Diyabetik Hastalarda Komplikasyon Geliştirme Riski Durumlarının Lojistik Regresyon Modelleri ile Retrospektif İncelenmesi

Öz

Son yıllarda insan sağlığını tehdit eden en önemli hastalıklardan birisi diyabettir. Diyabet tanısı birçok tıbbi muayene bulgusu, tetkik ve tetkikler sonucunda konulabilmektedir. Bu tanı ile değerlendirilen hastalarda komplikasyon gelişmesi hasta için çok ciddi riskler taşımaktadır. Risk durumlarının doğru ve güvenilir bir şekilde öngörülmesi hekimlerin karar verme süreçlerine olumlu katkı sağlayacaktır. Bilgi teknolojilerindeki gelişmeler sayesinde elde edilen veriler çok daha hızlı ve güvenilir bir şekilde işlenebilmektedir. Lojistik regresyon analizi, basitlik ve doğruluk hedeflerine uygun olması açısından verileri analiz etmek için mevcut yöntemler arasından seçilmiştir. Lojistik regresyon analizi sonucunda; yaş, HbA1C ve bazı lipid parametreleri komplikasyon gelişiminde etkilidir. Ayrıca diyabet komplikasyon tipleri spesifik olarak belirlenmiş ve komplikasyonlara neden olan faktörlerin etkileri araştırılmıştır.

Anahtar Kelimeler

• Diyabet
• Lojistik Regresyon Analizi
• HbA1C
• Periferik Dolaşım Komplikasyonu

Retrospective Investigation of Complication Development Risk Conditions with Logistic Regression Models in Diabetic Patients

Öz

Diabetes is one of the most important diseases that threaten human health in recent years. The diagnosis of diabetes can be made as a result of many medical examination findings, analyzes and examinations. The development of complications in patients evaluated with this diagnosis carries very serious risks for the patient. The accurate and reliable prediction of risk situations will contribute positively to the decision making process of physicians. The data obtained with the help of the developments in information technologies can be processed much faster and reliably. The logistic regression analysis was chosen among the available methods to analyze the data in terms of being the suitable for simplicity and accuracy targets. As a result of the logistic regression analysis; age, HbA1C and some lipid parameters have an effect on complication development. In addition, diabetes complication types were determined specifically and the effects of the factors causing complications were investigated.

Anahtar Kelimeler

• Diabetes
• Logistic Regression Analyses
• HbA1C
• Peripheral Circulatory Complication

Kaynakça

1. Alonso, G. T., Coakley, A., Pyle, L., Manseau, K., Thomas, S., & Rewers, A. (2020). Diabetic ketoacidosis at diagnosis of type 1 diabetes in Colorado children, 2010–2017. Diabetes Care, 43(1), 117-121.
2. Alpar R (2013). Applied Multivariate Statistical Methods (Fourth Edition). Detay Publishing, 637-659, Ankara
3. American Diabetes Association (2004). Diagnosis and Classification of Diabetes Mellitus Diabetes Care, 31(1): 55-60.
4. Bekiari, E., Kitsios, K., Thabit, H., Tauschmann, M., Athanasiadou, E., Karagiannis, T., ... & Tsapas, A. (2018). Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. bmj, 361.
5. Bland, J. M., & Altman, D. G. (2000). The odds ratio. Bmj, 320(7247), 1468.
6. Bonney, G. E. (1987). Logistic regression for dependent binary observations. Biometrics, 951-973.
7. Davies, M. J., D’Alessio, D. A., Fradkin, J., Kernan, W. N., Mathieu, C., Mingrone, G., ... & Buse, J. B. (2018). Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes care, 41(12), 2669-2701.
8. Dawson, B., & Trapp, R. G. (2004). Basic & clinical biostatistics. In Basic & clinical biostatistics (pp. 438-438).

9. Ebrahim, O. A., & Derbew, G. (2023). Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021. Scientific reports, 13(1), 7779.
10. ElSayed, N. A., Aleppo, G., Aroda, V. R., Bannuru, R. R., Brown, F. M., Bruemmer, D., ... & American Diabetes Association. (2023). 2. Classification and diagnosis of diabetes: standards of care in diabetes—2023. Diabetes care, 46(Supplement_1), S19-S40.
11. Federation, I. D. (2019). IDF diabetes atlas, 9th edn. Brussels. Belgium2015 [Available from: http://www. diabetesatlas. org.
12. Gujarati D N (1999). Basic Econometrics (Trans. U. Senesen & G. Senesen), Literatur Publishing, Istanbul
13. Hebel, J. R., & McCarter, R. (2011). Study guide to epidemiology and biostatistics. Jones & Bartlett Publishers.
14. Hosmer, D. W. (1989). Assessing the fit of the model. Applied logistic regression.
15. International Diabetes Federation. IDF Diabetes Atlas, 4th edn. Brussels, Belgium: International Diabetes Federation, 2009.
16. Jensen, E. T., Stafford, J. M., Saydah, S., D’Agostino, R. B., Dolan, L. M., Lawrence, J. M., ... & Dabelea, D. (2021). Increase in prevalence of diabetic ketoacidosis at diagnosis among youth with type 1 diabetes: the SEARCH for Diabetes in Youth Study. Diabetes Care, 44(7), 1573-1578.
17. Kim, D., Alshuwaykh, O., Dennis, B. B., Cholankeril, G., Knowles, J. W., & Ahmed, A. (2023). Chronic liver disease-related mortality in diabetes before and during the COVID-19 in the United States. Digestive and Liver Disease, 55(1), 3-10.
18. Liu, N., Wang, G., Liu, C., Liu, J., Huang, S., Zhou, Y., & Xiao, E. (2023). Non‐alcoholic fatty liver disease and complications in type 1 and type 2 diabetes: a Mendelian randomization study. Diabetes, Obesity and Metabolism, 25(2), 365-376.
19. Menard, S. (2002). Applied logistic regression analysis (Vol. 106). Sage.
20. Mertler, C. A., Vannatta, R. A., & LaVenia, K. N. (2021). Advanced and multivariate statistical methods: Practical application and interpretation. Routledge.
21. Ozcan S (2002), Diabetes Nursing Association Book, Chapter 13, 145, Istanbul.
22. Park, S. Y., Rhee, S. Y., Chon, S., Ahn, K. J., Kim, S. H., Baik, S. H., ... & KNDP study investigators. (2017). Effects of foot complications in patients with Type 2 diabetes mellitus on public healthcare: An analysis based on the Korea National Diabetes Program Cohort. Journal of Diabetes and its Complications, 31(2), 375-380.
23. Rewers, A., Dong, F., Slover, R. H., Klingensmith, G. J., & Rewers, M. (2015). Incidence of diabetic ketoacidosis at diagnosis of type 1 diabetes in Colorado youth, 1998-2012. Jama, 313(15), 1570-1572.
24. Saracbasi O, Dolgun A. (2015) Logistic regression analysis. Hacettepe University Publications
25. Selim, S. (2017). Frequency and pattern of chronic complications of diabetes and their association with glycemic control. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 11, S311-S314.
26. Walker, S. H., & Duncan, D. B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika, 54(1-2), 167-179.
27. Zhang, B. (1999). A chi-squared goodness-of-fit test for logistic regression models based on case-control data. Biometrika, 86(3), 531-539.