Obesity as a Risk Factor for Adverse Outcomes After Pedestrian Trauma Accidents in the Pediatric Population.

OBJECTIVE: The aim of the study is to determine whether overweight or obese children are at an increased risk for injury and adverse outcomes following pedestrian motor vehicle accidents. METHODS: We performed a retrospective study of patients between the ages of 2 and 17 who were pedestrians injured in a collision with a motorized vehicle, presenting to a level 1 trauma center, between January 1, 2010, to December 31, 2021. Patients with admission weights falling above the 90th percentile of the Centers for Disease Control and Prevention's sex-specific growth charts were identified as overweight/obese, those below the cutoff were categorized as nonobese. Groups were compared regarding demographics, mechanism of injury, Injury Severity Score, and Abbreviated Injury Scale by body region of injury. Outcome measures included hospital admission, length of hospital stay, intensive care unit (ICU) admission, ICU length of stay, and mortality. RESULTS: Of the 306 patients included, 72 (23.5%) were overweight/obese and 234 (76.5%) were nonobese. The mean Injury Severity Score scores were higher among overweight/obese patients (5.37 vs 8.74, P = 0.008). Overweight/obese children were more likely to sustain severe abdominal injuries (Abbreviated Injury Scale ≥ 3) (12.2% vs 5.1%; odds ratio [OR], 2.64; 95% CI, 1.07-6.56; P = 0.030), be admitted to the hospital (94.5% vs 74.3%; OR, 12.07; 95% CI, 2.87-50.72; P < 0.001), require ICU admission (31.0% vs 20.0%, OR, 1.87; 95% CI, 1.03-3.36; P = 0.036), and require a longer ICU stay (0.9 vs 0.4 days, P = 0.014) compared with nonobese patients. CONCLUSIONS: Obese and overweight children are at increased risk for higher injury severity scores, severe abdominal injuries, and ICU admission after pedestrian motor vehicle accidents.

Impact of Ketogenic and Low-Glycemic Diets on Inflammatory Skin Conditions.

Diet plays an emerging role in dermatologic therapy. The ketogenic and low-glycemic diets have potential anti-inflammatory and metabolic effects, making them attractive for treating inflammatory skin conditions. We provide an overview of the current evidence on the effects of ketogenic and low-glycemic diets on inflammatory skin conditions including acne, psoriasis, seborrheic dermatitis (SD), atopic dermatitis (AD), and hidradenitis suppurativa (HS). We conclude that low-glycemic diets show promise for treating acne, while the evidence for ketogenic diets in treating other inflammatory skin conditions is limited. Randomized clinical trials are needed to explore the efficacy of these diets as stand-alone or adjunctive treatments for inflammatory skin conditions.

The Efficacy of Stem Cells in Wound Healing: A Systematic Review.

Wound healing is an intricate process involving coordinated interactions among inflammatory cells, skin fibroblasts, keratinocytes, and endothelial cells. Successful tissue repair hinges on controlled inflammation, angiogenesis, and remodeling facilitated by the exchange of cytokines and growth factors. Comorbid conditions can disrupt this process, leading to significant morbidity and mortality. Stem cell therapy has emerged as a promising strategy for enhancing wound healing, utilizing cells from diverse sources such as endothelial progenitor cells, bone marrow, adipose tissue, dermal, and inducible pluripotent stem cells. In this systematic review, we comprehensively investigated stem cell therapies in chronic wounds, summarizing the clinical, translational, and primary literature. A systematic search across PubMed, Embase, Web of Science, Google Scholar, and Cochrane Library yielded 22,454 articles, reduced to 44 studies after rigorous screening. Notably, adipose tissue-derived mesenchymal stem cells (AD-MSCs) emerged as an optimal choice due to their abundant supply, easy isolation, ex vivo proliferative capacities, and pro-angiogenic factor secretion. AD-MSCs have shown efficacy in various conditions, including peripheral arterial disease, diabetic wounds, hypertensive ulcers, bullous diabeticorum, venous ulcers, and post-Mohs micrographic surgery wounds. Delivery methods varied, encompassing topical application, scaffold incorporation, combination with plasma-rich proteins, and atelocollagen administration. Integration with local wound care practices resulted in reduced pain, shorter healing times, and improved cosmesis. Stem cell transplantation represents a potential therapeutic avenue, as transplanted stem cells not only differentiate into diverse skin cell types but also release essential cytokines and growth factors, fostering increased angiogenesis. This approach holds promise for intractable wounds, particularly chronic lower-leg wounds, and as a post-Mohs micrographic surgery intervention for healing defects through secondary intention. The potential reduction in healthcare costs and enhancement of patient quality of life further underscore the attractiveness of stem cell applications in wound care. This systematic review explores the clinical utilization of stem cells and stem cell products, providing valuable insights into their role as ancillary methods in treating chronic wounds.

