Automated Machine Learning Predicts Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris et Arthritis And Uncovers New Influences On Disease Progression: Retrospective Study.

BACKGROUND: Psoriasis vulgaris (PsV) and Psoriatic arthritis (PsA) are intertwined multifactorial diseases with significant impact on health and quality of life, which can be debilitating due to chronicity and treatment complexity. Predicting treatment response and disease progression in these conditions is challenging, but crucial for optimising therapeutic interventions. The advancing technology of automated machine learning (AutoML) holds great promise for rapidly building highly accurate predictive models based on patient features and treatment data. OBJECTIVE: The study aimed to develop highly accurate ML models using AutoML to address key clinical questions in PsV and PsA patients, including predicting therapy changes and identifying reasons for therapy changes, factors influencing skin lesion progression or factors associated with an abnormal BASDAI score. METHODS: After extensive dataset preparation of clinical study data from 309 PsV and PsA patients, a secondary dataset was created and ultimately analysed using AutoML to build a variety of predictive models and select the most accurate one for each variable of interest. RESULTS: "Therapy change at 24 weeks follow-up" was modelled using the eXtreme Gradient Boosted Trees Classifier with Early Stopping model (AUC of 0.9078 and LogLoss of 0.3955 for the holdout partition) to gain insight into the factors influencing therapy change, such as the initial systemic therapeutic agent, the score achieved in the CASPAR classification criteria at baseline, and changes in quality of life. An AVG blender of 3 models (Gradient Boosted Trees Classifier, ExtraTrees Classifier, Eureqa Generalised Additive Model Classifier) with an AUC of 0.8750 and a LogLoss of 0.4603 was used to predict therapy changes on two hypothetical patients to highlight the importance of such influencing factors. Notably, treatments such as MTX or specific biologicals showed a lower propensity for change. A further AVG Blender of RandomForest Classifier, eXtreme Gradient Boosted Trees Classifier and Eureqa Classifier (AUC of 0.9241 and LogLoss of 0.4498) was then used to estimate "PASI change after 24 weeks" with the primary predictors being the initial PASI score, change in pruritus and change in therapy. A lower initial PASI score, and consistently low pruritus were associated with better outcomes. Finally, "BASDAI classification at baseline" was analysed using an AVG Blender of Eureqa Generalised Additive Model Classifier, eXtreme Gradient Boosted Trees Classifier with Early Stopping and Dropout Additive Regression Trees Classifier with an AUC of 0.8274 and LogLoss of 0.5037. Factors influencing BASDAI scores included initial pain, disease activity and HADS scores for depression and anxiety. Increased pain, disease activity and psychological distress were generally likely to lead to higher BASDAI scores. CONCLUSIONS: The practical implications of these models for clinical decision making in PsV and PsA have the potential to guide early investigation and treatment, contributing to improved patient outcomes.

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Impact of an eHealth Smartphone App on Quality of Life and Clinical Outcome of Patients With Hand and Foot Eczema: Prospective Randomized Controlled Intervention Study.

