Negative impact of comorbidities on all-cause mortality of patients with psoriasis is partially alleviated by biologic treatment: A real-world case-control study.

BACKGROUND: Cardiovascular comorbidities are believed to cause higher mortality in psoriasis patients. Conversely, systemic therapy may improve overall survival. OBJECTIVE: To evaluate the impact of different comorbidities and therapy on mortality risk of psoriasis patients in the entire population of Alberta, Canada (population 4.37 million). METHODS: Cohorts of psoriasis cases (n = 18,618) and controls (ambulatory patients matched 1:3 by age and sex) were retrieved from Alberta Health Services Data Repository of Reporting database within the period 2012 to 2019. Cases were stratified according to Charlson Comorbidity Index, and the type of therapy. RESULTS: Mortality in psoriasis cohort was significantly higher than in the controls (median age of death 72.0 years vs 74.4 years, respectively). Charlson Comorbidity Index and comorbidities were strong predictors of mortality, in particular drug induced liver injury (hazard ratio 1.8, affective bipolar disease, hazard ratio 1.6, and major cardiovascular diseases. Mortality was lower in patients treated with biologics (hazard ratio 0.54). LIMITATIONS: Some factors (psoriasis type and severity, response to treatment, smoking, alcohol intake) could not be measured. CONCLUSIONS: Hepatic injury, psychiatric affective disorders and cardiovascular disease were major determinants of overall survival in psoriasis. Biologic therapy was associated with a reduced mortality risk.

Review of Machine Learning in Predicting Dermatological Outcomes.

Artificial intelligence is a broad branch of computer science that has garnered significant interest in the field of medicine because of its problem solving, decision making and pattern recognition abilities. Machine learning, a subset of artificial intelligence, hones in on the ability of computers to receive data and learn for themselves, manipulating algorithms as they organize the information they are processing. Dermatology is at a particular advantage in the implementation of machine learning due to the availability of large clinical image databases that can be used for machine training and interpretation. While numerous studies have implemented machine learning in the diagnostic aspect of dermatology, less research has been conducted on the use of machine learning in predicting long-term outcomes in skin disease, with only a few studies published to date. Such an approach would assist physicians in selecting the best treatment methods, save patients' time, reduce treatment costs and improve the quality of treatment overall by reducing the amount of trial-and-error in the treatment process. In this review, we aim to provide a brief and relevant introduction to basic artificial intelligence processes, and to consolidate and examine the published literature on the use of machine learning in predicting clinical outcomes in dermatology.