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. 2022 Oct 20:9:903090.
doi: 10.3389/fmed.2022.903090. eCollection 2022.

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

Affiliations

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

Victor A Colli et al. Front Med (Lausanne). .

Abstract

Background: Chronic 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.

Methods: Data 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.

Results: The 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.

Conclusion: The 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.

Keywords: Mexican; chronic kidney disease; prediction; risk score; validation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the sample selection.
FIGURE 2
FIGURE 2
Area under the curve of risk predictive models of chronic kidney disease previously described in the Mexican population, using data of the National Health and Nutrition Survey 2016: (A) Kwon Korean model, (B) O’Seaghdha model 1, (C) O’Seaghdha model 2, (D) O’Seaghdha model 3, (E) Nelson model, (F) Al-Shamsi full model, (G) Al-Shamsi stepwise model, (H) Lee model 3, and (I) Lee model 4.

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