Retrospective analysis of onychomycosis prescribing patterns using the medicare part D prescribers database 2016-2020.

Onychomycosis, defined as a fungal nail infection, affects 5.5% of the global population. Our objectives were to analyse prescription trends of onychomycosis medications using the Medicare Part D Prescribers database from 2016 to 2020, stratified by physician specialty. There was a 4% annual increase in the total cost of onychomycosis medications, with a notable decrease of 12.8% in 2020 during the COVID-19 pandemic. Physicians demonstrated a strong consideration for price when selecting treatments, with the least expensive medications (ciclopirox and terbinafine) accounting for nearly 99% of all prescriptions. In contrast, the more costly medications (efinaconazole and tavaborole) were rarely prescribed. In addition, physicians often opted for the less costly generic versions of ciclopirox and itraconazole, prescribing them 99% and 91% of the time, respectively. Notably, physician assistants and nurse practitioners had higher overall increases in prescription rates, at 15%, compared to 1%-6% for other specialties. There are no recent United States onychomycosis guidelines, and our study emphasizes cost considerations when prescribing onychomycosis treatments.

Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

BACKGROUND: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. OBJECTIVE: To develop time-to-event risk prediction models for melanoma metastatic recurrence. METHODS: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. RESULTS: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. LIMITATIONS: Retrospective nature and cohort from one geography. CONCLUSIONS: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.

Pre-Existing Inflammatory Disease Predicts Cutaneous Immunotherapy Toxicity Development: A Multi-Institutional Cohort Study.

Background: Relationships between pre-existing inflammatory diseases (pIDs) and cutaneous immune-related adverse events (cirAEs) have not been well-studied. This study is to investigate associations between pIDs and cirAEs among immune-checkpoint inhibitor (ICI) recipients at the Mass General Brigham healthcare system. Methods: Electronic health records were reviewed to ascertain cirAE status. Patients' pID status was determined using International Classification of Diseases (ICD) codes. Cox proportional hazard, logistic regression, and linear regression models were performed. Results: Among 3607 ICI recipients, 1354 had pIDs, and 672 developed cirAEs. After covariate adjustments, patients with cutaneous pIDs (HR:1.56, p<0.001) or both cutaneous and non-cutaneous pIDs (HR:1.76, p<0.001) had increased cirAE risk in contrast to patients with non-cutaneous pIDs alone (HR:1.01, p=0.9). In adjusted ordinal logistic regression modeling, cutaneous pIDs (OR:1.55, p<0.0001) and the presence of both cutaneous pIDs and non-cutaneous pIDs (OR:1.71, p=0.002) were associated with increased cirAE severity. The time to cirAE onset was different between the cutaneous pID group and the non-cutaneous pID group (Mean: 98 vs. 146 days, p=0.021; Beta: -0.11, p=0.033). Conclusions: ICI recipients with cutaneous pIDs should have increased clinical monitoring due to their increased risk of cirAE development, severity, and earlier onset.

Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis.

Background: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes. Methods: We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies. Results: ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms. Conclusion: ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.

The 5-item modified frailty index predicts spinal osteotomy outcomes better than age in adult spinal deformity patients: an ACS - NSQIP analysis.

PURPOSE: To evaluate the utility of 5-Item Modified Frailty Index (mFI-5) as compared to chronological age in predicting outcomes of spinal osteotomy in Adult Spinal Deformity (ASD) patients. METHODS: Using Current Procedural and Terminology (CPT) codes, the American College of Surgeons National Surgery Quality Improvement Program (ACS-NSQIP) database was queried for adult patients undergoing spinal osteotomy from 2015 to 2019. Multivariate regression analysis was performed to evaluate the effect of baseline frailty status, measured by mFI-5 score, and chronological age on postoperative outcomes. Receiver-operating characteristic (ROC) curve analysis was performed to analyze the discriminative performance of age versus mFI-5. RESULTS: A total of 1,789 spinal osteotomy patients (median age 62 years) were included in the analysis. Among the patients assessed, 38.5% (n = 689) were pre-frail, 14.6% frail (n = 262), and 2.2% (n = 39) severely frail using the mFI-5. Based on the multivariate analysis, increasing frailty tier was associated with worsening outcomes, and higher odds ratios (OR) for poor outcomes were found for increasing frailty tiers as compared to age. Severe frailty was associated with the worst outcomes, e.g., unplanned readmission (OR 9.618, [95% CI 4.054-22.818], p < 0.001) and major complications (OR 5.172, [95% CI 2.271-11.783], p < 0.001). In the ROC curve analysis, mFI-5 score (AUC 0.838) demonstrated superior discriminative performance than age (AUC 0.601) for mortality. CONCLUSIONS: The mFI5 frailty score was found to be a better predictor than age of worse postoperative outcomes in ASD patients. Incorporating frailty in preoperative risk stratification is recommended in ASD surgery.