Regression-based prediction of seeking diabetes-related emergency medical assistance by regular clinic patients

  • Wasantha P. Jayawardene
  • Dayani C. Nilwala
  • Godfred O. Antwi
  • David K. Lohrmann
  • Mohammad R. Torabi
  • Stephanie L. Dickinson
Original Article
  • 36 Downloads

Abstract

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.

Keywords

Type-2 diabetes Clinic patients Logistic regression Prediction Diabetes-related emergency medical assistance 

Notes

Acknowledgements

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Ethical approval for the recruitment of human subjects and review of medical records were obtained from the Institutional Review Board, Indiana University Bloomington, USA.

Informed consent

The authors obtained informed consent from all individual participants included in the study.

References

  1. 1.
    Fox CS, Coady S, Sorlie PD, et al. Trends in cardiovascular complications of diabetes. JAMA-J Am Med Assoc. 2004;292(20):2495–9.CrossRefGoogle Scholar
  2. 2.
    Ritz E, Orth SR. Nephropathy in patients with type 2 diabetes mellitus. N Engl J Med. 1999;341(15):1127–33.CrossRefPubMedGoogle Scholar
  3. 3.
    Sumner CJ, Sheth S, Griffin JW, Cornblath DR, Polydefkis M. The spectrum of neuropathy in diabetes and impaired glucose tolerance. Neurology. 2003;60(1):108–11.CrossRefPubMedGoogle Scholar
  4. 4.
    Ramsey SD, Sandhu N, Newton K, et al. Incidence, outcomes, and cost of foot ulcers in patients with diabetes. Diabetes Care. 1999;22(3):382–7.CrossRefPubMedGoogle Scholar
  5. 5.
    Nyenwe EA, Kitabchi AE. Evidence-based management of hyperglycemic emergencies in diabetes mellitus. Diabetes Res Clin Pract. 2011;94(3):340–51.CrossRefPubMedGoogle Scholar
  6. 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
  7. 7.
    Di Cianni G, Goretti C, Onetto F, et al. Emergency hospitalizations for severe hypoglycaemia in patients with type 2 diabetes. Acta Diabetol. 2013;50(3):463–4.CrossRefPubMedGoogle Scholar
  8. 8.
    Begum N, Donald M, Ozolins IZ, Dower J. Hospital admissions, emergency department utilisation and patient activation for self-management among people with diabetes. Diabetes Res Clin Pract. 2011;93(2):260–7.CrossRefPubMedGoogle Scholar
  9. 9.
    National Trauma Secretariat of Sri Lanka. Emergency medical services. 2013; http://www.traumaseclanka.gov.lk /. Accessed 12 November 2013.
  10. 10.
    Adams ST, Leveson SH. Clinical prediction rules. Br Med J 2012;344.Google Scholar
  11. 11.
    Liao L, Mark DB. Clinical prediction models: are we building better mousetraps? J Am Coll Cardiol. 2003;42(5):851–3.CrossRefPubMedGoogle Scholar
  12. 12.
    Gandara E, Wells PS. Diagnosis: use of clinical probability algorithms. Clinics in Chest Medicine. 2010;31(4):629.CrossRefPubMedGoogle Scholar
  13. 13.
    Marchese MC. Clinical versus actuarial prediction—a review of the literature. Percept Mot Skills. 1992;75(2):583–94.PubMedGoogle Scholar
  14. 14.
    Grobman WA, Stamilio DM. Methods of clinical prediction. Am J Obstet Gynecol. 2006;194(3):888–94.CrossRefPubMedGoogle Scholar
  15. 15.
    Bland JM, Altman DG. Statistics notes—the odds ratio. Br Med J. 2000;320(7247):1468–8.Google Scholar
  16. 16.
    Reed M, Huang J, Brand R, et al. Implementation of an outpatient electronic health record and emergency department visits, hospitalizations, and office visits among patients with diabetes. JAMA-Journal of the American Medical Association. 2013;310(10):1060–5.CrossRefGoogle Scholar
  17. 17.
    American Diabetes Association. Standards of medical care in diabetes—2011. Diabetes Care. 2011;34(Supplement 1):S11–61.CrossRefPubMedCentralGoogle Scholar
  18. 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
  19. 19.
    Shrivastava S, Shrivastava P, Ramasamy J. Role of self-care in management of diabetes mellitus. J Diabetes Metab Disord. 2013;12(1):14.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 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
  21. 21.
    Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots—a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561–77.PubMedGoogle Scholar
  22. 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
  23. 23.
    Šimundić A-M. Measures of diagnostic accuracy: basic definitions. EJIFCC. 2009;19(4):203–11.PubMedPubMedCentralGoogle Scholar
  24. 24.
    Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Boston: Allyn and Bacon; 2013.Google Scholar
  25. 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
  26. 26.
    Buscema M. A brief overview and introduction to artificial neural networks. Substance Use Misuse. 2002;37(8–10):1093–148.CrossRefPubMedGoogle Scholar
  27. 27.
    Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49(11):1225–31.CrossRefPubMedGoogle Scholar
  28. 28.
    Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63(8):826–33.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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
  30. 30.
    Tsai C-L, Clark S, Camargo CA Jr. Risk stratification for hospitalization in acute asthma: the CHOP classification tree. Am J Emerg Med. 2010;28(7):803–8.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform. 2008;77(2):81–97.CrossRefPubMedGoogle Scholar

Copyright information

© Research Society for Study of Diabetes in India 2017

Authors and Affiliations

  • Wasantha P. Jayawardene
    • 1
  • Dayani C. Nilwala
    • 2
  • Godfred O. Antwi
    • 1
  • David K. Lohrmann
    • 1
  • Mohammad R. Torabi
    • 1
  • Stephanie L. Dickinson
    • 3
  1. 1.Applied Health Science, School of Public Health BloomingtonIndiana UniversityBloomingtonUSA
  2. 2.Pathology DepartmentBase Hospital HomagamaHomagamaSri Lanka
  3. 3.Epidemiology and Biostatistics, School of Public Health BloomingtonIndiana UniversityBloomingtonUSA

Personalised recommendations