Personalized prediction of chronic wound healing: An exponential mixed effects model using stereophotogrammetric measurement

  • Xu Y
  • Sun J
  • Carter R
 et al. 
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Abstract

Study aim: Stereophotogrammetric digital imaging enables rapid and accurate detailed 3D wound monitoring. This rich data source was used to develop a statistically validated model to provide personalized predictive healing information for chronic wounds. Materials: 147 valid wound images were obtained from a sample of 13 category III/IV pressure ulcers from 10 individuals with spinal cord injury. Methods: Statistical comparison of several models indicated the best fit for the clinical data was a personalized mixed-effects exponential model (pMEE), with initial wound size and time as predictors and observed wound size as the response variable. Random effects capture personalized differences. Results: Other models are only valid when wound size constantly decreases. This is often not achieved for clinical wounds. Our model accommodates this reality. Two criteria to determine effective healing time outcomes are proposed: r-fold wound size reduction time, tr-fold, is defined as the time when wound size reduces to 1/r of initial size. tδis defined as the time when the rate of the wound healing/size change reduces to a predetermined threshold δ < 0. Healing rate differs from patient to patient. Model development and validation indicates that accurate monitoring of wound geometry can adaptively predict healing progression and that larger wounds heal more rapidly. Accuracy of the prediction curve in the current model improves with each additional evaluation. Conclusion: Routine assessment of wounds using detailed stereophotogrammetric imaging can provide personalized predictions of wound healing time. Application of a valid model will help the clinical team to determine wound management care pathways.

Author-supplied keywords

  • Chronic wounds
  • Personalized medicine
  • Stereophotogrammetry

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Authors

  • Kath BogieLouis Stokes VA Medical Center

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  • Yifan Xu

  • Jiayang Sun

  • Rebecca R. Carter

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