NDLI: Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients

Content Provider	Springer Nature : BioMed Central
Author	Greco, Salvatore Salatiello, Alessandro De Motoli, Francesco Giovine, Antonio Veronese, Martina Cupido, Maria Grazia Pedarzani, Emma Valpiani, Giorgia Passaro, Angelina
Abstract	Background Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. Methods In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. Results Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. Conclusions Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.
Related Links	https://cardiab.biomedcentral.com/counter/pdf/10.1186/s12933-024-02245-8.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
ISSN	14752840
DOI	10.1186/s12933-024-02245-8
Journal	Cardiovascular Diabetology
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-03
Access Restriction	Open
Subject Keyword	Diabetes Angiology Cardiology Type 2 diabetes mellitus Glycemic variability Glucose metabolism disorder AdaBoost-FAS 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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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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