Advances in Skin & Wound Care最新文献

Objective: The aim of this study was to retrieve, evaluate, and summarize the best available evidence on skin-mucosal care for patients with Stevens-Johnson syndrome (SJS) / toxic epidermal necrolysis (TEN).

Data sources: The data were gathered from national and international databases and reputable websites. A top-down approach was applied following the 6S Evidence Search Model, with a search time frame from the time of database construction to December 25, 2023. Multiple sources, including clinical decisions, guidelines, and expert consensus, were reviewed.

Study selection: Studies that addressed skin-mucosal care in patients with SJS/TEN were eligible for inclusion. The literature was rigorously screened to ensure its relevance and methodological quality. A total of 11 documents were included in this review, which consisted of 1 clinical decision, 5 clinical practice guidelines, and 5 expert consensus documents.

Data extraction: Two researchers independently assessed the quality of the included studies, extracted evidence, and summarized the findings. A total of 34 pieces of evidence spanning 6 dimensions of skin-mucosal care were identified and categorized.

Data synthesis: The extracted evidence was synthesized and summarized to provide actionable insights for clinical practice. The evidence focused on wound care, infection prevention, pain management, hydration, and specific recommendations for skin-mucosal care tailored to the unique needs of patients with SJS/TEN.

Conclusions: This article offers a comprehensive evidence-based basis for effective skin-mucosal care to SJS/TEN patients. These best practices can improve patient outcomes and contribute to better clinical management.

Objective: The incidence of skin cancer is increasing worldwide every day. This study was conducted to evaluate body image perception and self-esteem in patients with skin cancer.

Methods: This prospective, cross-sectional, relationship-seeking, and descriptive research design study was carried out with 320 patients diagnosed with skin cancer to assess their body image perception and self-esteem levels. Data were collected between December 2023 and September 2024 at a city hospital. The data collection tools used in the study included the Patient Information Form, Body Image Scale, and Rosenberg Self-Esteem Scale (RSES).

Results: The average age of the patients participating in the study was found to be 63.9 ± 18.4 years. The average scores of the patients on the Body Image Scale and the RSES were 129.6 ± 26.1. It was found that the patients had a low body image perception. The average score on the RSES was 2.6 ± 1.5, indicating that the self-esteem level of the patients was at a moderate level. A statistically significant relationship was found between the scores on the Body Image Scale and the RSES ( P <.01). As the patients' positive body image perception increased, their self-esteem also improved.

Conclusions: It was determined that patients with skin cancer had a low body image perception and moderate self-esteem levels.

Objective: To explore the efficacy of a double-pocket fecal catheter system combined with camellia oil application in the prevention and treatment of irritant contact dermatitis (ICD) due to incontinence among patients in the intensive care unit (ICU).

Methods: A total of 248 patients admitted to the integrated ICU of the authors' hospital between January 1, 2022, and December 31, 2023, were selected. Among them, 204 high-risk patients with ICD due to incontinence were identified using the perineal assessment tool. The high-risk patients were divided into research group 1 (camellia oil application), research group 2 (double-pocket fecal catheter system combined with zinc oxide ointment application), research group 3 (double-pocket fecal catheter system combined with camellia oil application), and the control group (zinc oxide ointment application). The incidence, time of onset, severity, daily treatment cost, nursing efficiency, and healing time of ICD due to incontinence were compared among different groups.

Results: Different treatment methods had varying effects on the incidence (χ 2 =14.211, P <.001), time of onset ( F =5.521, P =.013), severity ( P =.023), daily treatment cost ( F =9.607, P <.001), nursing efficiency ( P =.037), and healing time of ICD due to incontinence ( F =3.907, P =.028). The performance of research group 3 in terms of ICD due to incontinence onset and healing times was superior to that of the other groups, with statistically significant differences ( P <.001).

Conclusions: Camellia oil combined with a double-pocket fecal catheter system exhibits high efficacy in the prevention and treatment of ICD due to incontinence among patients in the ICU.

Objective: To review currently available devices on pressure injury (PI) early detection, summarize challenges and opportunities to clinical implementation, and propose evaluation standards for device categories.

Data sources: PubMed and US Food and Drug Administration (FDA) databases.

Study selection: Published in English from peer-reviewed journals with full text available. Excluded if opinion statements, lack of empirical data, or unrelated to project's objective.