BACKGROUND: Chronic hand and foot eczema is a polyetiological dermatological condition. Patients experience pain, itching, and sleep disturbances and have a reduced quality of life. Skin care programs and patient education can improve the clinical outcome. eHealth devices offer a new opportunity to better inform and monitor patients. OBJECTIVE: This study aimed to systematically analyze the effect of a monitoring smartphone app combined with patient education on the quality of life and clinical outcome of patients with hand and foot eczema. METHODS: Patients in the intervention group received an educational program; attended study visits on weeks 0, 12, and 24; and had access to the study app. Patients in the control group attended the study visits only. The primary end point was a statistically significant reduction in Dermatology Life Quality Index, pruritus, and pain at weeks 12 and 24. The secondary end point was a statistically significant reduction in the modified Hand Eczema Severity Index (HECSI) score at weeks 12 and 24. This is an interim analysis at week 24 of the 60-week randomized controlled study. RESULTS: In total, 87 patients were included in the study and randomized to the intervention group (n=43, 49%) or control group (n=44, 51%). Of the 87 patients, 59 (68%) completed the study visit at week 24. There were no significant differences between the intervention and control groups regarding quality of life, pain, itch, activity, and clinical outcome at weeks 12 and 24. Subgroup analysis revealed that, compared with the control group, the intervention group with an app use frequency of fewer than once every 5 weeks had a significant improvement in the Dermatology Life Quality Index at weeks 12 (P=.001) and 24 (P=.05), in pain measured on a numeric rating scale at weeks 12 (P=.02) and 24 (P=.02), and in the HECSI score at week 12 (P=.02). In addition, the HECSI scores assessed on the basis of pictures taken by the patients of their hands and feet correlated strongly with the HECSI scores recorded by physicians during regular personal visits (r=0.898; P=.002) even when the quality of the images was not that good. CONCLUSIONS: An educational program combined with a monitoring app that connects patients with their treating dermatologists can improve quality of life if the app is not used too frequently. In addition, telemedical care can at least partially replace personal care in patients with hand and foot eczema because the analysis of the pictures taken by the patients correlates strongly with that of the in vivo images. A monitoring app such as the one presented in this study has the potential to improve patient care and should be implemented in daily practice. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00020963; https://drks.de/search/de/trial/DRKS00020963.

[Assessment of an e­learning platform for dermatosurgery]. / Beurteilung einer E­Learning-Plattform für Dermatochirurgie.

BACKGROUND: Dermatosurgical (DS) teaching is based on a combination of reading/understanding textbooks and applying surgical procedures (±â€¯supervision). Most textbooks are primarily text-centered. The text is visually supported by photos/sketches (S) and possibly videos (V). A learning goal of this teaching should be that the learner is confident to perform a procedure independently. METHODS: We have developed an online-based platform, the FlapFinder (FF; www.skin-surgery.org ), which teaches the user DS in the facial region primarily in the form of S + V. These are supported by a short text (T) and bonus material (B). B contains personal recommendations from the FF authors. A SurveyMonkey® (Survey Monkey, San Mateo, CA, USA) analysis should clarify how this is assessed by the user. RESULTS: In all, 62 participants completed the questionnaire in full. This was a heterogeneous group (27 dermatologists vs. 35 non-dermatologists; 32â€¯× clinic vs. 30â€¯× non-clinic) with different prior experience. The majority of users found that the combination of T + S + V helped them to understand (55/62; 88.7%), remember (53/62, 85.5%), and perform the procedures independently (43/62; 69.3%). While S + V were most frequently used (22/62; 35.5% and 27/62; 43.6%), users reported having benefited most from this (20/62; 32.3% and 24/62; 38.7%), T + B were used less (0/62, 0.0% and 2/62; 3.2%). Nevertheless, the majority would not want to do without either S, V, T, or B (49/62; 79%). CONCLUSION: The combination of S + V + T + B is rated positively by DS learners. S + V are rated as particularly helpful. Future studies must clarify whether the learning objective of the concrete practical performance of DS is changed by e­media.

Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.

The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.

Impact of an eHealth Smartphone App on the Mental Health of Patients With Psoriasis: Prospective Randomized Controlled Intervention Study.

