Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning
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| Názov: | Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning |
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| Autori: | Chen, Yang, Yang, Zhengkun, Liu, Yang, Gue, Ying, Zhong, Ziyi, Chen, Tao, Wang, Feifan, McDowell, Garry, Huang, Bi, Lip, Gregory Y. H. |
| Zdroj: | Cardiovasc Diabetol Cardiovascular Diabetology, Vol 23, Iss 1, Pp 1-15 (2024) Chen, Y, Yang, Z, Liu, Y, Gue, Y, Zhong, Z, Chen, T, Wang, F, McDowell, G, Huang, B & Lip, G Y H 2024, 'Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning', Cardiovascular Diabetology, vol. 23, no. 1, 426. https://doi.org/10.1186/s12933-024-02521-7 |
| Informácie o vydavateľovi: | Springer Science and Business Media LLC, 2024. |
| Rok vydania: | 2024 |
| Predmety: | Male, Blood Glucose, Time Factors, Databases, Factual, Critical Illness, Glycemic Control, Risk Assessment, Glycaemic variability, Decision Support Techniques, Machine Learning, 03 medical and health sciences, Atrial Fibrillation/mortality, 0302 clinical medicine, Risk Factors, Predictive Value of Tests, Cause of Death, Machine learning, Atrial Fibrillation, Diseases of the circulatory (Cardiovascular) system, Humans, Intensive care unit, Hospital Mortality, Mortality, Retrospective Studies, Aged, Aged, 80 and over, Blood Glucose/metabolism, Research, Reproducibility of Results, Middle Aged, Prognosis, Atrial fibrillation, Critical Illness/mortality, Intensive Care Units, Glycemic Control/mortality, RC666-701, Female, Biomarkers/blood, Biomarkers |
| Popis: | 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. |
| Druh dokumentu: | Article Other literature type |
| Popis súboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 1475-2840 |
| DOI: | 10.1186/s12933-024-02521-7 |
| Prístupová URL adresa: | https://pubmed.ncbi.nlm.nih.gov/39593120 https://doaj.org/article/d6860eafc67b4ff5b19b551c13cf9b20 https://vbn.aau.dk/da/publications/0804227f-b572-431c-aba1-63e7df2f4a8a https://vbn.aau.dk/ws/files/756088857/Chen_et_al._2024_._Prognostic_value_of_glycaemic_variability_for_mortality_in_critically_ill_atrial_fibrillation_patients_and_mortality_prediction_model_using_machine_learning.pdf https://doi.org/10.1186/s12933-024-02521-7 http://www.scopus.com/inward/record.url?scp=85210241694&partnerID=8YFLogxK https://cardiab.biomedcentral.com/articles/10.1186/s12933-024-02521-7 https://doi.org/10.1186/s12933-024-02521-7 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....89895a117326fc3e754c683d0479c99b |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 14752840 |
| DOI: | 10.1186/s12933-024-02521-7 |