Single-Lesion Skin Cancer Risk Stratification Triage Pathway.

Importance: Access to timely dermatologic care remains a challenge, especially for patients with new skin lesions. Assessing the efficiency of new triage pathways may assist in better resource allocation and shorter time to care. Objective: To evaluate whether a rule-based triage system was associated with better skin cancer risk stratification of patients and reduced wait times. Design, Setting, and Participants: This retrospective quality improvement cohort study of patients referred to Stanford University dermatology clinics was conducted between November 2017 and January 2023. A rules-based triage system based on a priori-determined high-risk lesion characteristics was implemented. Exposures: Referral reasons and risk factors of patients provided by their primary care physicians. Main Outcomes and Measures: Biopsy results of patients (diagnosis of any skin cancer and melanoma) at their visit or within 6 months after the visit. Regression models were used to assess the association between risk factors at referral and (1) biopsy outcomes and (2) time to first visit, adjusting for sociodemographic factors. Results: Among 37 478 patients (mean [SD] age, 54 (18) years; 21 292 women [57%]), the rates of aggregate biopsy, malignant biopsy specimens, and melanoma were comparable across patients seen after (n = 12 302) and before (n = 25 176) the implementation of the new triage pathway. Patients seen through the lesion pathway had a higher risk of having malignant biopsy results (adjusted risk ratio [aRR], 1.6; 95% CI, 1.4-1.9) and melanoma (aRR, 2.0; 95% CI, 1.2-3.2) than those not seen through the pathway. Lesions that were concerning to referring clinicians for skin cancer were associated with an increased risk of skin cancer (all skin cancer: aRR, 2.8; 95% CI, 2.2-3.5; melanoma: aRR, 2.02; 95% CI, 1.1-3.7). Patients in the 3 high-risk lesion groups were seen faster in the new triage pathway (mean reduction, 26 days; 95% CI, 18-34 days). Conclusions and Relevance: In this study, a new automated, rules-based referral pathway was implemented that expedited care for patients with high-risk skin cancer. This reform may have contributed to improving patient stratification, reducing the time from referral to first encounter, and maintaining accuracy in identifying malignant lesions. The findings highlight the potential to optimize clinical resource allocation by better risk stratification of referred patients.

Parental E-Cigarette Use and Pediatric Atopic Dermatitis.

This cross-sectional study uses data from the 2014-2018 National Health Interview Survey to assess whether there is an association between parental e-cigarette use and atopic dermatitis in children.

Noninvasive Detection of Skin Cancer by Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging.

We report a technique for the noninvasive detection of skin cancer by imprint desorption electrospray ionization mass spectrometry imaging (DESI-MSI) using a transfer agent that is pressed against the tissue of interest. By noninvasively pressing a tape strip against human skin, metabolites, fatty acids, and lipids on the skin surface are transferred to the tape with little spatial distortion. Running DESI-MSI on the tape strip provides chemical images of the molecules on the skin surface, which are valuable for distinguishing cancer from healthy skin. Chemical components of the tissue imprint on the tape strip and the original basal cell carcinoma (BCC) section from the mass spectra show high consistency. By comparing MS images (about 150-µm resolution) of same molecules from the tape strip and from the BCC section, we confirm that chemical patterns are successfully transferred to the tape stripe. We also used the technique to distinguish cherry angiomas from normal human skin by comparing the molecular patterns from a tape strip. These results demonstrate the potential of the imprint DESI-MSI technique for the noninvasive detection of skin cancers as well as other skin diseases before and during clinical surgery.

Development of a digital tool for home-based monitoring of skin disease for older adults.

We developed a digital tool for home-based monitoring of skin disease, our digital tool. In the current observational pilot study, we found that DORA is feasible to use in practice, as it has a high patient compliance, retention and satisfaction. Clinicans rated the photos generally good quality or perfect quality. These results show that the digital health tool DORA can easily be used by patients to send photos to their dermatologist, which could reduce unnecessary clinical visits. It may also be used in other settings where digital literacy barriers and unequal access to dermatologists contribute to healthcare disparities.

Dupilumab significantly improves sleep in adults with atopic dermatitis: results from the 12-week placebo-controlled period of the 24-week phase IV randomized double-blinded placebo-controlled DUPISTAD study.

