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Enregistrement W2573177522 · doi:10.1097/01.asw.0000511435.99585.0d

Skin Tears: Finally Recognized

2017· article· en· W2573177522 sur OpenAlexaff
Kimberly LeBlanc, Sharon Baranoski

Notice bibliographique

RevueAdvances in Skin & Wound Care · 2017
Typearticle
Langueen
DomaineHealth Professions
ThématiquePressure Ulcer Prevention and Management
Établissements canadiensCARE Canada
Organismes subventionnairesnon disponible
Mots-clésMedicineTearsDermisSkin careDermatologyPopulationHealth careSurgeryPathology

Résumé

récupéré en direct d'OpenAlex

Skin tears are “wounds caused by shear, friction, and/or blunt force resulting in separation of skin layers. A skin tear can be partial thickness (separation of the epidermis from the dermis) or full thickness (separation of both the epidermis and dermis from underlying structures).”1 They are reported across all healthcare settings and are predominantly found in older adults and the critically and chronically ill populations. The exploration of skin tears as unique wounds remains in its infancy, and researchers are only now beginning to understand that skin tears are complex and multifactorial.1 To facilitate communication and appropriate documentation, wounds should be assessed and classified using a reliable and valid classification system.2 In 2013, the International Skin Tear Advisory Panel (ISTAP) developed and validated the ISTAP Skin Tear Classification System. Using this system, skin tears are classified as type 1 (no skin/flap loss), type 2 (partial skin/flap loss), and type 3 (complete flap loss).2 Since 2013, there has been global uptake and multilanguage translation of the classification system, affording healthcare clinicians and researchers a universal language for describing skin tears. In 1991, it was estimated that 1.5 million skin tears occur each year in institutionalized adults in the United States.3 Studies in the long-term-care population have reported skin tear prevalence to be between 3.9% and 54%.4–11 Strazzieri-Pulido et al12 conducted a systematic review and reported skin tear prevalence to be between 3.3% and 22% in the acute care setting and between 5.5% and 19.5% in the home care setting. Prevalence studies of skin tears are limited, and many were conducted more than 10 years ago. The accuracy of those studies today, especially given the rising complexity of individuals in the long-term-care setting, is questionable. Determining the extent of skin tear prevalence across the healthcare continuum is relevant because of the potentially high economic and quality-of-life costs if these acute wounds become complex and chronic.13–15 Knowledge of current skin tear prevalence will aid in the allocation of resources, provide benchmarking, and support prevention programs. Themes related to skin tear risk are starting to emerge, particularly in association with the aging population. Skin changes associated with aging, dependence on others for care, presence of edema, higher concurrent risk of pressure injury development, cognitive impairment, and aggressive behavior are all viewed as factors that may contribute to skin tear development in the aging population.4,14,16–20 These risk factors, both modifiable and nonmodifiable, need to be explored to develop a comprehensive and valid risk assessment tool. Recent studies have focused on nonmodifiable skin characteristics that contribute to skin tears, such as the presence of ecchymosis, senile purpura, hematoma, photo aging, and evidence of a previously healed skin tear.4,16–18 Newall et al18 developed a risk assessment tool based on 4 skin characteristics (ecchymosis, senile purpura, hematoma, and evidence of a previously healed skin tear), presence of edema, and the inability to self-reposition. The tool was found to have high sensitivity but low specificity, and the authors reported that they were currently testing what they hoped would be a more parsimonious model. Modifiable skin tear risk factors are believed to include, but are not limited to, xerosis, falls, handling during care, use of adhesives, nutritional intake, polypharmacy, and behavioral issues (modifiable through behavior modification programs and caregiver approach to care). Carville et al15 reported that xerosis is highly prevalent in the aging population, and when xerosis is present concurrently with natural age-related skin changes, it can contribute to a heightened skin tear risk. They demonstrated a reduction in skin tear incidence with the application of moisturizer twice daily. Attention is being given to heightened skin tear risk and other health concerns experienced by the older adult population in hopes of establishing bundled approaches to care. For example, skin tears and pressure injuries are presumed to share many of the same risk factors, particularly in the long-term-care setting.21–23 Pressure injuries cause great financial burden on the healthcare system, and their prevalence is used as a benchmark for quality of care.24 LeBlanc et al20 argue that because of the overlap in skin tear and pressure injury risk factors and similarities in prevalence emphasis on a bundled approach to prevention should be undertaken. In the January issue of Advances in Skin & Wound Care, the continuing education article by Catherine Cheung, MD, FRCP, was a timely offering because it reaffirmed the belief that falls in the older adult population are linked to skin tears. Falls are reported by clinicians to be one of the contributing causes of skin tears in the aging population.5,17 Despite this belief, skin tear and fall prevention programs are rarely bundled. The ISTAP maintains that it is essential for healthcare professionals to incorporate fall prevention as part of skin tear prevention programs involving the aging population, hypothesizing that many skin tears can be prevented by preventing falls.1 Experts agree that the best management for skin tears is prevention; however, knowledge gaps continue to exist related to the risk factors for skin tears across various populations.4,14,16–20 Many may believe that risk factors can vary widely among different populations and that no single risk assessment tool will capture all individuals at risk. Much of the current research is being conducted on the aging population, as experts agree that this is the group at highest risk. Research to determine risk factors is needed in the neonatal and critically and chronically ill populations. In addition to these parsimonious models for risk assessment, further research is needed to determine the prevalence and incidence across the continuum, dressing selection, associated pain, associations between skin tears and comorbidities, and the identification of which skin tears are more likely to become complex and chronic.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil0,156

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,024
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0030,004
Communication savante0,0090,015
Science ouverte0,0020,007
Intégrité de la recherche0,0060,010
Charge utile insuffisante (le modèle a refusé de juger)0,0470,016

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.

Tête enseignante Opus0,031
Tête enseignante GPT0,441
Écart entre enseignants0,410 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations26
Publié2017
Routes d'admission1
Résumé présentoui

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