Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis
Abstract Peripheral arterial disease (PAD) affects approximately 236.62 million individuals globally, exposing them to significantly increased risks of major limb events such as death and amputation. Concurrently, the number of diagnostic prediction models for PAD patients is steadily rising; howeve...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10459-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332658436308992 |
|---|---|
| author | Xiaoyan Quan Huarong Xiong Xiaoyu Liu Pan Song Dan Wang Qin Chen Xiaoli Hu Meihong Shi |
| author_facet | Xiaoyan Quan Huarong Xiong Xiaoyu Liu Pan Song Dan Wang Qin Chen Xiaoli Hu Meihong Shi |
| author_sort | Xiaoyan Quan |
| collection | DOAJ |
| description | Abstract Peripheral arterial disease (PAD) affects approximately 236.62 million individuals globally, exposing them to significantly increased risks of major limb events such as death and amputation. Concurrently, the number of diagnostic prediction models for PAD patients is steadily rising; however, these studies exhibit varying results, and their quality and applicability in clinical practice and future research remain unclear. To systematically assess the methodological quality of studies on PAD diagnostic prediction models. PubMed, Embase, Web of Science and Cochrane Database of Systematic Reviews were searched to identify studies which aiming to develop or validate a diagnostic prediction model of PAD. The retrieval time limit is from the establishment of the database to June 1, 2025. Two researchers independently screened and extracted data from eligible studies and evaluated the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 24 studies on PAD diagnostic prediction models were included, most of which exhibited high risk of bias, predominantly in the domains of study population and statistical analysis. The meta-analyzed Area Under the Receiver Operating Characteristic Curve (AUC) was 0.79 [0.74, 0.84], indicating favorable model performance. The reported number of predictor variables ranged from 2 to 20, with common predictors including age, gender, hypertension, diabetes, smoking, and BMI. This study demonstrates that PAD diagnostic prediction models exhibit good predictive performance, albeit accompanied by a high risk of bias and substantial heterogeneity across studies. Future research on modeling should emphasize comprehensive methodological enhancements in model design, construction, evaluation, and validation, with full disclosure of crucial model information. It should also utilize network computing for presenting model outcomes and conduct large-scale, multi-center external validation of existing models to promote their clinical application. Trial registration: This study protocol has been registered with PROSPERO (registration number: CRD42024557144). |
| format | Article |
| id | doaj-art-052dddd5e85a46d5810f4cd21d4eae44 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-052dddd5e85a46d5810f4cd21d4eae442025-08-20T03:46:08ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-10459-3Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysisXiaoyan Quan0Huarong Xiong1Xiaoyu Liu2Pan Song3Dan Wang4Qin Chen5Xiaoli Hu6Meihong Shi7Department of Nursing, The Affiliated Hospital, Southwest Medical UniversityDepartment of Endocrinology, Affiliated Hospital of Southwest Medical UniversityNursing School, Southwest Medical UniversityDepartment of Nursing, The Affiliated Hospital, Southwest Medical UniversityDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical UniversityNursing School, Southwest Medical UniversityNursing School, Southwest Medical UniversityDepartment of Nursing, The Affiliated Hospital, Southwest Medical UniversityAbstract Peripheral arterial disease (PAD) affects approximately 236.62 million individuals globally, exposing them to significantly increased risks of major limb events such as death and amputation. Concurrently, the number of diagnostic prediction models for PAD patients is steadily rising; however, these studies exhibit varying results, and their quality and applicability in clinical practice and future research remain unclear. To systematically assess the methodological quality of studies on PAD diagnostic prediction models. PubMed, Embase, Web of Science and Cochrane Database of Systematic Reviews were searched to identify studies which aiming to develop or validate a diagnostic prediction model of PAD. The retrieval time limit is from the establishment of the database to June 1, 2025. Two researchers independently screened and extracted data from eligible studies and evaluated the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 24 studies on PAD diagnostic prediction models were included, most of which exhibited high risk of bias, predominantly in the domains of study population and statistical analysis. The meta-analyzed Area Under the Receiver Operating Characteristic Curve (AUC) was 0.79 [0.74, 0.84], indicating favorable model performance. The reported number of predictor variables ranged from 2 to 20, with common predictors including age, gender, hypertension, diabetes, smoking, and BMI. This study demonstrates that PAD diagnostic prediction models exhibit good predictive performance, albeit accompanied by a high risk of bias and substantial heterogeneity across studies. Future research on modeling should emphasize comprehensive methodological enhancements in model design, construction, evaluation, and validation, with full disclosure of crucial model information. It should also utilize network computing for presenting model outcomes and conduct large-scale, multi-center external validation of existing models to promote their clinical application. Trial registration: This study protocol has been registered with PROSPERO (registration number: CRD42024557144).https://doi.org/10.1038/s41598-025-10459-3Peripheral arterial diseaseDiagnosisPrediction modelSystematic reviewMeta-analysis |
| spellingShingle | Xiaoyan Quan Huarong Xiong Xiaoyu Liu Pan Song Dan Wang Qin Chen Xiaoli Hu Meihong Shi Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis Scientific Reports Peripheral arterial disease Diagnosis Prediction model Systematic review Meta-analysis |
| title | Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis |
| title_full | Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis |
| title_fullStr | Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis |
| title_full_unstemmed | Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis |
| title_short | Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis |
| title_sort | diagnosis models to predict peripheral arterial disease a systematic review and meta analysis |
| topic | Peripheral arterial disease Diagnosis Prediction model Systematic review Meta-analysis |
| url | https://doi.org/10.1038/s41598-025-10459-3 |
| work_keys_str_mv | AT xiaoyanquan diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT huarongxiong diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT xiaoyuliu diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT pansong diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT danwang diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT qinchen diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT xiaolihu diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis AT meihongshi diagnosismodelstopredictperipheralarterialdiseaseasystematicreviewandmetaanalysis |