Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach
Clinical Rheumatology, ISSN: 1434-9949, Vol: 44, Issue: 1, Page: 277-289
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
Objective: To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA). Results: The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737–0.826) for the training set and 0.780 (95% CI, 0.712–0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611–0.757) and 0.653 (95% CI, 0.535–0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets. Conclusion: The Borutamodel represents a promising tool for early risk assessment of PsA. (Table presented.)
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85210149974&origin=inward; http://dx.doi.org/10.1007/s10067-024-07244-4; http://www.ncbi.nlm.nih.gov/pubmed/39585569; https://link.springer.com/10.1007/s10067-024-07244-4; https://dx.doi.org/10.1007/s10067-024-07244-4; https://link.springer.com/article/10.1007/s10067-024-07244-4
Springer Science and Business Media LLC
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