RESUMEN
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.
RESUMEN
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.
