Regression-based prediction of seeking diabetes-related emergency medical assistance by regular clinic patients
This study aims to examine diabetes-related emergency medical assistance (DREMA)-seeking factors among type-2 diabetes patients and predict likelihood of patients seeking DREMA before their next scheduled clinic visit. This case-control-designed study comprised a systematic random sampling of 228 patients who completed a structured interview (mean age = 62.6 years). DREMA prediction model was developed based on parameter estimates of a logistic regression model on DREMA (≥ 1 admission vs. 0 admissions), with variable selection from a forward stepwise selection process, considering all 24 potential independent variables. For the final DREMA prediction model, four variables were retained via forward stepwise logistic regression analysis: (1) age, (2) type of rice consumed, (3) physical activity outside of a regular job, and (4) leisure time exercise frequency. Likelihood of seeking DREMA increased with aging, regular or frequent consumption of white rice rather than brown or parboiled rice, and being physically inactive outside of occupation. Odds of seeking DREMA were directly associated with frequency of exercise during leisure time. With further validation and model updating based on local population characteristics, clinicians will be able to predict the DREMA-event likelihood for each clinic patient diagnosed with type-2 diabetes. Modifiable DREMA-seeking variables suggest possible interventions for preventing undesired DREMA events.
KeywordsType-2 diabetes Clinic patients Logistic regression Prediction Diabetes-related emergency medical assistance
We would like to thank Dr. Ahmed YoussefAgha and Jessie He for their contributions to the first draft of the manuscript and data analysis, respectively.
The authors have no relevant financial or non-financial relationships to disclose i.e., there was no funding or support.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval for the recruitment of human subjects and review of medical records were obtained from the Institutional Review Board, Indiana University Bloomington, USA.
The authors obtained informed consent from all individual participants included in the study.
- 6.Nita CA, Rusu A, Bala CG, Hancu N. Predictors of postprandial hyperglycemia in patients with type 2 diabetes. Acta Endocrinol. 2009;5(2):177–82.Google Scholar
- 9.National Trauma Secretariat of Sri Lanka. Emergency medical services. 2013; http://www.traumaseclanka.gov.lk /. Accessed 12 November 2013.
- 10.Adams ST, Leveson SH. Clinical prediction rules. Br Med J 2012;344.Google Scholar
- 15.Bland JM, Altman DG. Statistics notes—the odds ratio. Br Med J. 2000;320(7247):1468–8.Google Scholar
- 18.Amarasinghe DACL, Fonseka P, Dalpatadu KCS, Unwin NC, Fernando DJS. Risk factors for long-term complications in patients with type 2 diabetes attending government institutions in the Western Province of Sri Lanka: a case control study. Diabetes Res Clin Pract. 2006;75:377–8.CrossRefPubMedGoogle Scholar
- 20.Morris SK, Parkin P, Science M, et al. A retrospective cross-sectional study of risk factors and clinical spectrum of children admitted to hospital with pandemic H1N1 influenza as compared to influenza A. BMJ Open. 2012;2(2).Google Scholar
- 22.Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden index and optimal cut-point estimated from observations affected by a lower limit of detection. Biometrical J Biometrische Zeitschrift. 2008;50(3):419–30.Google Scholar
- 24.Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Boston: Allyn and Bacon; 2013.Google Scholar
- 25.Schoenbach V, Rosamond W. Understanding the fundamentals of epidemiology: an evolving text. In: Department of Epidemiology SoPH UoNCaCH. Fall 2000 ed. Chapel Hill: University of North Carolina; 2000. p. 209–20.Google Scholar
- 29.Hermundstad AM, Brown KS, Bassett DS, Carlson JM. Learning, memory, and the role of neural network architecture. Plos Computational Biology. 2011;7(6).Google Scholar