Keywords
Length of Stay
Mathematical Model
Artificial intelligence
Diabetes mellitus Hipoglikemia
Length of Stay
Model Matematika
Artificial Intelligence
Diabetes Melitus
How to Cite
Abstract
Background: Diabetes Mellitus (DM), a burden disease that has contributed to the health burden globally. The most common acute complication is hypoglycemia. Hypoglycemia in hospitalized diabetic patients is associated with increased length of stay (LOS), mortality, and costs. Patients with co-morbidities are often not in accordance with the clinical pathway, so a mathematical model is needed which estimates length of stay and the required resources can be estimated.
Aim: To formulate a mathematical model predicting the LOS of hypoglycemic patients treated with various conditions
Methode: This study is a retrospective cohort study using secondary data of DM patients who experienced hypoglycemia from January 1, 2011 to December 2020 which obtained from the medical records of Zainoel Abidin Hospital Banda Aceh, Indonesia. Data collected from May to November 2021. The data were analyzed using multivariate analysis with logistic regression test. using R software to obtain a mathematical modeling of length of stay. The variables that affect the length of stay using logistic regression test with p value <0.05.
Result: There were 573 hypoglycemic patients, most of the patients who experienced hypoglycemia were male (51.3 %), the average age was 59.6 years. Most of the patients who experienced hypoglycemia were type 2 DM patients (96.4%), Infection was the main comorbid (46.6 %), Mild hypoglycemia was the highest (47.2%) and the average length of stay was 8.06 days. Based on mathematical modeling, it was found that for every additional unit of age, the length of treatment increased by 6.99 7 days. Then a logistic regression test was carried out to see the influential variables.
Conclusion: Based on the mathematical modeling obtained, it is known that for each additional unit of age, the length of stay will increase by about 6.9≈7 days. Furthermore, the formulation of this mathematical model is expected to be able to predict the length of stay for various comorbidities, predict the number of consumable materials and as a basis for making artificial intelligence applications that is beneficial to efficiency.
References
Bloomfield HE, Greer N, Newman D, et al. (2012). Predictors and Consequences of Severe Hypoglycemia in Adults with Diabetes - A Systematic Review of the Evidence. Washington, DC: Department of Veterans Affairs; Available at: http://www.hsrd.research.va.gov/publications/esp/hypoglycemia-2012.pdf.
Brodovicz KG, Mehta V, Zhang Q, et al. (2013). Association between hypoglycemia and inpatient mortality and length of hospital stay in hospitalized, insulin-treated patients. Curr Med Res Opin.; 29: 101-107.
Bonaventura, F. Montecucco, F. Dallegri. (2015). Update on strategies limiting iatrogenic hypoglycemia, Endocr Connect, 4 (3), R37–45.
Boucai, L., Southern, W. N., & Zonszein, J. (2011). Hypoglycemia-associated mortality is not drug-associated but linked to comorbidities. The American Journal of Medicine, 124, 1028–1035.
Brutsaert, E., Carey, M., & Zonszein, J. (2014). The clinical impact of inpatient hypoglycemia. Journal of Diabetes and Its Complications, 28(4), 565–572. https://doi.org/10.1016/j.jdiacomp.2014.03.002.
Daughaday WH. (2007). Hypoglycemia due to paraneoplastic secretion of insulin-like growth factor-I. J Clin Endocrinol Metab, 92(5):1616.
Draznin, B., Gilden, J., Golden, S. H., Inzucchi, S. E., Baldwin, D., Bode, B. W., et al. (2013). Pathways to quality inpatient management of hyperglycemia and diabetes: A call to action. Diabetes Care, 36, 1807–1814.
E.R. Sequest, J. Anderson, B. Childs, P. Cryer, S. Dagogo-jack, L. Fish, et al., (2013). Hypoglycemia and diabetes: a report of a workgroup of the American diabetes association and, The Endocrin Soc, 36, 1384–1395.
Garg R, Hurwitz S, Turchin A, Trivedi A. (2013). Hypoglycemia, with or without insulin therapy, is associated with increased mortality among hospitalized patients. Diabetes Care, 36, 1107-1110.
Jones GC, Timmons JG, Cunningham SG, Cleland SJ, Sainsbury CAR. (2017). Hypoglycemia and Clinical Outcomes in Hospitalized Patients With Diabetes: Does Association With Adverse Outcomes Remain When Number of Glucose Tests Performed Is Accounted For?. J Diabetes Sci Technol, 11(4), 720-723. https://doi.org/10.1177/1932296816688012.
Marshall, A., Vasilakis, C., & El-Darzi, E. (2005). Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions. Health Care Management Science, 8(3), 213–220. https://doi.org/10.1007/s10729-005-2012-z.
M. Carey, L. Boucai, J. Zonszein, (2013). Impact of hypoglycemia in hospitalized patients, Curr. Diabetes Rep., 13 (1), 107–15
P. Harper and A. Shahani. (2002). Modelling for the planning and management of bed capacities in hospitals, Journal of Operational Research Society, 53, 11–18.
Pratiwi, C., Mokoagow, M. I., Made Kshanti, I. A., & Soewondo, P. (2020). The risk factors of inpatient hypoglycemia: A systematic review. Heliyon, 6(5), e03913. https://doi.org/10.1016/j.heliyon.2020.e03913.
R. Gomez-Huelgas, R. Guijarro-Merino, A. Zapatero, R. Barba, A. Guijarro- Contreras, F. Tinahones, et al. (2015). The frequency and impact of hypoglycemia among hospitalized patients with diabetes: a population-based study, J. Diabet. Complicat., 29(8), 1050–1055.
Samya V, Shriraam V, Jasmine A, et al. (2019). Prevalence of Hypoglycemia Among Patients With Type 2 Diabetes Melitus in a Rural Health Center in South India. J Prim Care Community Health. https://doi.org/10.1177/2150132719880638
Van Herpe, T., Mesotten, D., Wouters, P. J., Herbots, J., Voets, E., Buyens, J., et al. (2013). Logic-insulin algorithm-guided versus nurse-directed blood glucose control during critical illness: The LOGIC-1 single-center, randomized, controlled clinical trial. Diabetes Care, 36, 188–194.