Clinical characteristics and treatment modalities in uremic and non uremic calciphylaxis - a dermatological single-center experience.

Calciphylaxis (CP) is a serious, potentially life-threatening disease that presents with medial calcification of small-sized vessels and painful ischemic ulcerations. Although calciphylaxis is frequently seen in patients with end-stage kidney disease on dialysis (calcific uremic arteriolopathy, CUA), there are reported cases of nonuremic calciphylaxis (NUC), which often remain undiagnosed. We conducted a retrospective chart review at our dermatological hospital and evaluated data concerning the epidemiology, comorbidities, medication, laboratory abnormalities, and therapeutic approaches of 60 patients diagnosed with calciphylaxis between 01/2012 and 12/2022. We identified 21 patients diagnosed with NUC and 39 with kidney disease. The predilection sites of skin lesions were the lower legs in 88% (n = 53), followed by the thigh and gluteal regions in 7% (n = 4). Significant differences were identified in comorbidities, such as atrial fibrillation (p < 0.001) and hyperparathyroidism (p < 0.01) accounting for CUA patients. Medication with vitamin K antagonists (p < 0.001), phosphate binders (p < 0.001), and loop diuretics (p < 0.01) was found to be associated with the onset of calciphylaxis. Hyperphosphatemia (p < 0.001), increased parathyroid hormone (p < 0.01) and triglyceride levels (p < 0.01), hypoalbuminemia (p < 0.01) and decreased hemoglobin values (p < 0.001) in the CUA cohort were significantly different from those in the NUC group. All patients with CUA received systemic medication. In contrast, only 38% (n = 8) of patients with NUC received systemic treatment. Striking discrepancies in the treatment of both cohorts were detected. In particular, NUC remains a disease pattern that is still poorly understood and differs from CUA in several important parameters.

Design and Implementation of an ETL-Process to Transfer Wound-Related Data into a Standardized Common Data Model.

For observational studies, which are relevant especially for chronic conditions like chronic wounds, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) offers a standardized database schema. In this study an ETL process for the transition of wound related data was developed. After understanding the data in general and mapping the relevant codes to concepts available in OMOP, the ETL process was implemented. In a first step, a generic algorithm to convert data to a csv format was implemented in Java. The resulting csv file was then processed within KNIME to be loaded into an OMOP CDM conformant database. During the whole ETL process, HL7 FHIR CodeSystem and ConceptMap resources were used for coding and mapping. First clinical test cases to retrieve data were successfully processed as an example to demonstrate the feasibility and usefulness. They concerned wound size at the first visit and the main issues of patients in the wound quality of life questionnaire (n = 24). In general, the ETL process worked well, yet some challenges arose, like post coordinated SNOMED codes or conditions, which might occur more than once.

Predilection sites of pyoderma gangrenosum: Retrospective study of 170 clearly diagnosed patients.

Pyoderma gangrenosum (PG) is a non-infectious, neutrophilic dermatosis that was difficult to diagnose in clinical practice. Today, the PARACELSUS score is a validated tool for diagnostics. Based on this score, patients with clearly diagnosed PG were examined with regard to predilection sites. In this retrospective study, the data of patients from the University Hospitals of Essen and Erlangen were analysed in whom the diagnosis of PG could be clearly confirmed using the PARACELSUS score. A total of 170 patients, 49 men (29%) and 121 women (71%) with an average age at first manifestation of 55.5 years, could be included in the analysis. The predilection sites were identified as the lower legs in 80.6% of the patients and the extensor sides in 75.2%. Other localisations of PG were the thighs in 14.1%, mammae and abdomen in 10.0% each, back and gluteal in 7.1% each, feet in 5.9%, arms in 4.7%, genital in 3.5% and head in 2.9%. This retrospective study is the first to identify a collective of PG patients with the highest data quality using the PARACELSUS score. It could be shown that PG can basically occur on the entire integument. However, the predilection sites of PG, which have now been reliably identified for the first time, are the lower legs and in particular the extensor sides.

Poor Adherence to Self-Applied Topical Drug Treatment Is a Common Source of Low Lesion Clearance in Patients with Actinic Keratosis-A Cross-Sectional Study.