BACKGROUND: Sleep disturbance is a prominent symptom of atopic dermatitis (AD) and can result in insomnia, daytime fatigue, drowsiness, reduced productivity and impaired quality of life (QoL). OBJECTIVES: The Dupilumab Effect on Sleep in AD Patients (DUPISTAD) phase IV randomized double-blinded placebo-controlled study evaluated the impact of dupilumab treatment on sleep and other patient- and physician-reported outcomes. METHODS: Adults with moderate-to-severe AD were randomized 2 : 1 to dupilumab 300 mg once every 2 weeks (q2w) or placebo for 12 weeks; concomitant topical corticosteroids were permitted. Patients subsequently entered an open-label phase and received dupilumab 300 mg q2w for a further 12 weeks. The primary endpoint was the percentage change in sleep quality from baseline to week 12, assessed using a novel numeric rating scale (NRS). Secondary and exploratory endpoints included percentage change in peak pruritus NRS (PP NRS), change in SCORing Atopic Dermatitis (SCORAD), SCORAD sleep visual analogue scale (VAS), Eczema Area and Severity Index, Patient-Reported Outcomes Measurement Information System (PROMIS) sleep-related impairment T-score and the Epworth Sleepiness Scale. Sleep diary and wrist actigraphy measurements were recorded throughout the study. RESULTS: In total, 127 patients received dupilumab and 61 patients received placebo. Demographic and baseline disease characteristics were balanced between groups. Sleep quality NRS significantly improved in patients treated with dupilumab by week 12 vs. placebo [least squares mean of the difference (LSMD) -15.5%, P < 0.001]. PP NRS (LSMD -27.9%, P < 0.001), SCORAD (LSMD -15.1, P < 0.001), SCORAD sleep VAS (LSMD -2.1, P < 0.001) and PROMIS T-score (LSMD -3.6, P < 0.001) were also significantly improved at week 12 with dupilumab vs. placebo. The overall percentage of patients reporting treatment-emergent adverse events was lower in the dupilumab group (56.7%) than in the placebo group (67.2%). CONCLUSIONS: Dupilumab significantly improved sleep quality and perception of sleep continuity, itch, metrics of AD severity and QoL in adults with moderate-to-severe AD, with an acceptable safety profile compared with placebo.

Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research.

Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists.

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality.

Importance: Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination. Objective: To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients. Design, Setting, and Participants: This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone. Interventions: During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos. Main Outcomes and Measures: The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support. Results: Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%. Conclusions and Relevance: In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

Long-term safety and efficacy of gene-corrected autologous keratinocyte grafts for recessive dystrophic epidermolysis bullosa.

BACKGROUND: Recessive dystrophic epidermolysis bullosa (RDEB) is a rare, devastating blistering genodermatosis caused by mutations in the COL7A1 gene, which encodes for type VII collagen and is necessary for dermal-epidermal adhesion and integrity. Disease manifestations include severe and debilitating wounds, aggressive squamous cell carcinomas, and premature death; however, there are currently no approved therapies. This Phase 1/2a, open-label study evaluated the long-term efficacy and safety of gene-corrected autologous keratinocyte grafts (EB-101) for chronic RDEB wounds. METHODS: Autologous keratinocytes were harvested from participants with severe RDEB, transduced with a retrovirus containing the full-length COL7A1 gene, and grown into 5 × 7 cm (35 cm2) sheets. Gene-corrected keratinocyte sheets were then transplanted onto chronic RDEB wounds present for ≥ 12 weeks. RESULTS: Seven adult participants with severe RDEB were grafted with six sheets each (42 total sheets) onto wounds and followed for a mean of 5.9 years (range 4-8 years). Long-term improvements in wound healing and symptoms were observed. At year five, 70% (21/30) of treated sites demonstrated ≥ 50% wound healing compared to baseline by investigator global assessment. No sites with ≥ 50% wound healing were painful or pruritic, compared to 67% (6/9) of sites with < 50% wound healing (p < 0.001) at year five. Grafts were well-tolerated throughout long-term follow-up. No serious adverse events related to treatment were reported over a mean of 5.9 years of follow-up. No persistent systemic autoimmunity against type VII collagen or replication-competent retrovirus infections were identified, and no participants developed squamous cell carcinomas related to treatment during long-term follow-up. CONCLUSIONS: Treatment with EB-101 appears safe and efficacious, and produces long-term improvements in wound healing, pain, and itch for RDEB patients. Results from the Phase 3 randomized controlled trial are forthcoming. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01263379. Registered December 15, 2010. https://clinicaltrials.gov/ct2/show/NCT01263379.

