OBJECTIVES: The purpose of this study was to develop and validate a risk-adjustment index for 1-year mortality specific to older people, based on administrative discharge diagnoses. DESIGN: Two prospective cohort studies, in tandem. The index developed in the initial cohort was subsequently validated in a separate cohort. SETTING: General medicine service of a university teaching hospital. PARTICIPANTS: For the development cohort, 524 hospitalized general medical patients aged 70 and older. For the validation cohort, 852 comparable patients. MEASUREMENTS: Administrative diagnosis data were used to construct the proposed index and several other widely used indices (Deyo-adapted Charlson; Acute Physiology, Age, Chronic Health Evaluation III conditions; total number of diagnoses; All Patient Refined Diagnosis Related Groups; and Disease Staging). We used receiver operating characteristic curve analysis and Cox proportional hazards modeling to compare our proposed index with the other indices with respect to predictive accuracy and strength of association with 1-year mortality. RESULTS: The High-Risk Diagnoses for the Elderly Scale was developed using 10 high-risk medical diagnoses. Individual condition weights, based on the magnitude of 1-year mortality risk, ranged from 1 (pneumonia, diabetes mellitus with end-organ damage) to 6 (lymphoma/leukemia); possible index scores ranged from 0 to 27. Mortality rates for patients categorized into four risk groups based on the index were 9.5%, 31.8%, 46.4%, and 73.6% in the development cohort (C statistic = 0.76), and 9.9%, 24.3%, 33.6%, and 50.8% in the validation subjects (C statistic = 0.68). The new index was a stronger predictor of mortality than several widely used measures. CONCLUSION: The High-Risk Diagnoses for the Elderly Scale, based on readily available administrative data,is a simple, accurate system for prediction of 1-year mortality in older hospitalized patients that demonstrated generalizability to an independent sample. Future studies are needed to test this index in other settings and populations.
OBJECTIVES: The purpose of this study was to develop and validate a risk-adjustment index for 1-year mortality specific to older people, based on administrative discharge diagnoses. DESIGN: Two prospective cohort studies, in tandem. The index developed in the initial cohort was subsequently validated in a separate cohort. SETTING: General medicine service of a university teaching hospital. PARTICIPANTS: For the development cohort, 524 hospitalized general medical patients aged 70 and older. For the validation cohort, 852 comparable patients. MEASUREMENTS: Administrative diagnosis data were used to construct the proposed index and several other widely used indices (Deyo-adapted Charlson; Acute Physiology, Age, Chronic Health Evaluation III conditions; total number of diagnoses; All Patient Refined Diagnosis Related Groups; and Disease Staging). We used receiver operating characteristic curve analysis and Cox proportional hazards modeling to compare our proposed index with the other indices with respect to predictive accuracy and strength of association with 1-year mortality. RESULTS: The High-Risk Diagnoses for the Elderly Scale was developed using 10 high-risk medical diagnoses. Individual condition weights, based on the magnitude of 1-year mortality risk, ranged from 1 (pneumonia, diabetes mellitus with end-organ damage) to 6 (lymphoma/leukemia); possible index scores ranged from 0 to 27. Mortality rates for patients categorized into four risk groups based on the index were 9.5%, 31.8%, 46.4%, and 73.6% in the development cohort (C statistic = 0.76), and 9.9%, 24.3%, 33.6%, and 50.8% in the validation subjects (C statistic = 0.68). The new index was a stronger predictor of mortality than several widely used measures. CONCLUSION: The High-Risk Diagnoses for the Elderly Scale, based on readily available administrative data,is a simple, accurate system for prediction of 1-year mortality in older hospitalized patients that demonstrated generalizability to an independent sample. Future studies are needed to test this index in other settings and populations.
Authors: Jessica W Weiss; Robert W Platt; Micah L Thorp; Xiuhai Yang; David H Smith; Amanda Petrik; Elizabeth Eckstrom; Cynthia Morris; Ann M O'Hare; Eric S Johnson Journal: J Am Geriatr Soc Date: 2015-03-04 Impact factor: 5.562
Authors: Joy R Goebel; Lynn V Doering; Lisa R Shugarman; Steve M Asch; Cathy D Sherbourne; Andy B Lanto; Lorraine S Evangelista; Adeline M Nyamathi; Sally L Maliski; Karl A Lorenz Journal: J Pain Symptom Manage Date: 2009-09-03 Impact factor: 3.612
Authors: Giampiero Mazzaglia; Lorenzo Roti; Giacomo Corsini; Angela Colombini; Gavino Maciocco; Niccolò Marchionni; Eva Buiatti; Luigi Ferrucci; Mauro Di Bari Journal: J Am Geriatr Soc Date: 2007-10-18 Impact factor: 5.562