A prognostic model for 1-year mortality in older adults after hospital discharge
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.
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ABSTRACT: To estimate the survival and quality-adjusted life-years (QALYs) of Full Code versus Do Not Intubate (DNI) advance directives in patients with severe chronic obstructive pulmonary disease and to evaluate how patient preferences and place of residence influence these outcomes. A Markov decision model using published data for COPD exacerbation outcomes. The advance directives that were modeled were as follows: DNI, allowing only noninvasive mechanical ventilation, or Full Code, allowing all forms of mechanical ventilation including invasive mechanical ventilation with endotracheal tube (ETT) insertion. In community-dwellers, Full Code resulted in a greater likelihood of survival and higher QALYs (4-year survival: 23% Full Code, 18% DNI; QALYs: 1.34 Full Code, 1.24 DNI). When considering patient preferences regarding complications, however, if patients were willing to give up >3 months of life expectancy to avoid ETT complications, or >1 month of life expectancy to avoid long-term institutionalization, DNI resulted in higher QALYs. For patients in long-term institutions, DNI resulted in a greater likelihood of survival and higher QALYs (4-year survival: 2% DNI, 1% Full Code; QALYs: 0.29 DNI, 0.24 Full Code). In sensitivity analyses, the model was sensitive to the probabilities of ETT complication and noninvasive mechanical ventilation failure and to patient preferences about ETT complications and long-term institutionalization. Our model demonstrates that patient preferences regarding ETT complications and long-term institutionalization, as well as baseline place of residence, affect the advance directive recommendation when considered in terms of both survival and QALYs. Decision modeling can demonstrate the potential trade-off between survival and quality of life, using patient preferences and disease-specific data, to inform the shared advance directive decision.Value in Health 03/2012; 15(2):357-66. DOI:10.1016/j.jval.2011.10.015 · 2.89 Impact Factor
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ABSTRACT: Aim of this study was to evaluate the usefulness of a Multidimensional Prognostic Index (MPI) based on a Comprehensive Geriatric Assessment (CGA) for predicting mortality risk in older patients with dementia. The present was a retrospective study with a year of follow-up that included 262 patients aged 65 years and older with a diagnosis of dementia. A standardized CGA that included information on clinical, cognitive, functional, and nutritional aspects, as well as comorbidity, medications, and social support network, was used to calculate MPI. The predictive value of the MPI for all-cause mortality over 1 month, 6 months, and 12 months of follow-up was evaluated. Higher MPI values were significantly associated with higher mortality at 1 month (MPI-1, low risk = 0%, MPI-2, moderate risk = 5.2%, MPI-3, severe risk = 13.7%; p < 0.002), 6-months (MPI-1 = 2.7%, MPI-2 = 11.2%, MPI-3 = 28.8%; p < 0.001), and 12-months (MPI-1 = 2.7%, MPI-2 = 18.2%, MPI-3 = 35.6%; p < 0.001) of follow-up. The discrimination of the MPI was also good, with areas under the ROC curves of 0.77 (sensitivity = 82.9%, specificity = 66.0%, with a cut off value > 0.16) at 12-months of follow up. In conclusion, the MPI, calculated from information collected in a standardized CGA, accurately stratified hospitalized elderly patients with dementia into groups at varying risk of short- and long-term mortality.Journal of Alzheimer's disease: JAD 08/2009; 18(1):191-9. DOI:10.3233/JAD-2009-1139 · 3.61 Impact Factor
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ABSTRACT: 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. 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. 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%). 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. © 8872147 Canada Inc.Canadian Medical Association Journal 06/2015; DOI:10.1503/cmaj.150209 · 5.81 Impact Factor