Data extraction: For both clinical device and research equipment: measurement mechanisms, measurement types, outcome/output, FDA classification, and indications for use. Addition data were extracted for clinical devices: instruction for use, end user, order requirement, and billable code.

Data synthesis: The 4 clinical devices are ultrasound, long-wave infrared thermography, subepidermal moisture assessment, and nearinfrared spectroscopy. The 3 research devices are laser Doppler flowmetry, laser speckle contrast imaging, and colorimetry.

Conclusions: The measurement mechanisms of all devices are unique and different from each other. One commonality is that they could measure the nonvisual signs of PI (eg, inflammation, edema, ischemia, and hypoxia) except colorimeter. Some clinical devices are promising to assist with early identification of PIs, especially in individuals with dark skin tones. Currently, there is no reimbursement available for early detection of PI. Current evidence did not support replacing the standard skin assessment of visual inspection and palpation with the devices reviewed, rather using validated devices to augment the current practice standard. This is especially recommended for individuals identified as high risk for a PI on admission to a facility.

Objective: To evaluate the clinical utility of a point-of-care, artificial intelligence-enabled multispectral imaging device in guiding targeted debridement of chronic leg ulcers in patients with leprosy, using a within-person randomized controlled pilot design.

Methods: Five adult male patients with lepromatous leprosy and at least 2 chronic leg ulcers each were enrolled in a split-body design. One ulcer per patient was randomized to the experimental arm (EA), where weekly debridement was guided by multispectral imaging, and the other to the control arm (CA), which received standard care. The device used autofluorescence to identify areas of suspected bacterial colonization and provided Gram-type classification. Healing was assessed by changes in wound area and Pressure Ulcer Scale for Healing scores over 18 weeks. Microbial confirmation was performed using standardized swab cultures.

Results: At 18 weeks, the mean wound size reduction was greater in the EA (84.46%) than in the CA (73.28%). Pressure Ulcer Scale for Healing scores decreased more rapidly in the EA (from 11.4 to 4.75) compared with the CA (from 11.0 to 6.75). One ulcer in each arm achieved full epithelialization, but the EA ulcer healed faster (5 vs. 9 weeks). Autofluorescence imaging enabled targeted systemic antimicrobial use in several cases. No adverse events were reported.

Conclusions: This pilot, the first of its kind in leprosy ulcer care, demonstrates the potential of artificial intelligence-enabled multispectral imaging to enhance wound healing through guided debridement. The technology offers real-time, noninvasive infection assessment that may support more effective, individualized wound management. Larger, blinded studies are warranted to validate these findings.

Objective: This study explores neonatal intensive care unit (NICU) nurses' perceptions of artificial intelligence (AI)-assisted neonatal skin assessment, focusing on its benefits, challenges, and ethical implications. Optimizing AI integration requires understanding nurses' attitudes.

Methods: A qualitative phenomenological approach was employed. Semi-structured interviews were conducted with 23 NICU nurses from a public hospital in Gaziantep, Turkey, between January and March 2025. Data were analyzed using inductive content analysis to identify emerging themes related to AI's impact on clinical decision-making, workflow efficiency, and professional autonomy.

Results: Findings revealed that nurses acknowledged AI's potential to enhance diagnostic accuracy, standardize assessments, and reduce interobserver variability. However, concerns were raised regarding algorithm reliability, professional autonomy, and ethical considerations. Nurses recognized AI's potential but stressed the need for transparency, training, and safeguards against over-reliance. Participants emphasized human oversight to ensure patient-centered care.

Conclusions: Artificial intelligence may improve neonatal skin assessment, but integration must balance technology and ethics. Engaging NICU nurses in AI system development and implementation is essential to fostering trust and ensuring alignment with clinical needs. Future research should assess AI's long-term impact and support interdisciplinary tool development that complements nursing expertise.

Objective: Pressure injuries (PIs) have become a global issue due to the significant social costs associated with various factors. Although many factors have been shown to have an impact on PIs, what specifically contributes to the worsening of the disease remains unclear. The aim of this study was to analyze variables that are highly correlated with PI aggravation using machine learning.

