Predictive Modeling Using a Nationally Representative Database to Identify Patients at Risk of Developing Microalbuminuria

Persistent Link:
http://hdl.handle.net/10150/333040
Title:
Predictive Modeling Using a Nationally Representative Database to Identify Patients at Risk of Developing Microalbuminuria
Author:
Villa Zapata, Lorenzo Andrés
Issue Date:
2014
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Background: Predictive models allow clinicians to more accurately identify higher- and lower-risk patients and make more targeted treatment decisions, which can help improve efficiency in health systems. Microalbuminuria (MA) is a condition characterized by the presence of albumin in the urine below the threshold detectable by a standard dipstick. Its presence is understood to be an early marker for cardiovascular disease. Therefore, identifying patients at risk for MA and intervening to treat or prevent conditions associated with MA, such as high blood pressure or high blood glucose, may support cost-effective treatment. Methods: The National Health and Nutrition Examination Survey (NHANES) was utilized to create predictive models for MA. This database includes clinical, medical and laboratory data. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized to validate the model. Univariate logistic regression was performed to identify variables related with MA. Stepwise multivariate logistic regression was performed to create the models. Model performance was evaluated using three criteria: 1) receiver operator characteristic (ROC) curves; 2) pseudo R-squared; and 3) goodness of fit (Hosmer-Lemeshow). The predictive models were then used to develop risk-scores. Results: Two models were developed using variables that had significant correlations in the univariate analysis (p-value<0.05). For Model A, variables included in the final model were: systolic blood pressure (SBP); fasting glucose; C-reactive protein; blood urea nitrogen (BUN); and alcohol consumption. For Model B, the variables were: SBP; glycohemoglobin; BUN; smoking status; and alcohol consumption. Both models performed well in the creation dataset and no significant difference between the models was found when they were evaluated in the validation set. A 0-18 risk score was developed utilizing Model A, and the predictive probability of developing MA was calculated. Conclusion: The predictive models developed provide new evidence about which variables are related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. Furthermore, the risk score developed using Model A may allow clinicians to more easily measure patient risk. Both predictive models will require external validation before they can be applied to other populations.
Type:
text; Electronic Dissertation
Keywords:
Predictive modeling; Pharmaceutical Sciences; Microalbuminuria
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Pharmaceutical Sciences
Degree Grantor:
University of Arizona
Advisor:
Warholak, Terri

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titlePredictive Modeling Using a Nationally Representative Database to Identify Patients at Risk of Developing Microalbuminuriaen_US
dc.creatorVilla Zapata, Lorenzo Andrésen_US
dc.contributor.authorVilla Zapata, Lorenzo Andrésen_US
dc.date.issued2014-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractBackground: Predictive models allow clinicians to more accurately identify higher- and lower-risk patients and make more targeted treatment decisions, which can help improve efficiency in health systems. Microalbuminuria (MA) is a condition characterized by the presence of albumin in the urine below the threshold detectable by a standard dipstick. Its presence is understood to be an early marker for cardiovascular disease. Therefore, identifying patients at risk for MA and intervening to treat or prevent conditions associated with MA, such as high blood pressure or high blood glucose, may support cost-effective treatment. Methods: The National Health and Nutrition Examination Survey (NHANES) was utilized to create predictive models for MA. This database includes clinical, medical and laboratory data. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized to validate the model. Univariate logistic regression was performed to identify variables related with MA. Stepwise multivariate logistic regression was performed to create the models. Model performance was evaluated using three criteria: 1) receiver operator characteristic (ROC) curves; 2) pseudo R-squared; and 3) goodness of fit (Hosmer-Lemeshow). The predictive models were then used to develop risk-scores. Results: Two models were developed using variables that had significant correlations in the univariate analysis (p-value<0.05). For Model A, variables included in the final model were: systolic blood pressure (SBP); fasting glucose; C-reactive protein; blood urea nitrogen (BUN); and alcohol consumption. For Model B, the variables were: SBP; glycohemoglobin; BUN; smoking status; and alcohol consumption. Both models performed well in the creation dataset and no significant difference between the models was found when they were evaluated in the validation set. A 0-18 risk score was developed utilizing Model A, and the predictive probability of developing MA was calculated. Conclusion: The predictive models developed provide new evidence about which variables are related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. Furthermore, the risk score developed using Model A may allow clinicians to more easily measure patient risk. Both predictive models will require external validation before they can be applied to other populations.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectPredictive modelingen_US
dc.subjectPharmaceutical Sciencesen_US
dc.subjectMicroalbuminuriaen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePharmaceutical Sciencesen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorWarholak, Terrien_US
dc.contributor.committeememberWarholak, Terrien_US
dc.contributor.committeememberSlack, Marionen_US
dc.contributor.committeememberMalone, Danielen_US
dc.contributor.committeememberMurcko, Anitaen_US
dc.contributor.committeememberRunger, Georgeen_US
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