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The predictive ability of EQ-5D-3L compared to the LACE index and its association with 30-day post-hospitalization outcomes

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To examine whether the EQ-5D-3L at the time of discharge from hospital provides additional prognostic information above the LACE index for 30-day post-discharge hospital readmission and to explore the association of EQ-5D-3L with readmissions, emergency department (ED) visits, and death within the same period.


Using data (n = 495; mean age 62.9 years (SD 18.6), 50.5% female) from a prospective cohort study of patients discharged from medical wards at two university hospitals, the prognostic ability of EQ-5D-3L was examined using C-statistic, Integrated Discrimination Improvement (IDI) Index, and Akaike’s Information Criterion (AIC). The associations between EQ-5D-3L dimensions, total sum, index and VAS scores at the time of discharge and 30-day post-discharge ED visits, readmission, and readmission/death were examined using multivariate logistic regression.


At the time of discharge, 58.6% of participants reported problems in mobility, 28.3% in self-care, 62.1% in usual activities, 62.7% in pain/discomfort, and 42.4% in anxiety/depression. Mean (SD) total sum score was 7.9 (2.0), index score was 0.69 (0.21), and VAS score was 63.7 (18.4). In adjusted analyses, mobility, self-care, usual activities, and the total sum score were significantly associated with 30-day readmission and readmission/death. Differences in C-statistic for LACE readmission prediction models with and without EQ-5D-3L were small. AIC analysis suggests that readmission prediction models containing EQ-5D-3L dimensions or scores were more often preferred to those with the LACE index only. IDI analysis indicates that the discrimination slope of readmission prediction models is significantly improved with the addition of mobility, self-care, or the total sum score of the EQ-5D-3L.


The EQ-5D-3L, especially the mobility and self-care dimensions as well as the total sum score, improves 30-day readmission prediction of the LACE index and is associated with 30-day readmissions or readmissions/death.

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  1. Gold, M., Franks, P., & Erickson, P. (1996). Assessing the health of the nation. The predictive validity of a preference-based measure and self-rated health. Medical Care, 34(2), 163–177.

    Article  CAS  Google Scholar 

  2. Berg, S. K., et al. (2019). Patient-reported outcomes are independent predictors of one-year mortality and cardiac events across cardiac diagnoses: Findings from the national DenHeart survey. European Journal of Preventive Cardiology, 26(6), 624–637.

    Article  Google Scholar 

  3. Greenwald, J. L., & Jack, B. W. (2009). Preventing the preventable: reducing rehospitalizations through coordinated, patient-centered discharge processes. Professional Case Management, 14(3), 135–140.

    Article  Google Scholar 

  4. Liang, J. W., et al. (2017). Quality of life independently predicts long-term mortality but not vascular events: the Northern Manhattan Study. Quality of Life Research, 26(8), 2219–2228.

    Article  Google Scholar 

  5. Wu S, et al., The relationship between self-rated health and objective health status: a population-based study. BMC Public Health, 2013. 13.

  6. Mavaddat, N., et al. (2014). Relationship of self-rated health with fatal and non-fatal outcomes in cardiovascular disease: a systematic review and meta-analysis. PLoS ONE, 9(7), e103509.

    Article  Google Scholar 

  7. Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21–37.

    Article  CAS  Google Scholar 

  8. Hansen, T. B., et al. (2015). Self-reported health-related quality of life predicts 5-year mortality and hospital readmissions in patients with ischaemic heart disease. European Journal of Preventive Cardiology, 22(7), 882–889.

    Article  Google Scholar 

  9. (CIHI), C.I.f.H.I. Patient Reported Outcome Measures. 2015 [cited 2016 September 30 ]; Available from:

  10. Valderas, J. M., Alonso, J., & Guyatt, G. H. (2008). Measuring patient-reported outcomes: moving from clinical trials into clinical practice. Medical Journal of Australia, 189(2), 93–94.

    Article  Google Scholar 

  11. Black N, Patient reported outcome measures could help transform healthcare. BMJ, 2013. 346.

  12. Greenhalgh, J., et al. (2018). How do aggregated patient-reported outcome measures data stimulate health care improvement? A realist synthesis. Journal of Health Services Research & Policy, 23(1), 57–65.

    Article  Google Scholar 

  13. Squitieri, L., Bozic, K. J., & Pusic, A. L. (2017). The Role of Patient-Reported Outcome Measures in Value-Based Payment Reform. Value Health, 20(6), 834–836.

    Article  Google Scholar 

  14. Devlin N and Appleby J, Getting the most out of PROMs: putting health outcomes at the heart of NHS decision-making. 2010, The King’s Fund and The Office Health Economics: London.

  15. Ernstsson O, Janssen MF, and Heintz E, Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries. Journal of Patient-Reported Outcomes, 2020. 4(78).

