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Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.

Psoriasis is a chronic inflammatory skin disease. The visibility of erythematous plaques on the skin as well as the pain and itchiness caused by the skin lesions frequently leads to psychological distress in patients. Smartphone apps are widespread and easily accessible. Earlier studies have shown that apps can effectively complement current management strategies for patients with psoriasis. However, no analysis of such apps has been published to date. The aim of this study is to systematically identify and objectively assess the quality of current publicly available German apps for patients with psoriasis using the Mobile Application Rating Scale (MARS) and compile brief ready-to-use app descriptions. We conducted a systematic search and assessment of German apps for patients with psoriasis available in the Google Play Store and Apple App Store. The identified apps were randomly assigned to 1 of 3 reviewers, who independently rated them using the German MARS (MARS-G). The MARS-G includes 15 items from 4 different sections (engagement, functionality, aesthetics, and information) to create an overall mean score for every app. Scores can range from 1 for the lowest-quality apps to 5 for the highest-quality apps. Apps were ranked according to their mean MARS-G rating, and the highest-ranked app was evaluated independently by 2 patients with psoriasis using the user version of the MARS-G (uMARS-G). Furthermore, app information, including origin, main function, and technical aspects, was compiled into a brief overview. In total, we were able to identify 95 unique apps for psoriasis, of which 15 were available in both app stores. Of these apps, 5 were not specifically intended for patients with psoriasis, 1 was designed for clinical trials only, and 1 was no longer available at the time the evaluation process began. Consequently, the remaining 8 apps were included in the final evaluation. The mean MARS-G scores ranged from 3.51 to 4.18. The app with the highest mean MARS-G score was Psoriasis Helferin (4.18/5.00). When rated by patients, however, the app was rated lower in all subcategories, resulting in a mean uMARS-G score of 3.48. Most apps had a commercial background and a focus on symptom tracking. However, only a fraction of the apps assessed used validated instruments to measure the user's disease activity. App quality was heterogeneous, and only a minority of the identified apps were available in both app stores. When evaluated by patients, app ratings were lower than when evaluated by health care professionals. This discrepancy highlights the importance of involving patients when developing and evaluating health-related apps as the factors that make an app appealing to users may differ between these 2 groups. Deutsches Register Klinischer Studien DRKS00020963; https://tinyurl.com/ye98an5b.

Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve [AUC] of 0.9078 and logarithmic loss [LogLoss] of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.

Background: Psoriatic arthritis (PsA) is a chronic systemic inflammatory disease that affects up to 30% of patients with psoriasis. Diagnosis and treatment could be improved by implementing an interdisciplinary dermatological-rheumatological consultation (IDRC). Objectives: This study aimed to assess the effect of a face-to-face IDRC involving both a dermatologist and a rheumatologist evaluating patients in a single visit, on disease activity and burden in patients with PsA. Design: Prospective, single-center, cohort study. Methods: 202 patients with psoriasis were enrolled, among whom 115 individuals with psoriasis and musculoskeletal symptoms underwent an IDRC. Disease manifestations, comorbidities, and both objective and subjective disease activity scores were evaluated. Results: Out of the participants, 56 were diagnosed with definite PsA, while the remaining 146 had psoriasis. Nail involvement was associated with axial PsA (odds ratio 4.11; 95% CI 1.22–13.82; p = 0.02). Patients with PsA often experienced a prolonged time to diagnosis (mean 187 weeks) and had a significant psychosocial burden (mean Hospital Anxiety and Depression Index Scale [HADS]-Anxiety score of 7.66 and mean HADS-Depression score of 5.63). Post-IDRC, both objective and subjective disease parameters showed improvement, and patients required less time for consultations with healthcare professionals compared to before the IDRC. Conclusion: These findings suggest that an IDRC approach could effectively expedite and optimize the diagnosis and treatment of patients with psoriasis and musculoskeletal symptoms. Plain language summary The effects of an interdisciplinary consultation on patients with psoriatic arthritis Psoriatic arthritis is a chronic inflammatory disease that occurs in one in three patients with psoriasis. Finding the right diagnosis and treatment is challenging and often time-consuming, so it is crucial to find ways to establish care models which accelerate and improve this care. In our presented study, we established a face-to-face consultation with a dermatologist and a rheumatologist consulting each patient in a single visit and assessed the effects of this consultation on diagnosis, disease activity and patient burden. We studied 202 patients with psoriasis. Out of these, 115 showed both skin and musculoskeletal symptoms and therefore underwent our interdisciplinary consultation. We looked at their symptoms, other health issues, and how they felt about their disease. We found that 56 of them had definite psoriatic arthritis, while the rest had psoriasis alone. Nail problems were linked to inflammation in the axial skeleton. People with psoriatic arthritis often got diagnosed late (after about 3-4 years) and felt a lot of stress. Our interdisciplinary consultation enabled a fast diagnosis and improved treatment - patients had fewer symptoms and higher quality of life. They also needed less time with doctors. These findings suggest that interdisciplinary care can improve diagnosis and treatment in patients with psoriasis and musculoskeletal symptoms.

BackgroundThe patient-reported outcomes (PRO) version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) and the computerized adaptive testing (CAT) version of the EORTC quality-of-life questionnaire QLQ-C30 have been proposed as new PRO measures in oncology; however, their implementation in patients undergoing cancer surgery has not yet been evaluated.MethodsPatients undergoing elective abdominal cancer surgery were enrolled in a prospective multicenter study, and postoperative complications were recorded according to the Dindo–Clavien classification. Patients reported PRO data using the CAT EORTC QLQ-C30 and the PRO-CTCAE to measure 12 core cancer symptoms. Patients were followed-up for 6 months postoperatively. The study was carried out by medical students of the CHIR-Net SIGMA study network.ResultsData of 303 patients were obtained and analyzed across 15 sites. PRO-CTCAE symptoms ‘poor appetite’, ‘fatigue’, ‘exhaustion’ and ‘sleeping problems’ increased after surgery and climaxed 10–30 days postoperatively. At 3–6 months postoperatively, no PRO-CTCAE symptom differed significantly to baseline. Patients reported higher ‘social functioning’ (p = 0.021) and overall quality-of-life scores (p < 0.05) 6 months after cancer surgery compared with the baseline level. There was a lack of correlation between postoperative complications or death and any of the PRO items evaluated. Feasibility endpoints for student-led research were met.ConclusionThe two novel PRO questionnaires were successfully applied in surgical oncology. Postoperative complications do not affect health-reported quality-of-life or common cancer symptoms following major cancer surgery. The feasibility of student-led multicenter clinical research was demonstrated, but might be enhanced by improved student training.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries. A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.