Prediction models for the risk of cardiovascular diseases in Chinese patients with type 2 diabetes mellitus: a systematic review

Objectives: Diabetes mellitus (DM) is a serious public health issue worldwide, and DM patients have higher risk of cardiovascular diseases (CVDs), which is the leading cause of DM-related deaths. China has the largest DM population, yet a robust model to predict CVDs in Chinese DM patients is still lacking. This systematic review is carried out to summarize existing models and identify potentially important predictors for CVDs in Chinese DM patients.

Study design: Systematic review.

Methods: Medline and Embase were searched for data from April 1st, 2011 to May 31st, 2018. A study was eligible if it developed CVD (defined as total CVD or any major cardiovascular component) risk prediction models or explored potential predictors of CVD specifically for Chinese people with type 2 DM. Standardized forms were utilized to extract information, appraise applicability, risk of bias, and availabilities.

Results: Five models and 29 studies focusing on potential predictors were identified. Models for a primary care setting, or to predict total CVD, are rare. A number of common predictors (e.g. age, sex, diabetes duration, smoking status, glycated hemoglobin (HbA1c), blood pressure, lipid profile, and treatment modalities) were observed in existing models, in which urine albumin:creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) are highly recommended for the Chinese population. Variability of blood pressure (BP) and HbA1c should be included in prediction model development as novel factors. Meanwhile, interactions between age, sex, and risk factors should also be considered.

Conclusions: A 10-year prediction model for CVD risk in Chinese type 2 DM patients is lacking and urgently needed. There is insufficient evidence to support the inclusion of other novel predictors in CVDs risk prediction functions for routine clinical use.