BACKGROUND: Many treatments for actinic keratosis (AK) have been proven efficient in clinical trials. However, patients with AK may still experience unsatisfactory therapeutic outcomes in clinical practice. OBJECTIVES: To investigate patient adherence to self-applied topical interventions for AK and to explore factors associated with adherence in a real-world setting. METHODS: A cross-sectional study was conducted. Patients presenting with AK were asked to complete a self-administered questionnaire about their last topical AK treatment. RESULTS: A total of 113 patients participated with a median age of 78.5 years (range 58-94). Fifty-four patients (47.8%) received topical diclofenac, ten (8.8%) imiquimod, nine (8%) 5-fluorouracil, nine (8%) 5-fluorouracil plus salicylic acid, and eight (7.1%) photodynamic therapy. The non-adherence rate was 46.9% (n = 53), and only 30.9% (n = 35) used the topical treatments according to the summary of product characteristics (SmPC). These subgroups were compared. Patients of the non-compliant group were significantly less informed about the application time of the specific topical intervention (p = 0.002) and adjusted the timeframe (p < 0.001) and application frequency of the therapy (p = 0.02) independently of their physician. Conversely, patients reporting a sufficient pre-treatment consultation (p = 0.019) generally complied with the SmPC compliance application. CONCLUSIONS: A thorough pre-treatment consultation can help to increase treatment adherence and ensure lesion clearance.

Can Synthetic Images Improve CNN Performance in Wound Image Classification?

For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large amounts, Generative Adversarial Networks (GAN) are a standard tool for synthesising artificial training images that can be used to augment the data set. We investigated the quality of synthetic wound images regarding two aspects: (i) improvement of wound-type classification by a Convolutional Neural Network (CNN) and (ii) how realistic such images look to clinical experts (n = 217). Concerning (i), results show a slight classification improvement. However, the connection between classification performance and the size of the artificial data set is still unclear. Regarding (ii), although the GAN could produce highly realistic images, the clinical experts took them for real in only 31% of the cases. It can be concluded that image quality may play a more significant role than data size in improving the CNN-based classification result.

Clinical Manifestation of Cutaneous Leishmaniasis Following a Mechanical Trauma.

Unusual skin ulcers frequently represent a diagnostic challenge. When the most common disease entities such as arterial, venous or diabetic ulcers have been excluded, the question of further differential diagnoses and procedures arises. Other possible causes include chronic inflammatory diseases, neoplasia, self-inflicted wounds, primary infectious diseases and physical/chemical damage to the skin. To narrow down the differential diagnoses, a detailed history of the patient is essential, which also needs to include events further back in time.

Automatic Wound Type Classification with Convolutional Neural Networks.

Chronic wounds are ulcerations of the skin that fail to heal because of an underlying condition such as diabetes mellitus or venous insufficiency. The timely identification of this condition is crucial for healing. However, this identification requires expert knowledge unavailable in some care situations. Here, artificial intelligence technology may support clinicians. In this study, we explore the performance of a deep convolutional neural network to classify diabetic foot and venous leg ulcers using wound images. We trained a convolutional neural network on 863 cropped wound images. Using a hold-out test set with 80 images, the model yielded an F1-score of 0.85 on the cropped and 0.70 on the full images. This study shows promising results. However, the model must be extended in terms of wound images and wound types for application in clinical practice.

An Image Based Object Recognition System for Wound Detection and Classification of Diabetic Foot and Venous Leg Ulcers.

Venous leg ulcers and diabetic foot ulcers are the most common chronic wounds. Their prevalence has been increasing significantly over the last years, consuming scarce care resources. This study aimed to explore the performance of detection and classification algorithms for these types of wounds in images. To this end, algorithms of the YoloV5 family of pre-trained models were applied to 885 images containing at least one of the two wound types. The YoloV5m6 model provided the highest precision (0.942) and a high recall value (0.837). Its mAP_0.5:0.95 was 0.642. While the latter value is comparable to the ones reported in the literature, precision and recall were considerably higher. In conclusion, our results on good wound detection and classification may reveal a path towards (semi-) automated entry of wound information in patient records. To strengthen the trust of clinicians, we are currently incorporating a dashboard where clinicians can check the validity of the predictions against their expertise.