A structural equation model predicts chronic wound healing time using patient characteristics and wound microbiome composition.

Abstract

Wound aetiology, host characteristics and the wound microbiome contribute to chronic wound development. Yet, there is little accounting for the relative importance of these factors to predict wound healing. Here, a structural equation model was developed to provide such an explanatory and predictive framework. Chronic wounds from 565 patients treated at a clinic practicing biofilm-based wound care were included. Patient information included DNA sequencing-based wound microbiome clinical reports corresponding to the initial clinical visit. Wound microbiome data was integrated into the SEM as a latent variable using a pre-modelling parcel optimization routine presented herein for the first time (available as R library parcelR). A microbiome latent construct associated with improved healing was validated, and the final SEM included this latent construct plus three species associated with diminished healing (Anaerococcus vaginalis, Finegoldia magna and Pseudomonas aeruginosa), as well as smoking, wound volume, slough, exudate, edema, percent granulation and wound etiology. This model explained 46% of variations in healing time, with the microbiome contributing the largest proportion of variance explained. Model validity was confirmed with an independent cohort (n = 79) through which ~60% of the variation in healing time was predicted. This model can serve as a foundation for the development of a predictive tool that may have clinical utility.