  16. Al Sayah F, et al., Enhancing the Use of Patient-reported Outcome Measures (PROMs) in the Healthcare System in Alberta - A White Paper. 2020, Alberta PROMs and EQ-5D Research and Support Unit (APERSU): Edmonton AB.

  17. Canadian Institute for Health Information, Patient Reported Outcome Measures. 2015, Canadian Institute for Health Information (CIHI).

  18. Canadian Institute for Health Information. (2012). All-cause readmission to acute care and return to the emergency department. . Ottawa: Canadian Institute for Health Information.

    Google Scholar 

  19. Kansagara, D., et al. (2011). Risk prediction models for hospital readmission: a systematic review. JAMA, 306(15), 1688–1698.

    Article  CAS  Google Scholar 

  20. Lewis G, Curry N, and Bardsley M, How predictive modelling can help reduce risk and hospital admissions, N. Trust, Editor. 2011, Nuffield Trust: London.

  21. van Walraven, C., et al. (2010). Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ, 182(6), 551–557.

    Article  Google Scholar 

  22. Damery, S., & Combes, G. (2017). Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: a retrospective cohort study. British Medical Journal Open, 13(7), e016921.

    Google Scholar 

  23. Miilunpalo, S., et al. (1997). Self-rated health status as a health measure: the predictive value of self-reported health status on the use of physician services and on mortality in the working-age population. Journal of Clinical Epidemiology, 50(5), 517–528.

    Article  CAS  Google Scholar 

  24. Soley-Bori, M., et al. (2015). Functional status and hospital readmissions using the medical expenditure panel survey. Journal of General Internal Medicine, 30(7), 965–972.

    Article  Google Scholar 

  25. Tonkikh, O., et al. (2016). Functional status before and during acute hospitalization and readmission risk identification. Journal of Hospital Medicine, 11(9), 636–641.

    Article  Google Scholar 

  26. Benzer, W., et al. (2016). Health-related quality of life predicts unplanned rehospitalization following coronary revascularization. Herz, 41(2), 138–143.

    Article  CAS  Google Scholar 

  27. Kahlon, S., et al. (2015). Association between frailty and 30-day outcomes after discharge from hospital. CMAJ, 187(11), 799–804.

    Article  Google Scholar 

  28. Bansback N, Tsuchiya A, and A.A. Brazier J, Canadian valuation of EQ-5D health states: preliminary value set and considerations for future valuation studies. PLoS One, 2012. 7(2).

  29. Health Quality Council of Alberta, Alberta Provincial Norms for EQ-5D-3L. 2013, Health Quality Council of Alberta

  30. alth Quality Council of Alberta, Health Quality Council Provincial Survey: Measuring and monitoring for success. 2009, Health Quality Council of Alberta.

  31. Breckenridge, K., et al. (2015). How to routinely collect data on patient-reported outcome and experience measures in renal registries in Europe: an expert consensus meeting. Nephrology, Dialysis, Transplantation, 30(10), 1605–1614.

    Article  Google Scholar 

  32. Valderas, J. M., et al. (2008). The impact of measuring patient-reported outcomes in clinical practice: a systematic review of the literature. Quality of Life Research, 17(2), 179–193.

    Article  CAS  Google Scholar 

  33. Al Sayah F, Ohinmaa A, and Johnson JA, Screening for anxiety and depressive symptoms in type 2 diabetes using patient-reported outcome measures: Comparative performance of the EQ-5D-5L and SF-12v2. MDM Policy and Practice, 2018: p. 1–11.

  34. Short H, et al., Screening for anxiety and depressive symptoms using the EQ-5D-3L post-hospital discharge, in International Society for Quality of Life Research (ISOQOL) 2019: San Diego, California

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This analysis did not receive any financial support from any funding agency, but the PROACTIVE study was supported by an operating grant from Alberta Innovates – Health Solutions. FAM holds the Alberta Health Services Chair in Cardiovascular Outcomes Research at the University of Alberta. SRM was co-PI on the PROACTIVE study; he passed away in January 2018.

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FAS planned and conducted the analysis and drafted the first draft of this manuscript. FAM, AO, and JAJ provided guidance and feedback on the analytical plan and results throughout the analysis, read and provided feedback on various drafts of this manuscript, and approved the submitted draft. SRM was the co-PI of the PROACTIVE study and contributed tremendously to the design and conduction of that study. He passed away two years ago, and his inclusion as a co-author is to honor his contributions to the PROACTIVE study that we analyze in this paper.

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Correspondence to Fatima Al Sayah.

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Authors Fatima Al Sayah, Arto Ohinmaa, and Jeffrey A. Johnson are members of the EuroQol Group.

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Al Sayah, F., McAlister, F.A., Ohinmaa, A. et al. The predictive ability of EQ-5D-3L compared to the LACE index and its association with 30-day post-hospitalization outcomes. Qual Life Res 30, 2583–2590 (2021).

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