Obstacles to Early Diagnosis and Treatment of Hidradenitis Suppurativa: Current Perspectives on Improving Clinical Management

Hidradenitis Suppurativa Online Documents Readability: An Analysis Including 23 European Languages

Purpose

Hidradenitis suppurativa (HS) is a complex disease with the vast burden to patients. The aim of the study was to evaluate readability of online electronic materials dedicated to HS.

Patients and Methods

The terms "hidradenitis suppurativa" and "acne inversa" translated into 23 official European Union languages were searched with Google. For each language, first 50 results were assessed for suitability. Included materials were focused on patient's education, had no barriers and were not advertisements. If both terms generated the same results, duplicated materials were excluded from the analysis. Origin of the article was categorized into non-profit, online-shop, dermatology clinic or pharmaceutical company class. Readability was evaluated with Lix score.

Results

A total of 458 articles in 22 languages were evaluated. The overall mean Lix score was 57 ± 9. This classified included articles as very hard to comprehend. Across all included languages significant differences in Lix score were revealed (P < 0.001). No significant differences across all origin categories and Lix scores were observed (all P > 0.05).

Conclusion

Despite the coverage of HS on the Internet, its complexity made it hard to comprehend. Dermatologist should ensure readable, barrier-free online educational materials. With adequate Google promotion, these would be beneficial for both physicians and patients.

Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach

Hidradenitis suppurativa (HS) is a chronic inflammatory follicular skin condition that is associated with significant psychosocial and economic burden and a diminished quality of life and work productivity. Accurate diagnosis of HS is challenging due to its unknown etiology, which can lead to underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We applied machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018 to develop a novel model to better understand HS underdiagnosis on a healthcare system level. The primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with an area under the curve (AUC) of 81%-82% observed among the top-performing models. The results of the models developed in this study could be input into the development of an impact of inaction model that determines the cost implications of HS diagnosis and treatment delay to the healthcare system.

Harnessing artificial intelligence for advancing early diagnosis in hidradenitis suppurativa

This perspective delves into the integration of artificial intelligence (AI) to enhance early diagnosis in hidradenitis suppurativa (HS). Despite significantly impacting Quality of Life, HS presents diagnostic challenges leading to treatment delays. We present a viewpoint on AI-powered clinical decision support system designed for HS, emphasizing the transformative potential of AI in dermatology. HS diagnosis, primarily reliant on clinical evaluation and visual inspection, often results in late-stage identification with substantial tissue damage. The incorporation of AI, utilizing machine learning and deep learning algorithms, addresses this challenge by excelling in image analysis. AI adeptly recognizes subtle patterns in skin lesions, providing objective and standardized analyses to mitigate subjectivity in traditional diagnostic approaches. The AI integration encompasses diverse datasets, including clinical records, images, biochemical and immunological data and OMICs data. AI algorithms enable nuanced comprehension, allowing for precise and customized diagnoses. We underscore AI's potential for continuous learning and adaptation, refining recommendations based on evolving data. Challenges in AI integration, such as data privacy, algorithm bias, and interpretability, are addressed, emphasizing the ethical considerations of responsible AI deployment, including transparency, human oversight, and striking a balance between automation and human intervention. From the dermatologists' standpoint, we illustrate how AI enhances diagnostic accuracy, treatment planning, and long-term follow-up in HS management. Dermatologists leverage AI to analyze clinical records, dermatological images, and various data types, facilitating a proactive and personalized approach. AI's dynamic nature supports continuous learning, refining diagnostic and treatment strategies, ultimately reshaping standards of care in dermatology.

Patients' Expectations and Satisfaction with the Patient-Doctor Relationship in Hidradenitis Suppurativa

INTRODUCTION

Hidradenitis suppurativa (HS) is a chronic inflammatory dermatosis with a vast psychosocial burden. We analyzed the actual and ideal patient-doctor relationship and patients' satisfaction with the patient-doctor relationship in relation to their satisfaction with life (SWL), HS-related quality of life, and psychopathological symptoms.

METHODS

105 HS patients (53% females; mean age 37.64 ± 14.01 years) were enrolled. Severity of the disease was measured using Hurley staging and the International HS Score System (IHS4). Instruments utilized: Patient Expectation Test; Satisfaction with Life Scale; HS Quality of Life; Patient Health Questionnaire-9; Generalized Anxiety Disorder-7; General Health Questionnaire.

RESULTS

Patients with Hurley I and mild IHS4 had the lowest satisfaction with the patient-doctor relationship. There were significant correlations between the actual patient-doctor relationship and the patients' SWL (r = 0.30; p = 0.002), depressive (r = -0.36; p < 0.01), anxiety (r = 0.37; p < 0.01) and psychopathological symptoms (r = -0.47; p < 0.0001) and between the satisfaction with the patient-doctor relationship and their SWL (r = -0.32; p = 0.00098). Multiple regression analysis revealed a significant influence of the following factors: Hurley II + III, psychopathological symptoms, and severe anxiety about the actual patient-doctor relationship and the satisfaction with the patient-doctor relationship.

CONCLUSIONS

Assessment of relations between patients and doctors is related to the patients' mental health and SWL. The usage of the Patient Expectation Test in clinical practice can improve the patient-doctor relationship and the general quality of care for and compliance by HS patients.