Use of biomarkers of metals to improve prediction performance of cardiovascular disease mortality.

ABSTRACT:

Background

Whether including additional environmental risk factors improves cardiovascular disease (CVD) prediction is unclear. We attempted to improve CVD mortality prediction performance beyond traditional CVD risk factors by additionally using metals measured in the urine and blood and with statistical machine learning methods.

Methods

Our sample included 7,085 U.S. adults aged 40 years or older from the National Health and Nutrition Examination Survey 2003-2004 through 2015-2016, linked with the National Death Index through December 31, 2019. Data were randomly split into a 50/50 training dataset used to construct CVD mortality prediction models (n = 3542) and testing dataset used as validation to assess prediction performance (n = 3543). Relative to the traditional risk

SUBMITTER: Fansler SD 

PROVIDER: S-EPMC11542438 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature