Chronic Wound Healing Time Predicted Through Microbiome and Clinical Data

A 2025 study published in Wound Repair and Regeneration in 2025 represents a significant step towards precision medicine in wound care. Researchers from Texas Tech University developed and validated a structural equation model (SEM) to predict healing time in chronic wounds, leveraging patient-specific clinical parameters and wound microbiome profiles. This approach offers a novel framework for integrating microbial ecology into predicting wound healing time and outcomes.

Chronic wounds are known to be a significant burden on their own, but even more so among elderly and diabetic populations. Existing approaches often assess host factors and wound characteristics independently. This study builds and expands on these factors, aiming to quantify their influence in predicting variations in wound healing and healing time.

A key innovation in this study was the application of both SEM and microbiome “parceling,” a method that grouped co-occurring bacterial species into latent constructs predictive of clinical outcomes. Researchers optimized the model using a multi-objective approach maximized for inter-parcel and parcel-healing time correlations.

Predictive Performance and Clinical Relevance
In a retrospective analysis at a Southwest Regional Wound Care Center, a cohort identified 565 eligible patient records requiring wound microbiome compositional profiling; examination and observational measurement of edema, slough, and accumulation of fluid at the initial visit; and follow-up visits until healing was complete as determined by the physician.

The model identified two latent constructs in relation to either a slower or faster healing time. Only the construct for faster healing time, constructed from parcels with 66 species with a Pearson correlation of 0.2 or higher, could be validated (p=0.004). A good to very good overall fit (CFI = 0.950, TLI = 0.937 and RMSEA = 0.034 [0.029–0.038]) of the model was identified with microbiome composition associated with the greatest proportion of variance of nearly 40 percent (β=-0.6323), followed by venous leg ulcers (VLCUs), slough, smoking status, exudate, granulation, and volume. Notably, Staphylococcus aureus, Corynebacterium striatum, and Proteus mirabilis were identified as the top three species influencing variability due to species interactions and not alone.

To validate the model, the researchers compared its performance to a model using principal component analysis (PCA) eigenvectors in place of the microbiome parcels using a broken stick regression approach. An independent cohort (n=79) of unique wounds found that the model was able to predict healing time with an R²=0.6 (p<0.001) better than the initial cohort.

Towards a Predictive Clinical Tool
Collectively, this approach outperformed both principal component analysis (PCA) and single-species predictors, supporting the application of SEM and microbiome parceling and development of a clinical decision-support tool. Such a tool could be integrated with electronic health records and better estimate healing trajectories with greater precision, enhancing triage decisions and potentially guiding empiric or sequence-informed antimicrobial therapy.

While promising, the current model is limited to baseline data collected at a single center that practices biofilm-focused wound management. Future models incorporating longitudinal data, treatment exposure, and host genomic factors could further improve accuracy and generalizability.

This study marks a novel approach to predictive modeling in chronic wound care, demonstrating how advanced statistical modeling and microbiome profiling can converge to forecast patient-specific healing trajectories.

Reference
Ancira J, Gabrilska R, Tipton C, et al. A structural equation model predicts chronic wound healing time using patient characteristics and wound microbiome composition. Wound Repair Regen. 2025;33(1):e70004. doi:10.1111/wrr.70004