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Original article

Vol. 147 No. 2930 (2017)

Prediction of potentially avoidable readmission risk in a division of general internal medicine

DOI
https://doi.org/10.4414/smw.2017.14470
Cite this as:
Swiss Med Wkly. 2017;147:w14470
Published
24.07.2017

Summary

INTRODUCTION

The 30-day post-discharge readmission rate is a quality indicator that may reflect suboptimal care. The computerised algorithm SQLape® can retrospectively identify a potentially avoidable readmission (PARA) with high sensitivity and specificity. We retrospectively analysed the hospital stays of patients readmitted to the Department of Internal Medicine of the CHUV (Centre Hospitalier Universitaire Vaudois) in order to quantify the proportion of PARAs and derive a risk prediction model.

METHOD

All hospitalisations between January 2009 and December 2011 in our division of general internal medicine were analysed. Readmissions within 30 days of discharge were categorised using SQLape®. The impact on PARAs was tested for various clinical and nonclinical factors. The performance of the developed model was compared with the well-validated LACE and HOSPITAL scores.

RESULTS

From a total of 11 074 hospital stays, 777 (7%) were followed with PARA within 30 days. By analysing a group of 6729 eligible stays, defined in particular by the patients' returning to their place of residence (home or residential care centre), we identified the following risk factors: ≥1 hospitalisation in the year preceding index admission, Charlson score >1, active cancer, hyponatraemia, length of stay >11 days, prescription of ≥15 different medications during the stay. These variables were used to derive a risk prediction model for PARA with a good discriminatory power (C-statistic 0.70) and calibration (p = 0.69). Patients were then classified as low (16.4%), intermediate (49.4%) or high (34.2%) risk of PARA. The estimated risk of PARA for each category was 3.5%, 8.7% and 19.6%, respectively. The LACE and the HOSPITAL scores were significantly correlated with the PARA risk. The discriminatory power of the LACE (C-statistic 0.61) and the HOSPITAL (C-statistic 0.54) were lower than our model.

CONCLUSION

Our model identifies patients at high risk of 30-day PARA with a good performance. It could be used to target transition of care interventions. Nevertheless, this model should be validated on more data and could be improved with additional parameters. Our results highlight the difficulty to generalise one model in the context of different healthcare systems.

