Lorenzo Villa-Zapata1, Terri Warholak2, Marion Slack3, Daniel Malone4, Anita Murcko5, George Runger6, Michael Levengood7. 1. Facultad de Farmacia, Universidad de Concepción, Barrio Universitario s/n, Concepción, Chile. lorenzovilla@udec.cl. 2. Center for Health Outcomes and PharmacoEconomic Research, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. warholak@pharmacy.arizonaedu. 3. Pharmacy Practice and Science, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. slack@pharmacy.arizona.edu. 4. Pharmacy Practice and Science, University of Arizona, College of Pharmacy, 1295 N. Martin, Tucson, AZ, 85721, USA. malone@pharmacy.arizona.edu. 5. Biomedical Informatics, Arizona State University, 550 N. 3rd Street, Phoenix, AZ, 85004-0698, USA. Anita.Murcko@asu.edu. 6. Biomedical Informatics, Arizona State University, Mayo Clinic, Samuel C. Johnson Research Bldg. 13212 East Shea Boulevard, Phoenix, AZ, USA. George.Runger@asu.edu. 7. College of Pharmacy, The University of Arizona, 1295 N. Martin, Tucson, AZ, 85721, USA. mjlevengood@gmail.com.
Abstract
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.
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 ureanitrogen, 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.
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