A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis
ObjectiveIndividuals with psoriasis are related to a significantly increased risk of cardiovascular diseases (CVDs), the major cause of death among psoriasis patients. Prompt diagnosis and intervention of CVDs can effectively retard the progression of the disease. This study developed and validated...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1584305/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850125050659733504 |
|---|---|
| author | Xiao-Yang Guo Guo-Hua Xue Yue-Min Zou Jia-Qi Chen Shi Chen Dong-Mei Zhou |
| author_facet | Xiao-Yang Guo Guo-Hua Xue Yue-Min Zou Jia-Qi Chen Shi Chen Dong-Mei Zhou |
| author_sort | Xiao-Yang Guo |
| collection | DOAJ |
| description | ObjectiveIndividuals with psoriasis are related to a significantly increased risk of cardiovascular diseases (CVDs), the major cause of death among psoriasis patients. Prompt diagnosis and intervention of CVDs can effectively retard the progression of the disease. This study developed and validated the CVDs diagnostic prediction model for psoriasis patients.MethodsMedical records from psoriasis patients admitted to Beijing Hospital of Traditional Chinese Medicine between January 2009 and September 2024 were reviewed retrospectively. Patients were randomized as training and validation sets at the 7:3 ratio. We then selected variables through univariate logistic regression and least absolute shrinkage and selection operator (LASSO). The screened factors were subsequently incorporated in a multivariate logistic regression model for establishing the diagnostic nomogram. Moreover, this constructed model was validated internally and externally, and its performance was compared with a previous model.ResultsIn this study, altogether 2,685 psoriasis patients were included. Five variables were finally selected for nomogram construction, which were age, hypertension, diabetes, dyslipidemia, and fasting blood glucose (FBG). According to our results, this model achieved favorable discrimination ability, and the area under the curve (AUC) values after 500 bootstrap resampling was 0.9355 (95% CI, 0.9219–0.9491) and 0.9118 (95% CI, 0.8899–0.9338) for training and validation sets, separately. Besides, calibration curves closely matched predicted and real values for both sets. Further, as indicated by DCA results, this model showed a high net benefit at predicted probabilities below 79% and 80% of training and validation sets, separately. In total, 188 psoriasis patients were enrolled in this work, with NHANES publicly available data being utilized for external validation. The corrected AUC was 0.8293 (95% CI, 0.7574–0.9012), and the calibration and DCA curves demonstrated good accuracy and clinical utility. Additionally, the model showed an increased AUC compared with a previously published diagnostic model. Its net reclassification index (NRI) and discrimination improvement index (IDI) were positive, showing that our model was superior to the previous model.ConclusionThis study provides a cost-effective and practical tool that can assist physicians in identifying psoriasis patients at a higher CVDs risk. This may facilitate early disease diagnosis and intervention. |
| format | Article |
| id | doaj-art-d1e48ceade4c4a4ea072994da978ea2e |
| institution | OA Journals |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-d1e48ceade4c4a4ea072994da978ea2e2025-08-20T02:34:10ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-05-011210.3389/fcvm.2025.15843051584305A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasisXiao-Yang Guo0Guo-Hua Xue1Yue-Min Zou2Jia-Qi Chen3Shi Chen4Dong-Mei Zhou5Beijing University of Chinese Medicine, Beijing, ChinaBeijing University of Chinese Medicine, Beijing, ChinaBeijing University of Chinese Medicine, Beijing, ChinaCapital Medical University, Beijing, ChinaBeijing University of Chinese Medicine, Beijing, ChinaBeijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, ChinaObjectiveIndividuals with psoriasis are related to a significantly increased risk of cardiovascular diseases (CVDs), the major cause of death among psoriasis patients. Prompt diagnosis and intervention of CVDs can effectively retard the progression of the disease. This study developed and validated the CVDs diagnostic prediction model for psoriasis patients.MethodsMedical records from psoriasis patients admitted to Beijing Hospital of Traditional Chinese Medicine between January 2009 and September 2024 were reviewed retrospectively. Patients were randomized as training and validation sets at the 7:3 ratio. We then selected variables through univariate logistic regression and least absolute shrinkage and selection operator (LASSO). The screened factors were subsequently incorporated in a multivariate logistic regression model for establishing the diagnostic nomogram. Moreover, this constructed model was validated internally and externally, and its performance was compared with a previous model.ResultsIn this study, altogether 2,685 psoriasis patients were included. Five variables were finally selected for nomogram construction, which were age, hypertension, diabetes, dyslipidemia, and fasting blood glucose (FBG). According to our results, this model achieved favorable discrimination ability, and the area under the curve (AUC) values after 500 bootstrap resampling was 0.9355 (95% CI, 0.9219–0.9491) and 0.9118 (95% CI, 0.8899–0.9338) for training and validation sets, separately. Besides, calibration curves closely matched predicted and real values for both sets. Further, as indicated by DCA results, this model showed a high net benefit at predicted probabilities below 79% and 80% of training and validation sets, separately. In total, 188 psoriasis patients were enrolled in this work, with NHANES publicly available data being utilized for external validation. The corrected AUC was 0.8293 (95% CI, 0.7574–0.9012), and the calibration and DCA curves demonstrated good accuracy and clinical utility. Additionally, the model showed an increased AUC compared with a previously published diagnostic model. Its net reclassification index (NRI) and discrimination improvement index (IDI) were positive, showing that our model was superior to the previous model.ConclusionThis study provides a cost-effective and practical tool that can assist physicians in identifying psoriasis patients at a higher CVDs risk. This may facilitate early disease diagnosis and intervention.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1584305/fullnomogrampsoriasiscardiovascular diseasesdiagnosticprediction model |
| spellingShingle | Xiao-Yang Guo Guo-Hua Xue Yue-Min Zou Jia-Qi Chen Shi Chen Dong-Mei Zhou A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis Frontiers in Cardiovascular Medicine nomogram psoriasis cardiovascular diseases diagnostic prediction model |
| title | A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis |
| title_full | A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis |
| title_fullStr | A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis |
| title_full_unstemmed | A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis |
| title_short | A diagnostic prediction model for cardiovascular diseases (CVDs) in patients with psoriasis |
| title_sort | diagnostic prediction model for cardiovascular diseases cvds in patients with psoriasis |
| topic | nomogram psoriasis cardiovascular diseases diagnostic prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1584305/full |
| work_keys_str_mv | AT xiaoyangguo adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT guohuaxue adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT yueminzou adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT jiaqichen adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT shichen adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT dongmeizhou adiagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT xiaoyangguo diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT guohuaxue diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT yueminzou diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT jiaqichen diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT shichen diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis AT dongmeizhou diagnosticpredictionmodelforcardiovasculardiseasescvdsinpatientswithpsoriasis |