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The geriatric nutritional risk index (GNRI) predicts adverse outcomes in chronic diseases, but its prognostic value for major adverse limb events (MALE) in elderly patients with peripheral artery disease (PAD) remains unverified; thus, this study aimed to establish the association between GNRI and MALE.
A multicenter, prospective study.
From January 2021 to August 2022, 1200 patients with PAD aged ≥ 60 years were enrolled. Patients were stratified by GNRI value (At-risk group: ≤ 98 vs. No-risk group: > 98). Data were analysed through Kaplan–Meier curves, multivariable Cox regression, restricted cubic spline (RCS) modelling, and subgroup analyses.
Among 1036 completers (13.7% attrition rate), 275 (26.5%) developed MALE during a mean follow-up of 18.9 ± 8.0 months. Kaplan–Meier analysis demonstrated significantly higher MALE incidence in patients in the At-risk group (log-rank p < 0.001). Adjusted Cox models revealed a 45% increased MALE risk in patients in the At-risk group (HR 1.45, 95% CI 1.12–1.86, p = 0.005). RCS identified a non-linear L-shaped relationship (p = 0.006) with inflection at GNRI = 95: Below 95, each 1-unit GNRI increase reduced MALE risk by 9% (HR 0.91, 95% CI 0.88–0.95, p < 0.001), while no significant association existed above 95. Subgroup analyses confirmed consistency across subgroups (all p-interaction > 0.05).
GNRI exhibits a non-linear L-shaped association with MALE risk in elderly patients with PAD, demonstrating critical prognostic utility below the 95 inflection point. Routine GNRI monitoring should be prioritised for patients with GNRI < 95 to guide preventive interventions.
GNRI should be incorporated as a routine risk assessment tool for elderly patients with PAD, with particular vigilance required for those with GNRI < 95. Prioritising nutritional screening and intervention in patients with GNRI < 95 may potentially improve clinical outcomes.
Patients contributed to this study by completing follow-up assessments.
This study followed the STROBE guidelines.
To examine the prevalence of factors of cognitive frailty in patients undergoing maintenance haemodialysis (MHD).
A cross-sectional study.
From September 2023 to January 2024, 1023 patients undergoing MHD were recruited from 11 hospitals in Chengdu, China, using convenience sampling. The participants' sociodemographic and lifestyle factors, health information and laboratory indicators were assessed using a general information questionnaire. Cognitive frailty was assessed using the Fried Frailty Phenotype and Montreal Cognitive Assessment Scales. Multivariate logistic regression was used to examine the associations between cognitive frailty and sociodemographic and clinical characteristics. Independent variables for the multivariate logistic regression model encompassing age, sex, educational level, marital status, visual impairment, hearing impairment, falls within a year, depression, weight, height, Malnutrition-inflammation score and serum albumin, sodium, phosphorus, total cholesterol and creatinine levels.
Among 1023 participants with a mean age of 69.52 years, 300 (29.3%) had cognitive frailty, with a predominance of older patients. Regression analysis showed that advanced age, low literacy and low serum creatinine, sodium and total cholesterol levels were positively correlated with cognitive frailty. Furthermore, 17.1% of the participants experienced depression, a risk factor for cognitive frailty, and malnutrition was an independent risk factor for cognitive frailty.
Older adult patients undergoing (MHD) are at an increased risk of developing cognitive frailty. The aetiology of cognitive frailty in this cohort was multifactorial. Targeted interventions should be designed and implemented based on these factors, prioritising nutritional guidance and mood management to prevent or reverse cognitive frailty.
The study adhered to the STROBE checklist.
Older adult patients undergoing MHD are at increased risk of developing cognitive frailty. Cognitive frailty screening must be incorporated into the routine assessment of older patients undergoing MHD.
No patient or public contribution.
To compare and analyse the differences in the clinical reasoning competence of nurses with different working years and their relationship with self-directed learning competence.
A cross-sectional survey design (online investigation) was used. A total of 376 nurses were recruited from four independent hospitals in China. Online questionnaires collected data on nurses' demographic characteristics and assessed their clinical reasoning and self-directed learning competence. Pearson correlation analysis, t-test, analysis of variance (ANOVA) and multivariate regression analysis were used.
Clinical reasoning competence scores of nurses with working years >10 years were higher than those of other nurses. Self-directed learning competence scores of nurses with working years of <1 year and (from ≥1 year to <3 years) were lower than those of nurses with working years of 6–10 years and >10 years. Self-directed learning competence scores of nurses with working years of 3–5 years were lower than those of nurses with working years of >10 years. There was a positive correlation between clinical reasoning competence, self-directed learning competence and each dimension among nurses of different working years. There are differences in the influence of different dimensions of self-directed learning competence on clinical reasoning competence among different working years.
There were differences in clinical reasoning and self-directed learning competence among nurses with different working years. Self-directed learning competence is a positive predictor of nurses' clinical reasoning competence, which applied to nurses with all working years; however, the specific effect of self-directed learning competence on clinical reasoning competence differed among nurses with different working years.
Nursing managers should pay attention to the development characteristics of clinical reasoning competence and self-directed learning competence of nurses with different working years and determine effective intervention strategies according to specific influencing factors.
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