NDLI: Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning

Content Provider	Springer Nature : BioMed Central
Author	Chen, Yang Yang, Zhengkun Liu, Yang Gue, Ying Zhong, Ziyi Chen, Tao Wang, Feifan McDowell, Garry Huang, Bi Lip, Gregory Y. H.
Abstract	Background The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourable prognosis, and abnormal GV is prevalent in ICUs. However, the impact of GV on the prognosis of AF patients in the ICU remains uncertain. This study aimed to evaluate the relationship between GV and all-cause mortality after ICU admission at short-, medium-, and long-term intervals in AF patients. Methods Data was obtained from the Medical Information Mart for Intensive Care IV 3.0 database, with admissions (2008–2019) as primary analysis cohort and admissions (2020–2022) as external validation cohort. Multivariate Cox proportional hazards models, and restricted cubic spline analyses were used to assess the associations between GV and mortality outcomes. Subsequently, GV and other clinical features were used to construct machine learning (ML) prediction models for 30-day all-cause mortality after ICU admission. Results The primary analysis cohort included 8989 AF patients (age 76.5 [67.7–84.3] years; 57.8% male), while the external validation cohort included 837 AF patients (age 72.9 [65.3–80.2] years; 67.4% male). Multivariate Cox proportional hazards models revealed that higher GV quartiles were associated with higher risk of 30-day (Q3: HR 1.19, 95%CI 1.04–1.37; Q4: HR 1.33, 95%CI 1.16–1.52), 90-day (Q3: HR 1.25, 95%CI 1.11–1.40; Q4: HR 1.34, 95%CI 1.29–1.50), and 360-day (Q3: HR 1.21, 95%CI 1.09–1.33; Q4: HR 1.33, 95%CI 1.20–1.47) all-cause mortality, compared with lowest GV quartile. Moreover, our data suggests that GV needs to be contained within 20.0%. Among all ML models, light gradient boosting machine had the best performance (internal validation: AUC [0.780], G-mean [0.551], F1-score [0.533]; external validation: AUC [0.788], G-mean [0.578], F1-score [0.568]). Conclusion GV is a significant predictor of ICU short-term, mid-term, and long-term all-cause mortality in patients with AF (the potential risk stratification threshold is 20.0%). ML models incorporating GV demonstrated high efficiency in predicting short-term mortality and GV was ranked anterior in importance. These findings underscore the potential of GV as a valuable biomarker in guiding clinical decisions and improving patient outcomes in this high-risk population.
Related Links	https://cardiab.biomedcentral.com/counter/pdf/10.1186/s12933-024-02521-7.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14752840
DOI	10.1186/s12933-024-02521-7
Journal	Cardiovascular Diabetology
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2024-11-26
Access Restriction	Open
Subject Keyword	Diabetes Angiology Cardiology Glycaemic variability Atrial fibrillation Intensive care unit Mortality Machine learning
Content Type	Text
Resource Type	Article
Subject	Cardiology and Cardiovascular Medicine Internal Medicine Endocrinology, Diabetes and Metabolism
Journal Impact Factor	8.5/2023
5-Year Journal Impact Factor	8.9/2023

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