Academic Journal
Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach.
| Τίτλος: | Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach. |
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| Συγγραφείς: | Zhan, Jinshan, Chen, Fangqi, Li, Yanqiu, Huang, Changzheng |
| Πηγή: | Clinical Rheumatology; Jan2025, Vol. 44 Issue 1, p277-289, 13p |
| Θεματικοί όροι: | Machine learning, Psoriatic arthritis, National Health & Nutrition Examination Survey, Chronic bronchitis, Receiver operating characteristic curves |
| Περίληψη: | 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. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms. [ABSTRACT FROM AUTHOR] |
| Copyright of Clinical Rheumatology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s10067-024-07244-4 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhan%2C+Jinshan%22">Zhan, Jinshan</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Fangqi%22">Chen, Fangqi</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yanqiu%22">Li, Yanqiu</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Changzheng%22">Huang, Changzheng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Clinical Rheumatology; Jan2025, Vol. 44 Issue 1, p277-289, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Psoriatic+arthritis%22">Psoriatic arthritis</searchLink><br /><searchLink fieldCode="DE" term="%22National+Health+%26+Nutrition+Examination+Survey%22">National Health & Nutrition Examination Survey</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+bronchitis%22">Chronic bronchitis</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Clinical Rheumatology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10067-024-07244-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 277 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Psoriatic arthritis Type: general – SubjectFull: National Health & Nutrition Examination Survey Type: general – SubjectFull: Chronic bronchitis Type: general – SubjectFull: Receiver operating characteristic curves Type: general Titles: – TitleFull: Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhan, Jinshan – PersonEntity: Name: NameFull: Chen, Fangqi – PersonEntity: Name: NameFull: Li, Yanqiu – PersonEntity: Name: NameFull: Huang, Changzheng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07703198 Numbering: – Type: volume Value: 44 – Type: issue Value: 1 Titles: – TitleFull: Clinical Rheumatology Type: main |
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