Purpose: Predictive models allow clinicians to identify higher- and lower-risk patients and make targeted treatment decisions. Microalbuminuria (MA) is a condition whose presence is understood to be an early marker for cardiovascular disease. The aims of this study were to develop a patient data-driven predictive model and a risk-score assessment to improve the identification of MA.
Methods: The 2007-2008 National Health and Nutrition Examination Survey (NHANES) was utilized to create a predictive model. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized for internal validation. The 2012-2013 NHANES was used as an external validation database. Multivariate logistic regression was performed to create the model. Performance was evaluated using three criteria: (1) receiver operating characteristic curves; (2) pseudo-R (2) values; and (3) goodness of fit (Hosmer-Lemeshow). The model was then used to develop a risk-score chart.
Results: A model was developed using variables for which there was a significant relationship. Variables included were systolic blood pressure, fasting glucose, C-reactive protein, blood urea nitrogen, and alcohol consumption. The model performed well, and no significant differences were observed when utilized in the validation datasets. A risk score was developed, and the probability of developing MA for each score was calculated.
Conclusion: The predictive model provides new evidence about variables related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. The risk score developed may allow clinicians to measure a patient's MA risk.
Keywords: Albuminuria; Microalbuminuria; Predictive model; Proteinuria.