Article

A prognostic model for 1-year mortality in older adults after hospital discharge

Section of Geriatrics, Department of Medicine, University of Chicago, Chicago, Ill 60637, USA.
The American journal of medicine (Impact Factor: 5.3). 06/2007; 120(5):455-60. DOI: 10.1016/j.amjmed.2006.09.021
Source: PubMed

ABSTRACT To develop and validate a prognostic index for 1-year mortality of hospitalized older adults using standard administrative data readily available after discharge.
The prognostic index was developed and validated retrospectively in 6382 older adults discharged from general medicine services at an urban teaching hospital over a 4-year period. Potential risk factors for 1-year mortality were obtained from administrative data and examined using logistic regression models. Each risk factor associated independently with mortality was assigned a weight based on the odds ratios, and risk scores were calculated for each patient by adding the points of each independent risk factor present. Patients in the development cohort were divided into quartiles of risk based on their final risk score. A similar analysis was performed on the validation cohort to confirm the original results.
Risk factors independently associated with 1-year mortality included: aged 70 to 74 years (1 point); aged 75 years and greater (2 points); length of stay at least 5 days (1 point); discharge to nursing home (1 point); metastatic cancer (2 points); and other comorbidities (congestive heart failure, peripheral vascular disease, renal disease, hematologic or solid, nonmetastatic malignancy, and dementia, each 1 point). In the derivation cohort, 1-year mortality was 11% in the lowest-risk group (0 or 1 point) and 48% in the highest-risk group (4 or greater points). Similarly, in the validation cohort, 1-year mortality was 11% in the lowest risk group and 45% in the highest-risk group. The area under the receiver operating characteristic curve was 0.70 for the derivation cohort and 0.68 for the validation cohort.
Reasonable prognostic information for 1-year mortality in older patients discharged from general medicine services can be derived from administrative data to identify high-risk groups of persons.

0 Followers
 · 
84 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Common Syndromes in Older Adults ii Minnesota Evidence-based Practice Center This report is based on research conducted by the Minnesota Evidence-based Practice Center (EPC) under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract No. HHSA 290-2007-10064-1). The findings and conclusions in this document are those of the authors, who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. The information in this report is intended to help clinicians, employers, policymakers, and others make informed decisions about the provision of health care services. This report is intended as a reference and not as a substitute for clinical judgment. This report may be used, in whole or in part, as the basis for the development of clinical practice guidelines and other quality enhancement tools, or as a basis for reimbursement and coverage policies. AHRQ or U.S. Department of Health and Human Services endorsement of such derivative products may not be stated or implied. This document is in the public domain and may be used and reprinted without permission, except those copyrighted materials noted for which further reproduction is prohibited without the specific permission of copyright holders.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To develop a screening tool to identify elderly patients at the end of life and quantify the risk of death in hospital or soon after discharge for to minimise prognostic uncertainty and avoid potentially harmful and futile treatments. Narrative literature review of definitions, tools and measurements that could be combined into a screening tool based on routinely available or obtainable data at the point of care to identify elderly patients who are unavoidably dying at the time of admission or at risk of dying during hospitalisation. Variables and thresholds proposed for the Criteria for Screening and Triaging to Appropriate aLternative care (CriSTAL screening tool) were adopted from existing scales and published research findings showing association with either in-hospital, 30-day or 3-month mortality. Eighteen predictor instruments and their variants were examined. The final items for the new CriSTAL screening tool included: age ≥65; meeting ≥2 deterioration criteria; an index of frailty with ≥2 criteria; early warning score >4; presence of ≥1 selected comorbidities; nursing home placement; evidence of cognitive impairment; prior emergency hospitalisation or intensive care unit readmission in the past year; abnormal ECG; and proteinuria. An unambiguous checklist may assist clinicians in reducing uncertainty patients who are likely to die within the next 3 months and help initiate transparent conversations with families and patients about end-of-life care. Retrospective chart review and prospective validation will be undertaken to optimise the number of prognostic items for easy administration and enhanced generalisability. Development of an evidence-based tool for defining and identifying the dying patient in hospital: CriSTAL. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
    Supportive and Palliative Care 01/2015; 5(1). DOI:10.1136/bmjspcare-2014-000770
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Abstract Objective: Loss of daily living functions can be a marker for end of life and possible hospice eligibility. Unfortunately, data on patient's functional abilities is not available in all settings. In this study we compare predictive accuracy of two indices designed to predict 6-month mortality among nursing home residents. One is based on traditional measures of functional deterioration and the other on patients' diagnoses and demography. Methods: We created the Hospice ELigibility Prediction (HELP) Index by examining mortality of 140,699 Veterans Administration (VA) nursing home residents. For these nursing home residents, the available data on history of hospital admissions were divided into training (112,897 cases) and validation (27,832 cases) sets. The training data were used to estimate the parameters of the HELP Index based on (1) diagnoses, (2) age on admission, and (3) number of diagnoses at admission. The validation data were used to assess the accuracy of predictions of the HELP Index. The cross-validated accuracy of the HELP Index was compared with the Barthel Index (BI) of functional ability obtained from 296,052 VA nursing home residents. A receiver operating characteristic curve was used to examine sensitivity and specificity of the predicted odds of mortality. Results: The area under the curve (AUC) for the HELP Index was 0.838. This was significantly (α <0.01) higher than the AUC for the BI of 0.692. Conclusions: For nursing home residents, comorbid diagnoses predict 6-month mortality more accurately than functional status. The HELP Index can be used to estimate 6-month mortality from hospital data and can guide prognostic discussions prior to and following nursing home admission.
    Journal of Palliative Medicine 11/2014; 49(2). DOI:10.1089/jpm.2014.0130 · 2.06 Impact Factor