Rationale, design and population description of the CREDENCE study: cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records.
Jingyuan LiangRomana PylypchukXun TangPeng ShenXiaofei LiuYi ChenJing TanJinguo WuJingyi ZhangPing LuHongbo LinPei GaoRodney T JacksonPublished in: European journal of epidemiology (2021)
The cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records (CREDENCE) study is a unique prospectively designed investigation of cardiovascular risk in two large contemporary cohorts of people with type 2 diabetes from New Zealand (NZ) and China. The study was designed to derive equivalent cardiovascular risk prediction equations in a developed and a developing country, using the same epidemiological and statistical methodology. Two similar cohorts of people with type 2 diabetes were identified from large general population studies in China and New Zealand, which had been generated from longitudinal electronic health record systems. The CREDENCE study aims to determine whether cardiovascular risk prediction equations derived in patients with type 2 diabetes in a developed country are applicable in a developing country, and vice versa, by deriving and validating equivalent diabetes-specific cardiovascular risk prediction models from the two countries. Baseline data in CREDENCE was collected from October 2004 in New Zealand and from January 2010 in China. In the first stage of CREDENCE, a total of 93,207 patients (46,649 from NZ and 46,558 from China) were followed until December 31st 2018. Median follow-up was 7.0 years (New Zealand) and 5.7 years (China). There were 5926 (7.7% fatal) CVD events in the New Zealand cohort and 3650 (8.8% fatal) in the Chinese cohort. The research results have implications for policy makers, clinicians and the public and will facilitate personalised management of cardiovascular risk in people with type 2 diabetes worldwide.
Keyphrases
- electronic health record
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- type diabetes
- cardiovascular disease
- prognostic factors
- clinical decision support
- healthcare
- peritoneal dialysis
- mental health
- adverse drug
- emergency department
- public health
- machine learning
- artificial intelligence
- patient reported outcomes
- adipose tissue
- deep learning