Development and internal validation of a diagnostic prediction model for psoriasis severity

BACKGROUND: While administrative health records such as national registries may be useful data sources to study the epidemiology of psoriasis, they do not generally contain information on disease severity. OBJECTIVES: To develop a diagnostic model to distinguish psoriasis severity based on administrative register data. METHOD: We conducted a retrospective registry-based cohort study using the Danish Skin Cohort linked with the Danish national registries. We developed a diagnostic model using a gradient boosting machine learning technique to predict moderate-to-severe psoriasis. We performed an internal validation of the model by bootstrapping to account for any optimism. RESULTS: Among 4016 adult psoriasis patients (55.8% women, mean age 59 years) included in this study, 1212 (30.2%) patients were identified as having moderate-to-severe psoriasis. The diagnostic prediction model yielded a bootstrap-corrected discrimination performance: c-statistic equal to 0.73 [95% CI: 0.71–0.74]. The internal validation by bootstrap correction showed no substantial optimism in the results with a c-statistic of 0.72 [95% CI: 0.70–0.74]. A bootstrap-corrected slope of 1.10 [95% CI: 1.07–1.13] indicated a slight under-fitting. CONCLUSION: Based on register data, we developed a gradient boosting diagnostic model returning acceptable prediction of patients with moderate-to-severe psoriasis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-023-00141-5.