Development and internal validation of a diagnostic prediction model for psoriasis severity
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Development and internal validation of a diagnostic prediction model for psoriasis severity. / Liljendahl, Mie Sylow; Loft, Nikolai; Egeberg, Alexander; Skov, Lone; Nguyen, Tri-Long.
In: Diagnostic and Prognostic Research, Vol. 7, No. 1, 2, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Development and internal validation of a diagnostic prediction model for psoriasis severity
AU - Liljendahl, Mie Sylow
AU - Loft, Nikolai
AU - Egeberg, Alexander
AU - Skov, Lone
AU - Nguyen, Tri-Long
N1 - © 2023. The Author(s).
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1186/s41512-023-00141-5
DO - 10.1186/s41512-023-00141-5
M3 - Journal article
C2 - 36747306
VL - 7
JO - Diagnostic and Prognostic Research
JF - Diagnostic and Prognostic Research
SN - 2397-7523
IS - 1
M1 - 2
ER -
ID: 336934931