Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm
BackgroundThe number of patients with cerebral small vessel disease is increasing, especially among the elderly population. With the continuous improvement of detection techniques, the positivity rate keeps increasing. Our goal is to develop a nomogram for early identification of PSCI and PSCID in s...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1431421/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548179012222976 |
|---|---|
| author | Yanzi Huang Wendie Huang Xiaoming Ma Guoyin Zhao Jingwen Kang Huajie Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Shiying Sheng Fengjuan Qian |
| author_facet | Yanzi Huang Wendie Huang Xiaoming Ma Guoyin Zhao Jingwen Kang Huajie Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Shiying Sheng Fengjuan Qian |
| author_sort | Yanzi Huang |
| collection | DOAJ |
| description | BackgroundThe number of patients with cerebral small vessel disease is increasing, especially among the elderly population. With the continuous improvement of detection techniques, the positivity rate keeps increasing. Our goal is to develop a nomogram for early identification of PSCI and PSCID in stroke patients.MethodsIn a retrospective cohort, chained data imputation was performed to ensure no statistical differences from the original dataset. Subsequently, Boruta algorithm was utilized for variable selection based on their importance, followed by logistic regression employing backward stepwise regression. Finally, the regression results were visualized as a Nomogram.ResultsThe nomogram chart in this study achieves clinical utility in a concise and user-friendly manner, passing the Hosmer-Lemeshow goodness-of-fit test. ROC and calibration curves indicate its high discriminative ability.ConclusionWhile CSVD is prevalent among middle-aged and older individuals, cognitive decline trajectories differ. Endocrine metabolic indicators like IGF-1 offer early predictive value. This study has produced a succinct nomogram integrating demographic and clinical indicators for medical application. |
| format | Article |
| id | doaj-art-2cde16a6fda048fe81056f4665f08c72 |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-2cde16a6fda048fe81056f4665f08c722025-02-03T06:33:49ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-02-011710.3389/fnagi.2025.14314211431421Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithmYanzi Huang0Wendie Huang1Xiaoming Ma2Guoyin Zhao3Jingwen Kang4Huajie Li5Jingwei Li6Jingwei Li7Jingwei Li8Jingwei Li9Jingwei Li10Jingwei Li11Shiying Sheng12Fengjuan Qian13Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Neurology of Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, ChinaDepartment of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Neurology of Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, ChinaDepartment of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, ChinaInstitute of Brain Sciences, Nanjing University, Nanjing, ChinaJiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, ChinaJiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, ChinaNanjing Neurology Clinic Medical Center, Nanjing, ChinaDepartment of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaDepartment of Endocrinology, The Third Affiliated Hospital of Soochow University, Changzhou, ChinaBackgroundThe number of patients with cerebral small vessel disease is increasing, especially among the elderly population. With the continuous improvement of detection techniques, the positivity rate keeps increasing. Our goal is to develop a nomogram for early identification of PSCI and PSCID in stroke patients.MethodsIn a retrospective cohort, chained data imputation was performed to ensure no statistical differences from the original dataset. Subsequently, Boruta algorithm was utilized for variable selection based on their importance, followed by logistic regression employing backward stepwise regression. Finally, the regression results were visualized as a Nomogram.ResultsThe nomogram chart in this study achieves clinical utility in a concise and user-friendly manner, passing the Hosmer-Lemeshow goodness-of-fit test. ROC and calibration curves indicate its high discriminative ability.ConclusionWhile CSVD is prevalent among middle-aged and older individuals, cognitive decline trajectories differ. Endocrine metabolic indicators like IGF-1 offer early predictive value. This study has produced a succinct nomogram integrating demographic and clinical indicators for medical application.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1431421/fullMCICSVDIGF-1cognitioncognitive impairmentstroke |
| spellingShingle | Yanzi Huang Wendie Huang Xiaoming Ma Guoyin Zhao Jingwen Kang Huajie Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Jingwei Li Shiying Sheng Fengjuan Qian Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm Frontiers in Aging Neuroscience MCI CSVD IGF-1 cognition cognitive impairment stroke |
| title | Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm |
| title_full | Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm |
| title_fullStr | Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm |
| title_full_unstemmed | Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm |
| title_short | Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm |
| title_sort | nomogram for predicting mild cognitive impairment in chinese elder csvd patients based on boruta algorithm |
| topic | MCI CSVD IGF-1 cognition cognitive impairment stroke |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1431421/full |
| work_keys_str_mv | AT yanzihuang nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT wendiehuang nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT xiaomingma nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT guoyinzhao nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingwenkang nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT huajieli nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT jingweili nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT shiyingsheng nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm AT fengjuanqian nomogramforpredictingmildcognitiveimpairmentinchineseeldercsvdpatientsbasedonborutaalgorithm |