Disparities in dermatology AI performance on a diverse, curated clinical image set.

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

A global, cross-sectional survey of patient-reported outcomes, disease burden, and quality of life in epidermolysis bullosa simplex.

BACKGROUND: Epidermolysis bullosa simplex (EBS) comprises a group of rare, blistering genodermatoses. Prior work has been limited by small sample sizes, and much remains unexplored about the disease burden and health-related quality of life (QOL) of patients with EBS. The aim of this study was to characterize the most common patient-reported clinical manifestations and the health-related impact of QOL in EBS, and to examine differences in disease burden by age. METHODS: Patients with a diagnosis of epidermolysis bullosa (EB) or their caregivers completed a one-time online survey administered by EBCare, an international online EB registry. Survey data from respondents self-reporting a diagnosis of EBS were analyzed for clinical and wound manifestations, medication use, and QOL (using Quality of Life in Epidermolysis Bullosa [QOLEB] scores). Differences across age groups were assessed using Kruskal-Wallis and Fisher's exact tests. RESULTS: There were 214 survey respondents with EBS. The mean age was 32.8 years (standard deviation = 19.2). Many respondents reported blisters (93%), recurrent wounds (89%), pain (74%), chronic wounds (59%), itch (55%), and difficulty walking (44%). Mean QOLEB score was 14.7 (standard deviation = 7.5) indicating a "moderate" impact on QOL, and 12% of respondents required regular use of opiates. Findings were consistent in subgroup analyses restricted to respondents with diagnostic confirmation via genetic testing or skin biopsy (n = 63 of 214). Age-stratified analyses revealed differences in disease burden: younger respondents were more likely to self-report severe disease (24% vs. 19% vs. 5% for respondents aged 0-9 vs. 10-17 vs. 18 + , p = 0.001), failure to thrive (9% vs. 15% vs. 3%, p = 0.02), and use of gastrostomy tubes (15% vs. 12% vs. 1%, p < 0.001) and topical antibiotics (67% vs. 69% vs. 34%, p < 0.001), while older respondents were more likely to be overweight or obese (6% vs. 0% vs. 51%, p < 0.001) and have difficulty walking (24% vs. 46% vs. 48%, p = 0.04). CONCLUSIONS: In the largest international cross-sectional survey of EBS patients conducted, respondents reported extensive disease burden including significant wounding, pain, itch, difficulty walking, and impact on QOL. Age stratified disease manifestations. These findings suggest significant unmet need, and treatment and counseling for EBS patients should consider age-specific differences.

Efficacy and Safety of Upadacitinib in Patients With Moderate to Severe Atopic Dermatitis: Analysis of Follow-up Data From the Measure Up 1 and Measure Up 2 Randomized Clinical Trials.

