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Area of Science:

  • Infectious Diseases
  • Public Health
  • Health Informatics

Background:

  • Healthcare-associated infections (HAI) represent a major challenge in patient safety and healthcare costs.
  • Current infection prevention and control (IPC) measures lack personalized risk assessment for individual patients.
  • Hospital-onset bloodstream infections (HOBSI) are a critical subset of HAI requiring targeted prevention.

Purpose of the Study:

  • To identify candidate risk parameters for hospital-onset bloodstream infections (HOBSI) from routine hospital data.
  • To lay the groundwork for an application supporting decision-making in personalized IPC.
  • To re-analyze existing patient data for novel risk factor identification.

Main Methods:

  • Re-analysis of a dataset comprising 4290 patients from a large-scale study.
  • Application of logistic regression modeling to identify significant risk factors.
  • Utilization of a random forest approach for enhanced predictive parameter identification.

Main Results:

  • Initial findings from the re-analysis are presented.
  • Candidate risk parameters for HOBSI have been identified using statistical and machine learning models.
  • The identified parameters are derived from routinely available hospital data.

Conclusions:

  • Routine hospital data contains valuable information for predicting HOBSI risk.
  • Personalized risk assessment for IPC is feasible and can be supported by data-driven applications.
  • Further development of decision support tools for personalized HOBSI prevention is warranted.