WOUND MANAGEMENT, HEALING, AND EARLY PROSTHETIC REHABILITATION: PART 3 - A SCOPING REVIEW OF CHEMICAL BIOMARKERS
Notice bibliographique
Résumé
BACKGROUND: Poor post-amputation healing delays prosthetic fitting, adversely affecting mortality, quality of life, and cardiovascular health. Current residual limb assessments are subjective and lack standardized guidelines, emphasizing the need for objective biomarkers to improve healing and prosthesis readiness assessments. OBJECTIVE(S): This review aimed to identify predictive, diagnostic, and indicative chemical biomarkers of healing of the tissues and structures found in the residual limbs of adults with amputation. METHODOLOGY: This scoping review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Searches using the terms “biomarkers,” “wound healing,” and “amputation” were performed across Web of Science, Ovid Medline, Ovid Embase, Scopus, Cochrane, PubMed, and CINAHL databases. Inclusion criteria were: 1) References to chemical biomarkers and healing; 2) Residuum tissue healing; 3) Repeatable methodology with ethical approval. Included articles were evaluated for quality of evidence (QualSyst tool) and level of evidence (JBI classification). Sources were categorized by study (e.g., randomized controlled trial or bench research), wound (diabetic, amputation, other), and model (human, murine, other) type. Chemical biomarkers repeated across study categories, and quantification methods were reported on. FINDINGS: From 3,306 titles and abstracts screened, 646 underwent full-text review, and 203 met the criteria for data extraction, with 76% classified as strong quality. 38 chemical biomarkers were identified across 4 to 50 sources, with interleukins (predictive, indicative, and diagnostic) and HbA1c (predictive) most prevalent, appearing in 50 and 48 sources, respectively. Other biomarkers included predictive blood markers (e.g., cholesterol, white blood cell counts), indicative growth factors, bacteria presence (predictive), proteins (predictive, indicative, and diagnostic, e.g., matrix metalloproteinases), and cellular markers (indicative and diagnostic, e.g., Ki-67, alpha-smooth muscle actin [α-SMA]). CONCLUSION: Predictive biomarkers identify comorbidities that may hinder healing, aiding in pre-amputation risk assessment for poor recovery. Indicative biomarkers monitor key biological healing processes, such as angiogenesis (the formation of new blood vessels), wound contraction, and inflammation. Diagnostic biomarkers provide direct insights into tissue composition and cellular-level healing. Integrating these biomarkers into post-amputation assessments enables continuous monitoring of the healing process while accounting for comorbidities, enhancing the objectivity of post-surgical healing management and ensuring more effective, personalized rehabilitation strategies. Layman's Abstract Poor healing after amputation can delay prosthetic fitting, negatively impacting health, and quality of life, and increasing the risk of heart problems and death. Currently, the assessment of residual limb health is subjective, with no standardized guidelines, creating a need for more reliable measures. This review explored chemical biomarkers (biological markers like those found in blood or tissue) that can indicate, predict, or diagnose tissue healing in adults with amputation. A scoping review was conducted using multiple databases, following established guidelines. Studies were included if they connected chemical biomarkers to healing, focused on residual limb tissue, and used ethical, repeatable methods. The studies were assessed for quality and classified based on research type, wound type (e.g., amputation or diabetic), and model (human or animal). Chemical biomarkers repeated across study categories, and methods used to measure them were reported on. From 3,306 titles and abstracts screened, 646 underwent full-text review, and 203 met the criteria for data extraction, with 76% classified as strong quality. 38 different biomarkers were identified, with two types, interleukins (involved in inflammation) and a blood sugar control marker (predicting healing), being the most common. Other biomarkers included blood tests (cholesterol, white blood cell counts) and bacteria levels that predict healing, growth factors that indicate healing progress, and markers that diagnose tissue changes at a cellular level. Biomarkers that predict healing can identify issues like infections or poor nutrition that might slow healing, useful for assessing non-healing risks before amputation. Markers that indicate healing show how the healing process is progressing by tracking changes like decreases in inflammation or increases in tissue growth. Diagnostic biomarkers provide detailed information about the healing tissue at a cellular level. Using a range of these biomarkers helps track every stage of healing and considers factors like other health conditions, leading to a more accurate way to manage recovery after amputation. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43717/33685 How To Cite: Williams-Reid H, Johannesson A, Buis A. Wound management, healing, and early prosthetic rehabilitation: Part 3 - A scoping review of chemical biomarkers. Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 1, No.1. Https://doi.org/10.33137/cpoj.v8i1.43717 Corresponding Author: Professor Arjan Buis, PhDDepartment of Biomedical Engineering, Faculty of Engineering, University of Strathclyde, Glasgow, Scotland.E-Mail: arjan.buis@strath.ac.ukORCID ID: https://orcid.org/0000-0003-3947-293X
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».