BACKGROUND: Psoriasis has a negative impact on patients' physical and mental health and can lead to anxiety and depression. Disease management strategies, including educational programs and eHealth devices, have been shown to improve health care for several chronic diseases. However, such disease management strategies are lacking in the routine care of patients with psoriasis. OBJECTIVE: This study aims to study the impact of a novel intervention that combines an educational program with a disease management smartphone app on the mental health of patients with psoriasis. METHODS: Patients with psoriasis in the intervention group received an educational program; attended visits on weeks 0, 12, 24, 36, and 60; and had access to the study app. Patients in the control group only attended the visits. The primary endpoint was a significant reduction of scores on the Hospital Anxiety and Depression Scale (HADS). Secondary end points were reductions in Dermatology Life Quality Index score, Psoriasis Area and Severity Index score, pruritus, and pain, as well as improvements in mood and daily activities. In addition, modulating effects of sex, age, disease duration, and app use frequency were evaluated. RESULTS: A total of 107 patients were included in the study and randomized into the control group (53/107, 49.5%) or intervention group (54/107, 50.5%). Approximately 71.9% (77/107) of the patients completed the study. A significant reduction in HADS-Depression (HADS-D) in the intervention group was found at weeks 12 (P=.04) and 24 (P=.005) but not at weeks 36 (P=.12) and 60 (P=.32). Patient stratification according to app use frequency showed a significant improvement in HADS-D score at weeks 36 (P=.004) and 60 (P=.04) and in HADS-Anxiety (HADS-A) score at weeks 36 (P=.04) and 60 (P=.05) in the group using the app less than once every 5 weeks. However, in patients using the app more than once every 5 weeks, no significant reduction in HADS-D (P=.84) or HADS-A (P=.20) score was observed over the 60-week study period compared with that observed in patients in the control group. All findings were independent of sex, age, and disease duration. CONCLUSIONS: These findings support the use of a disease management smartphone app as a valid tool to achieve long-term improvement in the mental health of patients with psoriasis if it is not used too frequently. Further studies are needed to analyze the newly observed influence of app use frequency. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00020755; https://tinyurl.com/nyzjyvvk.

Reconstruction of Full Thickness Defects on the Scalp With Artificial Dermal Regeneration Template: Analysis of Long-Term Results in 68 Cases.

BACKGROUND: Artificial skin substitute templates have been shown to be a reliable solution for the reconstruction of large scalp defects with exposed skull bone, but there is a lack of long-term data. OBJECTIVE: The aim of this retrospective study was to investigate the long-term outcome of the procedure in a large cohort of 68 cases. MATERIALS AND METHODS: In total, 58 patients with 68 full thickness scalp defects with exposed skull bone, were included. Mean follow-up time was 24 (±19) months. RESULTS: The mean size of the defects was 63 (±54) cm2. During the follow-up period, no local recurrences occurred. Complications were observed in 13% of the cases including template necrosis (4%), infections (4%), ulcerations (3%), and autograft necrosis (2%). During the final follow-up, 26 patients had died due to internal diseases not associated with the surgery. Cosmetic results were rated good by the patients and an independent observer. CONCLUSION: The use of a dermal regeneration template for the reconstruction of large, full thickness defects of the scalp with exposed skull bone is a reliable method regarding the complication rate, safety of the procedure, and cosmetic outcome. Limitations of this study are the retrospective and single center design.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008-2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Reconstruction of Nasal Defects With Dermal Skin Substitutes-A Retrospective Study of 36 Defects.

OBJECTIVES: It is uncertain whether dermal regeneration templates (DRTs) are helpful to reconstruct nasal defects. The aim of this study was to assess whether the aesthetic subunits determine the outcome. METHODS: In this unicentric, retrospective study, the surgical procedures and outcomes of patients who received DRTs to reconstruct nasal defects were assessed and compared with the involved aesthetic subunits. RESULTS: DRTs were used for reconstruction of 36 nasal defects in 35 patients with involvement of 76 aesthetic subunits: nasal sidewall (n = 21), nasal ala (n = 13), nasal tip/columella (n = 12, n = 1, respectively), nasal dorsum (n = 12), and extranasal aesthetic areas (n = 17). Fifty-eight nasal and 8 extranasal aesthetic subunits were reconstructed with DRTs, 10 subunits with a flap. Twenty-nine of 36 defects healed without any complications (80.5%). All reconstructed nasal tips/columella and the nasal dorsa healed without any complications. Region-specific complications were retraction of the ala rim (4/12; 33.3% of the patients with involvement of the nasal ala) and the formation of a fistula in the nasal sidewall (1/21; 4.8%). Region-specific complications of extranasal subunits were the development of an ectropium (2/3; 66.7% of the patients with involvement of the lower lid). CONCLUSIONS: DRTs can be helpful to reconstruct nasal defects. However, if the defect involves the aesthetic subunits nasal ala or the infraorbital region, different techniques should be preferred.