Evidence map›Paper›PMID 40866407›Full record

ArticleScientific reports2025

To explore the relationship between multiple glycemic markers and all-cause mortality in patients with severe atrial fibrillation based on multiple databases.

Aifeng He, Hongjing Li, Shengkai Yang, Leiming Xu, Congliang You, Suhui Li, Lulu Zhang, Chao Zhang

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Aifeng He *Binhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Hongjing Li *Xuzhou Mining Group General Hospital, Xuzhou, Jiangsu Province, China.
Shengkai Yang *Binhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Leiming XuBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Congliang YouBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Suhui LiBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Lulu ZhangBinhai County People's Hospital, Kangda College of Nanjing Medical University, Yancheng, Jiangsu Province, China.
Chao ZhangHongze District People's Hospital, Huai'an, Jiangsu Province, China. 13912057269@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Atrial fibrillation is a common cardiac arrhythmia that significantly increases the risk of morbidity and mortality in critically ill patients. This study aimed to investigate the associations between glycemic control indicators-hemoglobin A1c (HbA1c), hemoglobin glycation index (HGI), stress hyperglycemia ratio (SHR), and glycemic variability (GV)-and clinical outcomes in AF patients admitted to intensive care unit (ICU). We conducted a retrospective analysis utilizing data from the Critical Care Medicine Information Marketplace (MIMIC)-IV database and a cohort from Binhai County People's Hospital, comprising patients with AF. Key endpoints included all-cause mortality during ICU stay and at 28-day post-ICU. Statistical analyses involved Kaplan-Meier survival curves, univariable and multivariable Cox regression models, smooth fitting curve, sensitivity analysis and weighted quantile sum (WQS) modeling. Finally, a total of 952 AF patients from MIMIC-IV database and 286 patients of Binhai People's Hospital due to AF were included for external verification. The results indicated that GV was the strongest predictor of mortality, with higher levels correlating with an increased risk of death (area under curve (AUC) = 0.620 for ICU mortality; AUC = 0.607 for 28-day post-ICU mortality). HGI and SHR also demonstrated significant associations with mortality outcomes, particularly in specific subgroups. Notably, lower HGI levels were linked to increased ICU mortality risk (log-rank P = 0.006). GV was the dominant factor in both outcomes and had a higher weight for death at 28 days after ICU (62.7% for ICU mortality vs. 47.3% for 28-day post-ICU mortality). These findings suggest that glycemic control is critical for improving outcomes in critically ill AF patients, highlighting the need for targeted interventions in managing hyperglycemia and its variability. Future studies should explore the mechanisms underlying these associations and assess the impact of glycemic management strategies on patient outcomes.

Indexed as

Atrial FibrillationBlood GlucoseAgedBiomarkersCritical IllnessDatabases, FactualFemaleGlycated HemoglobinHospital MortalityHumansHyperglycemiaIntensive Care UnitsKaplan-Meier EstimateMaleMiddle AgedRetrospective StudiesBiomarkersBlood GlucoseGlycated HemoglobinAtrial fibrillationGlycemic variabilityICUMIMICMortality

Identifiers

PMID40866407
PMCPMC12391337

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