External validation of the Hospital-patient One-year Mortality Risk (HOMR) model for predicting death within 1 year after hospital admission

Carl van Walraven, Finlay A McAlister, Jeffrey A Bakal, Steven Hawken, Jacques Donzé, Carl van Walraven, Finlay A McAlister, Jeffrey A Bakal, Steven Hawken, Jacques Donzé

Abstract

Background: Predicting long-term survival after admission to hospital is helpful for clinical, administrative and research purposes. The Hospital-patient One-year Mortality Risk (HOMR) model was derived and internally validated to predict the risk of death within 1 year after admission. We conducted an external validation of the model in a large multicentre study.

Methods: We used administrative data for all nonpsychiatric admissions of adult patients to hospitals in the provinces of Ontario (2003-2010) and Alberta (2011-2012), and to the Brigham and Women's Hospital in Boston (2010-2012) to calculate each patient's HOMR score at admission. The HOMR score is based on a set of parameters that captures patient demographics, health burden and severity of acute illness. We determined patient status (alive or dead) 1 year after admission using population-based registries.

Results: The 3 validation cohorts (n = 2,862,996 in Ontario, 210 595 in Alberta and 66,683 in Boston) were distinct from each other and from the derivation cohort. The overall risk of death within 1 year after admission was 8.7% (95% confidence interval [CI] 8.7% to 8.8%). The HOMR score was strongly and significantly associated with risk of death in all populations and was highly discriminative, with a C statistic ranging from 0.89 (95% CI 0.87 to 0.91) to 0.92 (95% CI 0.91 to 0.92). Observed and expected outcome risks were similar (median absolute difference in percent dying in 1 yr 0.3%, interquartile range 0.05%-2.5%).

Interpretation: The HOMR score, calculated using routinely collected administrative data, accurately predicted the risk of death among adult patients within 1 year after admission to hospital for nonpsychiatric indications. Similar performance was seen when the score was used in geographically and temporally diverse populations. The HOMR model can be used for risk adjustment in analyses of health administrative data to predict long-term survival among hospital patients.

© 2015 Canadian Medical Association or its licensors.

Figures

Figure 1:
Figure 1:
Covariates used to calculate a patient’s Hospital-patient One-year Mortality Risk (HOMR) score at the time of admission to hospital. The Diagnostic Risk Score (Appendix 1) quantifies risk of death for diagnostic groups beyond that explained by the other covariates. Points for the interacting covariates of age and Charlson Comorbidity Index score include the risk of patient age, comorbidity score and their interaction. In contrast, points for living status and admission urgency include the risk of these covariates and their interaction with admissions by ambulance in the previous year; points for the latter covariate are considered separately. See Table 3 for the expected risk of death within 1 year after hospital admission for each HOMR score. ED = emergency department, ICU = intensive care unit. *In the year before admission. †See Appendix 2 for definitions of each service. (Appendices are available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.150209/-/DC1)
Figure 2:
Figure 2:
Observed and expected risks of death within 1 year after admission to hospital in the 3 validation cohorts (Ontario, Alberta and Boston) and the derivation cohort, as calculated by the Hospital-patient One-year Mortality Risk (HOMR) model. Error bars indicate 95% confidence intervals. A summary of the calibration of each cohort to the expected risk of death is given in Table 2.

