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Practical prediction model for the risk of 2-year mortality of individuals in the general population
  1. Alexander Goldfarb-Rumyantzev1,
  2. Shiva Gautam2,
  3. Robert S Brown1
  1. 1Division of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
  2. 2Department of Internal Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Alexander Goldfarb-Rumyantzev, Division of Nephrology, Beth Israel Deaconess Medical Center and Harvard Medical School, 171 Pilgrim Rd, Libby-2, Boston, MA 02215, USA; agoldfar{at}bidmc.harvard.edu

Abstract

This study proposed to validate a prediction model and risk-stratification tool of 2-year mortality rates of individuals in the general population suitable for office practice use. A risk indicator (R) derived from data in the literature was based on only 6 variables: to calculate R for an individual, starting with 0, for each year of age above 60, add 0.14; for a male, add 0.9; for diabetes mellitus, add 0.7; for albuminuria >30 mg/g of creatinine, add 0.7; for stage ≥3 chronic kidney disease (CKD), add 0.9; for cardiovascular disease (CVD), add 1.4; or for both CKD and CVD, add 1.7. We developed a univariate logistic regression model predicting 2-year individual mortality rates. The National Health and Nutrition Examination Survey (NHANES) data set (1999–2004 with deaths through 2006) was used as the target for validation. These 12,515 subjects had a mean age of 48.9±18.1 years, 48% males, 9.5% diabetes, 11.7% albuminuria, 6.8% CVD, 5.4% CKD, and 2.8% both CKD and CVD. Using the risk indicator R alone to predict mortality demonstrated good performance with area under the receiver operating characteristic (ROC) curve of 0.84. Dividing subjects into low-risk (R=0–1.0), low intermediate risk (R>1.0–3.0), high intermediate risk (R>3.0–5.0) or high-risk (R>5.0) categories predicted 2-year mortality rates of 0.52%, 1.44%, 5.19% and 15.24%, respectively, by the prediction model compared with actual mortality rates of 0.29%, 2.48%, 5.13% and 13.40%, respectively. We have validated a model of risk stratification using easily identified clinical characteristics to predict 2-year mortality rates of individuals in the general population. The model demonstrated performance adequate for its potential use for clinical practice and research decisions.

  • Risk
  • Death
  • Medicine
  • Decision Making

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