and patient care.

Main Methods:

  • Construction of a multimorbidity network (MN) from a large longitudinal electronic health record (EHR) dataset (93,647,498 diagnoses, 824,847 patients).
  • Application of Phi-correlation and Cosine Index (CI) to measure disease associations.
  • Utilized network centrality measures to rank prevalent comorbidities.
  • Generated class-level networks for disease prevalence and strength.

Main Results:

  • The general MN comprised 928 diseases and 337,253 associations.
  • Networks at 99% confidence revealed 575 relationships (73 diseases via Phi-correlation, 102 via CI).
  • Thirteen diseases were identified as the most notable multimorbidities based on centrality measures.
  • Specific analysis of NDs identified key associated conditions and relationships.

Conclusions:

  • Network analysis provides a robust framework for understanding complex disease comorbidities in neurodegenerative diseases (NDs).
  • Identification of key multimorbidities can guide clinical practice, preventive strategies, and resource allocation.
  • This approach can lead to improved quality of life for patients and caregivers.