Cognitive Impairment in Patients on Maintenance Hemodialysis and Its Influencing Factors: a Multicenter Cross-sectional Study
Bibliographic record
Abstract
Background Understanding the condition and influencing factors of cognitive impairment in maintenance hemodialysis (MHD) patients could signficantly enhance their quality of life while alleviating the burden on their families and society. Objective TO investigate the status of cognitive impairment in MHD patients and explore the possible influencing factors. Methods Using convenience sampling, we selected MHD patients from three hemodialysis centers (including the Department of Nephrology at the First Affiliated Hospital of Shihezi University, the Department of Nephrology at Shihezi People's Hospital, and the Langshen Hemodialysis Center) in Shihezi City between April 2023 and April 2024. We collected data on demographic characteristics, cognitive impairment levels, sleep quality, independent living abilities, serum levels of α-Klotho, β-Klotho, FGF-23, and other common laboratory indicators. Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA), sleep quality was evaluated with the Athens Insomnia Scale (AIS-8), and independent living ability was assessed using the Functional Activities Questionnaire (FAQ). Serum levels of α-Klotho, β-Klotho, and FGF-23 were measured by the ELISA method. Univariate and multivariate Logistic regression analyses were performed to identify influencing factors, which were validated for their predictive value on cognitive impairment using the receiver operating characteristic (ROC) curve. A nomogram was subsequently plotted. Results A total of 276 MHD patients were surveyed, revealing a cognitive impairment incidence rate of 76.4% (211/276). Among these, 145 patients had mild cognitive impairment and 66 patients had moderate cognitive impairment. Nearly half of the patients exhibited suspected insomnia (21.4%) or confirmed insomnia (25.4%). Among the patients studied, 14.9% (41 out of 276) lacked the ability to live independently. The multivariate Logistic regression analysis indicated that age (OR=1.038, 95%CI=1.004-1.072) and sleep disorders (OR=1.179, 95%CI=1.051-1.322) were risk factors for cognitive impairment in MHD patients (P<0.05). High serum α-Klotho levels (OR=0.996, 95%CI=0.994-0.998), high serum β-Klotho levels (OR=0.750, 95%CI=0.661-0.852), and higher years of education (OR=0.800, 95%CI=0.699-0.915) were protective factors (P<0.05). The area under the ROC curve (AUC) showed that age (AUC=0.732, 95%CI=0.667-0.797), sleep disorder (AUC=0.710, 95%CI=0.638-0.783), α-Klotho (AUC=0.774, 95%CI=0.709-0.839), β-Klotho (AUC=0.741, 95%CI=0.663-0.819) and years of education (AUC=0.718, 95%CI=0.647-0.789) had predictive value for cognitive impairment in MHD patients. The combination of age, sleep disorder, serum α-Klotho, serum β-Klotho and years of education (P=-0.004×α-Klotho-0.287×β-Klotho+0.370×age-0.223×years of education +0.165×AIS-8 score+6.658) predicted the occurrence of MHD. The AUC of cognitive impairment was 0.894 (95%CI=0.851-0.937, P<0.001), the sensitivity was 82.9%, and the specificity was 78.5%. Conclusion The prevalence of cognitive impairment among MHD patients is substantially high, estimated at approximately 76%. Age, sleep disorders, years of education, and levels of α-Klotho and β-Klotho are important influencing factors. Medical staff and patients' families should raise awareness of cognitive impairment, actively screen and intervene in key patients to improve their quality of life and reduce the burden on their families and society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".