Key result
Nine-variable clinical nomogram detects mild cognitive impairment in AF patients with ~0.91 AUC.
Why the study?
Middle-aged and elderly patients with AF face an increased risk of cognitive impairment and dementia, prompting the need to identify independent factors for mild cognitive impairment and develop a screening nomogram for early risk identification.
Can a nomogram incorporating routine clinical variables accurately screen for mild cognitive impairment in middle-aged and elderly patients with atrial fibrillation?
Cross-Sectional (n=475)
No
Can a nomogram incorporating routine clinical variables accurately screen for mild cognitive impairment in middle-aged and elderly patients with atrial fibrillation?
A newly developed nomogram incorporating nine routine clinical variables shows high accuracy for screening mild cognitive impairment in middle-aged and elderly patients with atrial fibrillation.
Warrants clinical caution regarding unrecognized MCI in cardiac patients; leaves open whether targeted screening improves cardiovascular outcomes.
Middle-aged and elderly patients with atrial fibrillation (AF) face a 1.4-fold higher risk of cognitive impairment (CI) and a 1.3-fold higher risk of dementia compared to those without AF. This study aimed to identify independent factors for mild cognitive impairment (MCI) among hospitalized AF patients and to develop a screening nomogram to facilitate the identification of high-risk individuals for early assessment and targeted non-pharmacological interventions. A cross-sectional study was conducted involving 475 patients with atrial fibrillation admitted to a tertiary care hospital in Nanning, China, between September 2024 and September 2025. Cognitive function was evaluated using the Beijing version of the Montreal Cognitive Assessment Scale. Multivariable logistic regression analysis was used to identify independent factors for MCI in these patients. Based on the results of the regression analysis, a screening nomogram was developed. The accuracy of the model was assessed using calibration curves and receiver operating characteristic analysis, and its clinical utility was evaluated through decision curve analysis. Internal validation was performed using the bootstrap method with 500 resamples. Among the 475 patients included in the study, 237 (49.89%) were diagnosed with MCI. Multivariate analysis identified nine independent factors: age, educational level, alcohol, hypertension, stroke, comorbidity count, duration of AF, malnutrition status and frailty status. The nomogram showed good discriminative ability, with an area under the curve of 0.913. Internal validation using bootstrap resampling (500 repetitions) yielded an optimism‑corrected C‑index of 0.901. The model exhibited a sensitivity of 0.806, a specificity of 0.887, and an overall accuracy of 0.846. Furthermore, the calibration curve showed acceptable agreement between predicted probabilities and observed outcomes. We developed and internally validated a nomogram incorporating nine routinely available clinical variables. This model shows potential as a screening and risk stratification tool for MCI in middle-aged and elderly AF patients, which may aid in early identification and guide targeted management pending external validation in independent study.
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Gan et al. (2026) conducted a cross-sectional in Atrial fibrillation (n=475). Nomogram screening tool was evaluated on Area under the curve (AUC) for predicting mild cognitive impairment. A nomogram incorporating nine clinical variables demonstrated good discriminative ability for screening mild cognitive impairment in middle-aged and elderly patients with atrial fibrillation, with an AUC of 0.913.
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