Methods: This observational study examined 71 Stage 2 PI patients from May 2018 to June 2021. The authors classified patients into 2 groups according to wound progression: (1) group A, aggravated group, and (2) group B, healed group. All 24 factors were analyzed using a Random Forest with hyperensemble approach, one of the machine learning algorithms. Each Random Forest is composed of 50,000 decision trees, and results from 100 Random Forests were hyperensembled. The mean decrease accuracy was calculated to evaluate the importance of the factor, and overlapped partial dependence plots were obtained to interpret the risk factors.

Results: Group A had 14 patients, whereas group B had 57. In an analysis using machine learning, the following factors were found to be highly associated with the aggravation of PIs: serum-albumin, Braden Scale, hemoglobin, wound size, serum-blood urea nitrogen, body mass index, serum-protein, and serum-creatinine. But the following variables were less associated: end-stage renal disease, sex, and myocardial infarction.

Conclusions: The PIs prediction model has broad application as a PI prevention tool. In addition, these findings can aid in the development of strategies to minimize the risk of PI aggravation.

Background: Diabetic foot ulcer (DFU) care is a challenge in reconstructive surgery. Artificial intelligence (AI) tools represent a new resource for patients with DFUs to seek information.

Objective: To evaluate the efficacy of ChatGPT-3.5 in responding to frequently asked questions related to DFU care.

Methods: Researchers posed 11 DFU care questions to ChatGPT-3.5 in December 2023. Questions were divided into topic categories of wound care, concerning symptoms, and surgical management. Four plastic surgeons in the authors' wound care department evaluated responses on a 10-point Likert-type scale for accuracy, comprehensiveness, and danger, in addition to providing qualitative feedback. Readability was assessed using 10 readability indexes.

Results: ChatGPT-3.5 answered questions with a mean accuracy of 8.7±0.3, comprehensiveness of 8.0±0.7, and danger of 2.2±0.6. ChatGPT-3.5 answered at the mean grade level of 11.9±1.8. Physician reviewers complimented the simplicity of the responses (n=11/11) and the AI's ability to provide general information (n=4/11). Three responses presented incorrect information, and the majority of responses (n=10/11) left out key information, such as deep vein thrombosis symptoms and comorbid conditions impacting limb salvage.

Conclusions: The researchers observed that ChatGPT-3.5 provided misinformation, omitted crucial details, and responded at nearly 4 grade levels higher than the American average. However, ChatGPT-3.5 was sufficient in its ability to provide general information, which may enable patients with DFUs to make more informed decisions and better engage in their care. Physicians must proactively address the potential benefits and limitations of AI.

Objective: Prevalence of hospital-acquired pressure injury (PI), as a critical measurement of medical care quality, has shown an upward trend. The aim of this study was to determine the predictive indicators of potential PIs and ensure that the predictive indicators can automatically be mined from electronic medical record systems.

Methods: The methods include 2 parts. One is the modified Delphi for indicator development, including clinical health care provider interviews, literature review, research group meetings, and Delphi survey. The other is feature selection, including extracting indicators from the health care information system (HIS) by structured query language and selecting indicators using the Random Forest technique.

Results: A predictive indicator system (with feature extraction rules for each indicator) consisting of 3 categories and 14 indicators was constructed. The experts' consensus was reached on all indicators (mean=4.28±0.65 to 4.94±0.23; coefficient of variation=4.63% to 17.20%; agreement rate=83.30% to 100.00%). The agreement between manual extraction and the computer's automatic extraction was good, with a Cohen κ score of 0.64 to 1.00. The accuracy of the good parsimonious prediction model was 95.26%.

Conclusions: This predictive indicator system is prepared for automatic PI prediction in the HIS. Many revisions should be conducted in further studies and practices in a real-life medical environment.

Abstract: Artificial intelligence (AI) is revolutionizing the landscape of skin and wound care by improving diagnostic accuracy, treatment effectiveness, and patient outcomes. Artificial intelligence-driven tools, including machine learning models and large language models (LLMs), enhance the precision of wound assessments, facilitate early infection detection, and streamline clinical workflows. In addition, these tools may aid in patient symptom reporting, bridging the communication gap between patients and health care providers. Current AI applications include image recognition for wound classification, patient-facing symptom-checking chatbots, and personalized treatment recommendations. The integration of AI technologies not only supports better clinical decision-making but also empowers patients through improved access, engagement, and education. These tools are currently aimed at supporting clinical decision-making, not replacing clinicians. Moving forward, the expansion of AI capabilities in skin and wound care holds great promise, driving cost-effective, scalable, and equitable health care solutions.