References

    1. van Walraven C. The Hospital-patient One-year Mortality Risk score accurately predicts long term death risk in hospitalized patients. J Clin Epidemiol 2014;67:1025–34.
    1. McGinn TG, Guyatt GH, Wyer PC, et al. Users’ guides to the medical literature. XXII: How to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 2000;284:79–84.
    1. Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med 2000;19:453–73.
    1. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515–24.
    1. Di Bari M, Balzi D, Roberts AT, et al. Prognostic stratification of older persons based on simple administrative data: development and validation of the “Silver Code,” to be used in emergency department triage. J Gerontol A Biol Sci Med Sci 2010;65:159–64.
    1. Dramé M, Novella JL, Lang PO, et al. Derivation and validation of a mortality-risk index from a cohort of frail elderly patients hospitalised in medical wards via emergencies: the SAFES study. Eur J Epidemiol 2008;23:783–91.
    1. Fischer SM, Gozansky WS, Sauaia A, et al. A practical tool to identify patients who may benefit from a palliative approach: the CARING criteria. J Pain Symptom Manage 2006;31:285–92.
    1. Inouye SK, Bogardus ST, Jr, Vitagliano G, et al. Burden of Illness Score for Elderly Persons: risk adjustment incorporating the cumulative impact of diseases, physiologic abnormalities, and functional impairments [published erratum in Med Care 2003; 41:446]. Med Care 2003;41:70–83.
    1. Martínez-Velilla N, Cambra-Contin K, Ibanez-Beroiz B. Comorbidity and prognostic indices do not improve the 5-year mortality prediction of components of comprehensive geriatric assessment in hospitalized older patients. BMC Geriatr 2014;14:64.
    1. Knaus WA, Harrell FE, Jr, Lynn J, et al. The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments. Ann Intern Med 1995;122:191–203.
    1. Levine SK, Sachs GA, Jin L, et al. A prognostic model for 1-year mortality in older adults after hospital discharge. Am J Med 2007;120:455–60.
    1. Pilotto A, Ferrucci L, Franceschi M, et al. Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res 2008;11:151–61.
    1. Pilotto A, Addante F, Franceschi M, et al. Multidimensional prognostic index based on a comprehensive geriatric assessment predicts short-term mortality in older patients with heart failure. Circ Heart Fail 2010;3:14–20.
    1. Teno JM, Harrell FE, Jr, Knaus W, et al. Prediction of survival for older hospitalized patients: the HELP survival model. Hospitalized Elderly Longitudinal Project. J Am Geriatr Soc 2000;48(Suppl 5): S16–24.
    1. Pilotto A, Addante F, Ferrucci L, et al. The multidimensional prognostic index predicts short- and long-term mortality in hospitalized geriatric patients with pneumonia. J Gerontol A Biol Sci Med Sci 2009;64:880–7.
    1. Pilotto A, Rengo F, Marchionni N, et al. Comparing the prognostic accuracy for all-cause mortality of frailty instruments: a multicentre 1-year follow-up in hospitalized older patients. PLoS ONE 2012;7:e29090.
    1. Pilotto A, Sancarlo D, Aucella F, et al. Addition of the Multidimensional Prognostic Index to the estimated glomerular filtration rate improves prediction of long-term all-cause mortality in older patients with chronic kidney disease. Rejuvenation Res 2012;15:82–8.
    1. Pilotto A, Sancarlo D, Panza F, et al. The Multidimensional Prognostic Index (MPI), based on a comprehensive geriatric assessment, predicts short- and long-term mortality in hospitalized older patients with dementia. J Alzheimers Dis 2009;18:191–9.
    1. Walter LC, Brand RJ, Counsell SR, et al. Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. JAMA 2001;285:2987–94.
    1. Yourman LC, Lee SJ, Schonberg MA, et al. Prognostic indices for older adults: A systematic review. JAMA 2012;307:182–92.
    1. Siontis GCM, Tzoulaki I, Castaldi PJ, et al. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol 2015;68:25–34.
    1. Kieszak SM, Flanders WD, Kosinski AS, et al. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. J Clin Epidemiol 1999;52:137–42.
    1. Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived from ICD-9-CCM administrative data. Med Care 2002;40:675–85.
    1. Hammill BG, Curtis LH, Fonarow GC, et al. Incremental value of clinical data beyond claims data in predicting 30-day outcomes after heart failure hospitalization. Circ Cardiovasc Qual Outcomes 2011;4:60–7.
    1. Walter LC, Brand RJ, Counsell SR, et al. Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. JAMA 2001;285:2987–94.
    1. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305:50–8.
    1. Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet 2013;381:752–62.

Source: PubMed

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