References

  1. Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55(6):573–87. doi:.https://doi.org/10.1016/S0895-4356(01)00521-2
  2. Halfon P, Eggli Y, Prêtre-Rohrbach I, Meylan D, Marazzi A, Burnand B. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44(11):972–81. doi:.https://doi.org/10.1097/01.mlr.0000228002.43688.c2
  3. van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551–7. doi:.https://doi.org/10.1503/cmaj.091117
  4. Gruneir A, Dhalla IA, van Walraven C, Fischer HD, Camacho X, Rochon PA, et al. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med. 2011;5(2):e104–11.
  5. Hasan O, Meltzer DO, Shaykevich SA, Bell CM, Kaboli PJ, Auerbach AD, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211–9. doi:.https://doi.org/10.1007/s11606-009-1196-1
  6. Billings J, Dixon J, Mijanovich T, Wennberg D. Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients. BMJ. 2006;333(7563):327. doi:.https://doi.org/10.1136/bmj.38870.657917.AE
  7. Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–8. doi:.https://doi.org/10.1001/jamainternmed.2013.3023
  8. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98. doi:.https://doi.org/10.1001/jama.2011.1515
  9. Allaudeen N, Schnipper JL, Orav EJ, Wachter RM, Vidyarthi AR. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011;26(7):771–6. doi:.https://doi.org/10.1007/s11606-011-1663-3
  10. Cotter PE, Bhalla VK, Wallis SJ, Biram RW. Predicting readmissions: poor performance of the LACE index in an older UK population. Age Ageing. 2012;41(6):784–9. doi:.https://doi.org/10.1093/ageing/afs073
  11. Cooksley T, Nanayakkara PW, Nickel CH, Subbe CP, Kellett J, Kidney R, et al.; safer@home consortium. Readmissions of medical patients: an external validation of two existing prediction scores. QJM. 2016;109(4):245–8. doi:.https://doi.org/10.1093/qjmed/hcv130
  12. Donzé JD, Williams MV, Robinson EJ, Zimlichman E, Aujesky D, Vasilevskis EE, et al. International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions. JAMA Intern Med. 2016;176(4):496–502. doi:.https://doi.org/10.1001/jamainternmed.2015.8462
  13. Aubert CE, Folly A, Mancinetti M, Hayoz D, Donzé J. Prospective validation and adaptation of the HOSPITAL score to predict high risk of unplanned readmission of medical patients. Swiss Med Wkly. 2016;146:w14335.
  14. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. doi:.https://doi.org/10.1016/0021-9681(87)90171-8
  15. Zilberberg MD, Exuzides A, Spalding J, Foreman A, Jones AG, Colby C, et al. Hyponatremia and hospital outcomes among patients with pneumonia: a retrospective cohort study. BMC Pulm Med. 2008;8(1):16. doi:.https://doi.org/10.1186/1471-2466-8-16
  16. McDonnell PJ, Jacobs MR. Hospital admissions resulting from preventable adverse drug reactions. Ann Pharmacother. 2002;36(9):1331–6. doi:.https://doi.org/10.1345/aph.1A333
  17. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365(21):2002–12. doi:.https://doi.org/10.1056/NEJMsa1103053
  18. Fick DM, Cooper JW, Wade WE, Waller JL, Maclean JR, Beers MH. Updating the Beers criteria for potentially inappropriate medication use in older adults: results of a US consensus panel of experts. Arch Intern Med. 2003;163(22):2716–24. doi:.https://doi.org/10.1001/archinte.163.22.2716
  19. National Committee on Quality Insurance. Final NDC lists: use of high-risk medications in the elderly (DAE). Washington, DC: National Committee on Quality Assurance; 2011.
  20. Balla U, Malnick S, Schattner A. Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems. Medicine (Baltimore). 2008;87(5):294–300. doi:.https://doi.org/10.1097/MD.0b013e3181886f93
  21. Witherington EM, Pirzada OM, Avery AJ. Communication gaps and readmissions to hospital for patients aged 75 years and older: observational study. Qual Saf Health Care. 2008;17(1):71–5. doi:.https://doi.org/10.1136/qshc.2006.020842
  22. Campbell SE, Seymour DG, Primrose WR ; ACMEPLUS Project. A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing. 2004;33(2):110–5. doi:.https://doi.org/10.1093/ageing/afh036
  23. Harris CMSA, Sridharan A, Landis R, Howell E, Wright S. What happens to the medication regimens of older adults during and after an acute hospitalization? J Patient Saf. 2013;9(3):150–3. doi:.https://doi.org/10.1097/PTS.0b013e318286f87d
  24. Davies EC, Green CF, Mottram DR, Rowe PH, Pirmohamed M. Emergency re-admissions to hospital due to adverse drug reactions within 1 year of the index admission. Br J Clin Pharmacol. 2010;70(5):749–55. doi:.https://doi.org/10.1111/j.1365-2125.2010.03751.x
  25. Forster AJ, Clark HD, Menard A, Dupuis N, Chernish R, Chandok N, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345–9.
  26. Alassaad A, Melhus H, Hammarlund-Udenaes M, Bertilsson M, Gillespie U, Sundström J. A tool for prediction of risk of rehospitalisation and mortality in the hospitalised elderly: secondary analysis of clinical trial data. BMJ Open. 2015;5(2):e007259. doi:.https://doi.org/10.1136/bmjopen-2014-007259
  27. Goldfield NI, McCullough EC, Hughes JS, Tang AM, Eastman B, Rawlins LK, et al. Identifying potentially preventable readmissions. Health Care Financ Rev. 2008;30(1):75–91.
  28. Joynt KE, Jha AK. Thirty-day readmissions--truth and consequences. N Engl J Med. 2012;366(15):1366–9. doi:.https://doi.org/10.1056/NEJMp1201598
  29. Mabire CEY, Junger A. Analyse des séjours hospitaliers: apport des données infirmières. Swiss Medical Informatics. 2009;66:6–14.
  30. Tinetti ME. Clinical practice. Preventing falls in elderly persons. N Engl J Med. 2003;348(1):42–9. doi:.https://doi.org/10.1056/NEJMcp020719
  31. Tinetti ME, Ginter SF. Identifying mobility dysfunctions in elderly patients. Standard neuromuscular examination or direct assessment? JAMA. 1988;259(8):1190–3. doi:.https://doi.org/10.1001/jama.1988.03720080024022
  32. Gandek B, Ware JE, Aaronson NK, Apolone G, Bjorner JB, Brazier JE, et al. Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol. 1998;51(11):1171–8. doi:.https://doi.org/10.1016/S0895-4356(98)00109-7
  33. van Walraven C, Bennett C, Jennings A, Austin PC, Forster AJ. Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ. 2011;183(7):E391–402. doi:.https://doi.org/10.1503/cmaj.101860
  34. van Walraven C, Jennings A, Forster AJ. A meta-analysis of hospital 30-day avoidable readmission rates. J Eval Clin Pract. 2012;18(6):1211–8. doi:.https://doi.org/10.1111/j.1365-2753.2011.01773.x
  35. Kilcup M, Schultz D, Carlson J, Wilson B. Postdischarge pharmacist medication reconciliation: impact on readmission rates and financial savings. J Am Pharm Assoc (2003). 2013;53(1):78–84. doi:.https://doi.org/10.1331/JAPhA.2013.11250
  36. Initiative leads to 11% drop in HF readmissions. Hosp Case Manag. 2013;21(4):48–9.
  37. Garnier A, Uhlmann M, Griesser AC, Lamy O. [Discharge from hospital: how to improve continuity of medical care?]. Rev Med Suisse. 2015;11(493):2064, 2066–9. [Article in French]

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