Development and internal validation of a diagnostic prediction model for psoriasis severity.
Diagn Progn Res
; 7(1): 2, 2023 Feb 07.
Article
en En
| MEDLINE
| ID: mdl-36747306
ABSTRACT
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.
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1
Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Diagn Progn Res
Año:
2023
Tipo del documento:
Article
País de afiliación:
Dinamarca