Chronic kidney disease risk prediction scores assessment and development in Mexican adult population

Abstract

BackgroundChronic kidney disease (CKD) is a major public health problem, with considerable growth in prevalence and mortality in recent years. Screening of CKD at primary care is crucial for the implementation of prevention strategies. The aims of this study are to assess CKD risk prediction scores and to develop a risk prediction score for the Mexican adult population.MethodsData from the Mexican National Health and Nutrition Survey 2016 was utilized and 3463 participants ≥ 20 years old were included. Reduced renal function with Glomerular filtration rate and/or the presence of albuminuria was defined as CKD. Multiple logistic regression models were performed for the creation of a training and validation model. Additionally, several models were validated in our Mexican population.ResultsThe developed training model included sex, age, body mass index, fast plasma glucose, systolic blood pressure, and triglycerides, as did the validation model. The area under the curve (AUC) was 0.78 (95% CI: 0.72, 0.79) for training model, and 0.76 (95% CI: 0.71, 0.80) in validation model for Mexican adult population. Age, female gender, presence of diabetes and hypertension, elevated systolic and diastolic blood pressure, serum and urinary creatinine, and higher HbA1c were significantly associated with the prevalent chronic kidney disease. Previous CKD risk predictive models were evaluated with a representative sample of the Mexican adult population, their AUC was between 0.61 and 0.78.ConclusionThe designed CKD risk predictive model satisfactorily predicts using simple and common variables in primary medical care. This model could have multiple benefits; such as, the identification of the population at risk, and prevention of CKD.

Risk Prediction Score for Chronic Kidney Disease in Healthy Adults and Adults With Type 2 Diabetes: Systematic Review

“…Also, looking through these variables gave us a chance to look at prevention therapies that control the progression of these variables, as reflected in the progression of CKD. CKD risk predictive models should be applied mainly in populations with risk factors for CKD susceptibility, initiation, or progression ( 33 ).…”