Importance: Primary results from the Measure Up 1 and Measure Up 2 studies demonstrated upadacitinib efficacy and safety through 16 weeks in patients with atopic dermatitis. Longer-term outcomes remain unknown. Objective: To evaluate long-term (52 weeks) efficacy and safety of upadacitinib treatment in patients with atopic dermatitis. Design, Setting, and Participants: Measure Up 1 and Measure Up 2 are ongoing double-blind, placebo-controlled, replicate phase 3 randomized clinical trials that include adults and adolescents with moderate to severe atopic dermatitis at 151 and 154 centers, respectively. Cutoffs for this analysis were December 21, 2020 (Measure Up 1), and January 15, 2021 (Measure Up 2). Interventions: Patients were randomized 1:1:1 to receive once-daily oral upadacitinib 15 mg, 30 mg, or placebo. At week 16, patients randomized at baseline to receive upadacitinib 15 mg (273 and 260 patients in Measure Up 1 and Measure Up 2, respectively) and 30 mg (270 and 268 patients) continued assigned treatment; placebo-treated patients were rerandomized 1:1 to receive upadacitinib 15 mg (121 and 120 patients in Measure Up 1 and Measure Up 2, respectively) or 30 mg (123 and 121 patients) in a double-blinded manner. Main Outcomes and Measures: Safety and efficacy, including 75% improvement in the Eczema Area and Severity Index and Validated Investigator Global Assessment for Atopic Dermatitis score of clear (0) or almost clear (1) with 2 or greater grades of improvement, were assessed. Results: Measure Up 1 and Measure Up 2 included a total of 1609 patients (mean [SD] age, 33.8 [15.6] years; 727 women [45.2%]; 882 men [54.8%]). Efficacy at week 16 was maintained through week 52. At week 52, 75% improvement in the Eczema Area and Severity Index was achieved by 82.0% (95% CI, 77.0%-86.9%) and 79.1% (95% CI, 73.9%-84.4%) of patients continuing the 15-mg dose and 84.9% (95% CI, 80.3%-89.5%) and 84.3% (95% CI, 79.6%-89.0%) of patients continuing the 30-mg dose (for Measure Up 1 and Measure Up 2, respectively); Validated Investigator Global Assessment for Atopic Dermatitis score of clear (0) or almost clear (1) with 2 or greater grades of improvement was achieved by 59.2% (95% CI, 52.9%-65.5%) and 52.6% (95% CI, 46.2%-59.1%) and 62.5% (95% CI, 56.3%-68.7%) and 65.1% (95% CI, 58.9%-71.2%) of patients in the Measure Up 1 and Measure Up 2 studies, respectively. Treatment discontinuation due to adverse events was low overall but was slightly higher for the upadacitinib 30-mg dose. Both upadacitinib doses were well tolerated with no new safety signals. Conclusions and Relevance: In this analysis of follow-up data from 2 randomized clinical trials, longer-term treatment of adolescents and adults with moderate to severe atopic dermatitis with upadacitinib demonstrated a favorable benefit-risk profile, with sustained efficacy responses through 52 weeks. Trial Registration: ClinicalTrials.gov Identifiers: NCT03569293 (Measure Up 1) and NCT03607422 (Measure Up 2).

A systematic literature review of the disease burden in patients with recessive dystrophic epidermolysis bullosa.

BACKGROUND/OBJECTIVE: Recessive dystrophic epidermolysis bullosa (RDEB) is a genetic collagen disorder characterized by skin fragility leading to blistering, wounds, and scarring. There are currently no approved curative therapies. The objective of this manuscript is to provide a comprehensive literature review of the disease burden caused by RDEB. METHODS: A systematic literature review was conducted in MEDLINE and Embase in accordance with PRISMA guidelines. Observational and interventional studies on the economic, clinical, or humanistic burden of RDEB were included. RESULTS: Sixty-five studies were included in the review. Patients had considerable wound burden, with 60% reporting wounds covering more than 30% of their body. Increases in pain and itch were seen with larger wound size. Chronic wounds were larger and more painful than recurrent wounds. Commonly reported symptoms and complications included lesions and blistering, anemia, nail dystrophy and loss, milia, infections, musculoskeletal contractures, strictures or stenoses, constipation, malnutrition/nutritional problems, pseudosyndactyly, ocular manifestations, and dental caries. Many patients underwent esophageal dilation (29-74%; median dilations, 2-6) and gastrostomy tube placement (8-58%). In the severely affected population, risk of squamous cell carcinoma (SCC) was 76% and mortality from SCC reached 84% by age 40. Patients with RDEB experienced worsened quality of life (QOL), decreased functioning and social activities, and increased pain and itch when compared to other EB subtypes, other skin diseases, and the general population. Families of patients reported experiencing high rates of burden including financial burden (50-54%) and negative impact on private life (79%). Direct medical costs were high, though reported in few studies; annual payer-borne total medical costs in Ireland were $84,534 and annual patient-borne medical costs in Korea were $7392. Estimated annual US costs for wound dressings ranged from $4000 to $245,000. Patients spent considerable time changing dressings: often daily (13-54% of patients) with up to three hours per change (15-40%). CONCLUSION: Patients with RDEB and their families/caregivers experience significant economic, humanistic, and clinical burden. Further research is needed to better understand the costs of disease, how the burden of disease changes over the patient lifetime and to better characterize QOL impact, and how RDEB compares with other chronic, debilitating disorders.

TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos.

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.