Veličković VM, Spelman T, Clark M, Probst S, Armstrong DG, Steyerberg E, et al.
Advances in wound care. Date of publication 2023 Jul 1;volume 12(7):387-398.
1. Adv Wound Care (New Rochelle). 2023 Jul;12(7):387-398. doi:
10.1089/wound.2022.0017. Epub 2022 Nov 1.
Individualized Risk Prediction for Improved Chronic Wound Management.
Veličković VM(1)(2), Spelman T(3)(4)(5), Clark M(6)(7), Probst S(8)(9)(10),
Armstrong DG(11), Steyerberg E(12).
Author information:
(1)HARTMANN GROUP, Heidenheim, Germany.
(2)Institute of Public Health, Medical Decision Making and HTA, UMIT, Hall in
Tirol, Austria.
(3)Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
(4)Burnet Institute, Melbourne, Australia.
(5)Department of Health Services Research, Peter MacCallum Cancer Centre,
Melbourne, Australia.
(6)Welsh Wound Innovation Centre, Pontyclun, United Kingdom.
(7)School of Health, Education and Life Sciences, Birmingham City University,
Birmingham, United Kingdom.
(8)Geneva School of Health Sciences, HES-SO University of Applied Sciences and
Arts, Geneva, Western Switzerland.
(9)Faculty of Medicine Nursing and Health Sciences, Monash University,
Melbourne, Australia.
(10)Care Directorate, University Hospital Geneva, Geneva, Switzerland.
(11)Southwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery,
Keck School of Medicine, University of Southern California (USC), Los Angeles,
California, USA.
(12)Leiden University Medical Center, Leiden, the Netherlands.
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.
DOI: 10.1089/wound.2022.0017
PMCID: PMC10125399
PMID: 36070447 [Indexed for MEDLINE]
Conflict of interest statement: V.V. is an employee of HARTMANN GROUP. T.S.
received compensation from serving on advisory boards for Biogen; speaker fees
from Biogen and Novartis. M.C. received speaker fees from Arjo. S.P. declares no
competing interests for this publication D.G.A. This study is partially
supported by National Institutes of Health, National Institute of Diabetes and
Digestive and Kidney Diseases Award Number 1R01124789-01A1 and partially
supported by National Science Foundation (NSF) Center to Stream Health care in
Place (#C2SHiP) CNS Award Number 2052578 E.S. declares no competing interests
The content of this article was expressly written by the authors listed.
