Clinical Drug Investigation

, Volume 23, Issue 2, pp 119–128 | Cite as

Influence of Drugs, Demographics and Medical History on Hospital Readmission of Elderly Patients

A Predictive Model
  • E. F. Ruth Morrissey
  • James C. McElnay
  • Michael Scott
  • Brian J. McConnell
Original Research Article



To investigate factors that influence hospital readmissions of elderly patients and to construct a robust hospital readmissions predictive model.


Each hospitalised patient was interviewed and medical, demographic and socioeconomic data were obtained from their medical charts. These patients were followed up prospectively for 1 year post discharge with all unplanned readmissions to general medical (including cardiology) wards during this time period recorded. Univariate analysis (chi-squared) was used to identify variables that had at least a minimal association with one or more unplanned readmissions 12 months post discharge (p < 0.25). These were entered into backward stepwise elimination logistic regression analysis (model entry set at p = 0.05). Examination of the significance of the log-likelihood ratio test for each variable determined its contribution to the model.


Data from a total of 487 elderly patients (≥65 years of age) with non-elective admissions to general medicine wards were used to refine the readmissions model. The refined model was then validated using similar data retrospectively collected from medical charts regarding 732 elderly patients (≥65 years of age).


Multivariate logistic regression analysis yielded a nine-variable model, which contained both predictive and protective variables for one or more readmissions 12 months post discharge from hospital. This model had a specificity of 79.3%, sensitivity of 60.0% and overall accuracy of 71.2% (cut-off point of p = 0.5). When the model was applied to the validation population, an overall percentage accuracy in classification of 65.2% was obtained.


This refined and validated hospital readmissions predictive model could be used by healthcare professionals to help identify vulnerable patients upon admission to hospital, and to put in place a comprehensive discharge planning process.



The authors have provided no information on conflicts of interest directly relevant to the content of this study.


  1. 1.
    Waite K, Oddone E, Weinberger M, et al. Lack of association between patients’ measured burden of disease and risk for hospital readmission. J Clin Epidemiol 1994; 47: 1229–36PubMedCrossRefGoogle Scholar
  2. 2.
    Neill J, Williams J. Leaving hospital: elderly people and their discharge to community care. National Institute for Social Work Research Unit Report to the Department of Health. London: HMSO, 1992Google Scholar
  3. 3.
    Satish S, Winograd CH, Chavez C, et al. Geriatric targeting criteria as predictors of survival and health care utilization. J Am Geriatr Soc 1996; 44: 914–21PubMedGoogle Scholar
  4. 4.
    Evans RL, Hendricks RD, Lawrence KV, et al. Identifying factors associated with health care use: a hospital-based risk-screening index. Soc Sci Med 1998; 27: 947–54CrossRefGoogle Scholar
  5. 5.
    Reuben DB, Wolde-Tsadik G, Pardamean B, et al. The use of targeting criteria in hospitalised HMO patients; results from the demonstration phase of the hospitalised older persons’ evaluation (HOPE) study. J Am Geriatr Soc 1992; 40: 482–8PubMedGoogle Scholar
  6. 6.
    Boult C, Dowd B, McCaffrey D, et al. Screening elders for risk ofhospital admission. J Am Geriatr Soc 1993; 41: 811–7PubMedGoogle Scholar
  7. 7.
    McElnay JC, McCallion CR, Al-Deagi F, et al. Hospital read-missions of elderly patients: a predictive model. Proceedings of ESCP Drug Information Conference; 1997, 39Google Scholar
  8. 8.
    Blaylock A, Cason CL. Discharge planning: predicting patients’ needs. J Gerontol Nurs 1992; 18: 5–10PubMedGoogle Scholar
  9. 9.
    Bull MJ. A discharge planning questionnaire for clinical practice. Appl Nurs Res 1994; 7: 193–9PubMedCrossRefGoogle Scholar
  10. 10.
    Evans RL, Hendricks RD. Evaluating hospital discharge planning: a randomised clinical trial. Med Care 1993; 31: 358–70PubMedCrossRefGoogle Scholar
  11. 11.
    Reuben DB, Borok GM, Wolde-Tsadik G, et al. A randomised trial of comprehensive geriatric assessment in the care of hospitalised patients. New Engl J Med 1995; 332: 1345–9PubMedCrossRefGoogle Scholar
  12. 12.
    Boult L, Boult C, Pirie P, et al. Test-retest reliability of a questionnaire that identifies elders at risk for hospital admission. J Am Geriatr Soc 1994; 42: 707–11PubMedGoogle Scholar
  13. 13.
    Muckart DJJ, Bhagwanjee S, Gouws E. Validation of an outcome prediction model for critically ill trauma patients without head injury. J Trauma 1997; 43: 934–9PubMedCrossRefGoogle Scholar
  14. 14.
    Hosmer DW, Lemeshow S. Applied logistic regression: Wiley series in probability and mathematical statistics. New York: John Wiley & Sons, 1989Google Scholar
  15. 15.
    Colledge NR, Ford MJ. The early hospital readmission of elderly people. Scott Med J 1994; 39: 51–2PubMedGoogle Scholar
  16. 16.
    Andrews K. Relevance of readmission of elderly patients discharged from a geriatric unit. J Am Geriatr Soc 1986; 34: 5–11PubMedGoogle Scholar
  17. 17.
    Runciman P, Currie CT, Nicol M, et al. Discharge of elderly patients from an accident and emergency department: evaluation of health visitor follow-up. J Adv Nurs 1996; 24: 711–8PubMedCrossRefGoogle Scholar
  18. 18.
    Col N, Fanale JE, Kronholm P. The role of medication non-compliance and adverse drug reactions in hospitalisations of the elderly. Arch Intern Med 1990; 150: 841–5PubMedCrossRefGoogle Scholar
  19. 19.
    Stewart RB, Cooper JW. Polypharmacy in the aged: practical solutions. Drugs Aging 1994; 4: 449–61PubMedCrossRefGoogle Scholar
  20. 20.
    Lakshmanan MG, O’Hershey CO, Breslau D. Hospital admissions caused by iatrogenic disease. Arch Intern Med 1986; 46: 1931–4CrossRefGoogle Scholar
  21. 21.
    Kuulasmaa K, Tunstall-Pedoe H, Dobson A, et al. Estimation of contribution of changes in classic risk factors to trends in coronary-event rates across the WHO MONICA Project populations. Lancet 2000; 355: 675–87PubMedCrossRefGoogle Scholar
  22. 22.
    Chin MH, Goldman L. Correlates of early hospital readmission or death in patients with congestive heart failure. Am J Cardiol 1997; 79: 1640–4PubMedCrossRefGoogle Scholar
  23. 23.
    Vollmer RT. Multivariate statistical analysis for pathologists: Part 1. the logistic model. Am J Clin Pathol 1996; 105: 115–26PubMedGoogle Scholar

Copyright information

© Adis International Limited 2003

Authors and Affiliations

  • E. F. Ruth Morrissey
    • 1
  • James C. McElnay
    • 1
  • Michael Scott
    • 2
  • Brian J. McConnell
    • 2
  1. 1.School of Pharmacy, The Queen’s University of BelfastBelfastIreland
  2. 2.Academic Practice UnitAntrim Area HospitalAntrimIreland

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