A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease
Background The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life.Objective This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to c...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Renal Failure |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2317450 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591112702787584 |
|---|---|
| author | Qin Yang Yuhe Xiang Guoting Ma Min Cao Yixi Fang Wenbin Xu Lin Li Qin Li Yu Feng Qian Yang |
| author_facet | Qin Yang Yuhe Xiang Guoting Ma Min Cao Yixi Fang Wenbin Xu Lin Li Qin Li Yu Feng Qian Yang |
| author_sort | Qin Yang |
| collection | DOAJ |
| description | Background The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life.Objective This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model.Methods 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure.Results Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model’s area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927).Conclusion The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies. |
| format | Article |
| id | doaj-art-8d8fd6db2f224e78b8c200b16ed6557f |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-8d8fd6db2f224e78b8c200b16ed6557f2025-01-23T04:17:49ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2317450A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney diseaseQin Yang0Yuhe Xiang1Guoting Ma2Min Cao3Yixi Fang4Wenbin Xu5Lin Li6Qin Li7Yu Feng8Qian Yang9School of Nursing, Chengdu Medical College, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaHealth Management Center, Sichuan Tai Kang Hospital, Chengdu, ChinaDepartment of Orthopedics, Sichuan Second Traditional Chinese Medicine Hospital, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaDepartment of Nephrology, First Affiliated Hospital of Chengdu Medical College, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaBackground The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life.Objective This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model.Methods 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure.Results Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model’s area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer–Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927).Conclusion The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2317450Chronic kidney diseasenon-dialysis patientsmild cognitive impairmentrisk prediction model |
| spellingShingle | Qin Yang Yuhe Xiang Guoting Ma Min Cao Yixi Fang Wenbin Xu Lin Li Qin Li Yu Feng Qian Yang A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease Renal Failure Chronic kidney disease non-dialysis patients mild cognitive impairment risk prediction model |
| title | A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease |
| title_full | A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease |
| title_fullStr | A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease |
| title_full_unstemmed | A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease |
| title_short | A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease |
| title_sort | nomogram prediction model for mild cognitive impairment in non dialysis outpatient patients with chronic kidney disease |
| topic | Chronic kidney disease non-dialysis patients mild cognitive impairment risk prediction model |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2317450 |
| work_keys_str_mv | AT qinyang anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yuhexiang anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT guotingma anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT mincao anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yixifang anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT wenbinxu anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT linli anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT qinli anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yufeng anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT qianyang anomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT qinyang nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yuhexiang nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT guotingma nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT mincao nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yixifang nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT wenbinxu nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT linli nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT qinli nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT yufeng nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease AT qianyang nomogrampredictionmodelformildcognitiveimpairmentinnondialysisoutpatientpatientswithchronickidneydisease |