A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease

Abstract

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.

Keywords:

• Chronic kidney disease
• non-dialysis patients
• mild cognitive impairment
• risk prediction model

Ethics statement

This study was approved by the institutional ethics committee for human research of the Chengdu medical college (CMCEC2023.NO.77). This research study was conducted in accordance with the guidelines of the Declaration of Helsinki. All patients have informed consent.

Author contributions

Qin Yang: study design, execution, data collection and analysis, manuscript writing and revision.

Yuhe Xiang: data collection and analysis, manuscript revision assistance.

Guoting Ma: conception, analysis and interpretation.

Min Cao: study design.

Yixi Fang: data collection.

Wenbin Xu and Lin Li: conception, study design.

Li Qin: Recruiting research patients.

Yu Feng: Guidance on professional knowledge of manuscript revision.

Qian Yang: conception, study design, manuscript review and revision.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Sichuan applied psychology research center (CSXL-22309), the Zigong philosophy social science primary research base sports and healthy innovation research center (YDJKY23-03), and Chengdu Medical College-Second Affiliated Hospital of Chengdu Medical College Union Fund (2022LHFSSYB-01).