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Multi-State Modeling of Pressure Injury Staging Transition Trajectories to Inform Next-Generation Clinical Decision

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Stage 2 pressure injuries (PrIs) act as a critical gateway to severe stages across common locations. Current risk assessment tools lack the time-sensitive data needed for effective PrI prevention.

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Area of Science:

  • Medical Informatics
  • Clinical Nursing
  • Health Services Research

Background:

  • Pressure injuries (PrIs) represent a significant healthcare challenge, impacting patient outcomes and increasing healthcare costs.
  • Existing risk assessment tools often lack the granularity to capture the dynamic progression of PrIs.
  • Understanding location-specific and time-sensitive trajectories is crucial for effective PrI management.

Purpose of the Study:

  • To evaluate the location-specific and time-sensitive trajectories of pressure injury (PrI) stages.
  • To analyze PrI progression using real-world electronic health record (EHR) data.
  • To identify critical transition points and influencing factors in PrI development.

Main Methods:

  • Utilized a large EHR dataset (29,475 patients, 2015-2023) to develop four PrI sub-cohorts (coccyx, buttocks, sacrum, heel).
  • Employed multi-state trajectory analysis to estimate transition intensities between PrI stages (stage 1, stage 2, severe).
  • Incorporated clinical expert knowledge and National Pressure Injury Advisory Panel (NPIAP) guidelines for state definitions and transition paths.

Main Results:

  • Stage 2 was identified as a "gateway state" for PrI progression across all analyzed locations.
  • The Braden Scale showed limited predictive value for transitions from stage 2 to severe stages.
  • Observed significant race-dependent variations in PrI progression trajectories across different injury locations.

Conclusions:

  • Current PrI risk assessment tools are suboptimal due to a lack of time-sensitive data, failing to capture dynamic progression.
  • There is a critical need for clinical decision support systems that incorporate time-sensitive data for personalized PrI prevention.
  • Future research should focus on developing dynamic models to predict and prevent PrI advancement effectively.