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How next-generation sequencing and multiscale data analysis will transform infectious disease management.

Theodore R Pak1, Andrew Kasarskis1

  • 1Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.

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Summary

Integrating next-generation sequencing (NGS) with electronic medical records (EMRs) and other patient data can create predictive models to combat healthcare-associated infections and antimicrobial resistance. This approach is key for future infectious disease management.

Keywords:
electronic medical recordshealthcare-associated infectionshospital-acquired infectionsmultiscale analysiswhole genome sequencing

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Area of Science:

  • Clinical microbiology
  • Infectious disease epidemiology
  • Health informatics

Background:

  • Routine next-generation sequencing (NGS) enhances clinical microbiology labs but integration with other data is underexplored.
  • Healthcare-associated infections and multidrug-resistant organisms pose significant threats.
  • Existing reviews focus on short-term NGS capabilities, not multiscale predictive modeling.

Purpose of the Study:

  • To explore integrating diverse datasets for advanced infection management.
  • To propose disruptive applications of omics and electronic medical record (EMR) data in clinical workflows.
  • To outline a framework for multiscale predictive modeling in infectious diseases.

Main Methods:

  • Review of current literature on NGS in clinical microbiology.
  • Identification of relevant "omics" and patient data sources.
  • Conceptualization of multiscale analysis integrating NGS and EMR data.

Main Results:

  • Proposed integration of NGS, EMR, immune profiling, and other 'omics' data.
  • Identified 3 potentially disruptive applications for these integrated data in clinical settings.
  • Highlighted the potential for multiscale analysis to address healthcare-associated infections and antimicrobial resistance.

Conclusions:

  • Multiscale analysis of integrated NGS and EMR data can combat infectious threats.
  • Such data integration is crucial for developing predictive models in infectious disease.
  • This approach should underpin future learning health systems for infectious disease management.