The mortality risk score and the ADG score: two points-based scoring systems for the Johns Hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada

Published: October 1, 2011
Category: Bibliography > Papers
Authors: Austin PC, Walraven C
Countries: Canada
Language: null
Types: Population Health
Settings: Hospital

Med Care 49:940-947.

Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

BACKGROUND: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’ Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults.

OBJECTIVES: To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups.

RESEARCH DESIGN: Retrospective cohort constructed using population-based administrative data.

PARTICIPANTS:All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples.

MEASURES: Death within 1 year.

RESULTS: In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk.

CONCLUSIONS: The MRS combined values for a person’s age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

PMID: 21921849

Population Markers,Mortality Prediction,Resource Use,Canada,Predictive Risk Modeling,Adults,Aged,80 and over,Co-morbidity,Databases,Factual,Gender,Logistic Models,Middle Aged,Ontario/epidemiology,Predictive Value of Tests,Retrospective Studies

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