Individualized Risk Prediction for Improved Chronic Wound Management
Details
Publication Year 2023-07,Volume 12,Issue #7,Page 387-398
Journal Title
Advances in Wound Care
Publication Type
Review
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.
Publisher
Mary Ann Liebert
Keywords
Humans; *Quality of Life; *Wound Healing; Prognosis; Risk Factors; Machine Learning; chronic wounds; personalized therapy; risk prediction; risk stratification; wound management
Department(s)
Health Services Research
PubMed ID
36070447
Open Access at Publisher's Site
https://doi.org/10.1089/wound.2022.0017
Terms of Use/Rights Notice
Refer to copyright notice on published article.


Creation Date: 2023-10-24 04:50:20
Last Modified: 2023-10-24 04:50:48

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