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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:46 |
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| Enthalten in: |
Renal failure - 46(2024), 1 vom: 28. März, Seite 2317450 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yang, Qin [VerfasserIn] |
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| Links: |
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| Themen: |
Chronic kidney disease |
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| Anmerkungen: |
Date Completed 01.03.2024 Date Revised 03.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/0886022X.2024.2317450 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369095138 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Chronic kidney disease | |
| 650 | 4 | |a mild cognitive impairment | |
| 650 | 4 | |a non-dialysis patients | |
| 650 | 4 | |a risk prediction model | |
| 700 | 1 | |a Xiang, Yuhe |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Guoting |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Yixi |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Wenbin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qin |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Qian |e verfasserin |4 aut | |
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