Individualized Risk Prediction for Improved Chronic Wound Management

Advances in Wound Care, vol. 12, pp. 387–398

Abstract

Significance: Chronic wounds are associated with significant morbidity, marked loss of quality of life, and considerable economic burden. Evidence-based risk prediction to guide improved wound prevention and treatment is limited by the complexity in their etiology, clinical underreporting, and a lack of studies using large high-quality datasets. Recent Advancements: The objective of this review is to summarize key components and challenges in the development of personalized risk prediction tools for both prevention and management of chronic wounds, while highlighting several innovations in the development of better risk stratification. Critical Issues: Regression-based risk prediction approaches remain important for assessment of prognosis and risk stratification in chronic wound management. Advances in statistical computing have boosted the development of several promising machine learning (ML) and other semiautomated classification tools. These methods may be better placed to handle large number of wound healing risk factors from large datasets, potentially resulting in better risk prediction when combined with conventional methods and clinical experience and expertise. Future Directions: Where the number of predictors is large and heterogenous, the correlations between various risk factors complex, and very large data sets are available, ML may prove a powerful adjuvant for risk stratifying patients predisposed to chronic wounds. Conventional regression-based approaches remain important, particularly where the number of predictors is relatively small. Translating estimated risk derived from ML algorithms into practical prediction tools for use in clinical practice remains challenging.

Authors 6

1. Vladica M. Veličković corresponding

   UMIT TIROL - Private University for Health Sciences and Health Technology · Hartmann Group (Germany)

   Affiliation as printed

   HARTMANN GROUP, Heidenheim, Germany

   Institute of Public Health, Medical Decision Making and HTA, UMIT, Hall in Tirol, Austria

2. Tim D. Spelman

   Burnet Institute · Peter MacCallum Cancer Centre · Karolinska Institutet

   Affiliation as printed

   Burnet Institute, Melbourne, Australia

   Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden

   Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, Australia

3. Michael Clark

   Birmingham City University

   Affiliation as printed

   School of Health, Education and Life Sciences, Birmingham City University, Birmingham, United Kingdom

   Welsh Wound Innovation Centre, Pontyclun, United Kingdom

4. Sebastian Probst

   HES-SO University of Applied Sciences and Arts Western Switzerland · HES-SO Genève · University Hospital of Geneva · Monash University

   Affiliation as printed

   Care Directorate, University Hospital Geneva, Geneva, Switzerland

   Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, Australia

   Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts, Geneva, Western Switzerland

5. David G. Armstrong

   University of Southern California

   Affiliation as printed

   Southwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery, Keck School of Medicine, University of Southern California (USC), Los Angeles, California, USA

6. Ewout Willem Steyerberg Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Leiden, the Netherlands