Dr. Khaled Abdel-Kader has completed prior training in nephrology and a master's in medical education with formal training in adult learning, medical errors, and cognitive theory as well as introductory coursework in clinical research and biostatistics. His primary research interest is characterizing and addressing chronic kidney disease (CKD) care deficiencies in the primary care setting. He has received individual post-doctoral funding to support his work in this area. His career goal is to become an expert in CKD epidemiology and an independent clinical investigator studying electronic medical record (EMR)-based interventions to improve CKD care and outcomes. In this career development award (CDA), he focuses on improving primary care physician (PCP) screening for CKD. This award provides him with mentorship, formal coursework, and hands-on experience in epidemiology, research design, medical informatics, decision analysis, health services research, and biostatistics. He has assembled a group of highly skilled mentors who will guide him and help him develop into an independent clinical investigator. A supportive research environment that has already cultivated the development of numerous successful independent clinical investigators complements these individual mentors. In addition, unique institutional resources including a well-developed EMR, EMR research infrastructure, and large patient base make the local environment an ideal setting for the candidate and his research. Dr. Abdel- Kader will use these research experiences, coursework, mentorship, and institutional resources and commitment to continue his progression to becoming an independently funded clinical researcher. An important element of the application is the candidate's research proposal. He will conduct his research project in 2 phases. In the first phase, he will leverage the local, well-developed EMR database and the University of Pittsburgh's large ambulatory patient base (>450,000 unique patients in the prior 2 years) to develop a decision tree predictive model to identify patients at high risk for CKD without the use of serum chemistries. He will compare the performance of the decision tree model to a prominent, recently developed logistic regression model of CKD risk. After identifying the model with the best performance, the candidate will conduct a randomized controlled trial of PCPs examining the effect of implementing the CKD predictive model in the EMR as a clinical alert versus usual care. The clinical alert will remind PCPs to screen high-risk patients for CKD if they have not already done so. This novel approach pairs machine modeling of CKD risk factors with an EMR clinical decision support system (CDSS) to provide real-time guidance to PCPs to improve the care delivered to patients with occult CKD. This project will provide the applicant with valuable experience in data mining, medical informatics, decision analysis, health services research, and clinical trial design and implementation. These experiences will be integral to his development into an independent clinical researcher. In addition to these direct experiences, the applicant will also benefit from the teaching and guidance provided by his team of proficient mentors and consultants. Dr. Mark Unruh, primary mentor for the proposal, is a well-funded, independent clinical investigator who brings expertise in epidemiology, clinical trials, and CKD. Dr. Mark Roberts, Chair of Health Policy and Management at the University of Pittsburgh's Graduate School of Public Health, brings a strong record of independent funding and well-established research interests in predictive modeling, decision tree analysis, and CKD. Dr. Shyam Visweswaran, an investigator in biomedical informatics, brings expertise in biomedical data mining, predictive modeling, and CDSS. Dr. Charity Moore is a highly skilled health services statistician with extensive experience in clinical trials. She will bring her expertise in research design, implementation, analysis, and interpretation to the project. Dr. Gary Fischer, director of the general internal medicine ambulatory clinic, has substantial experience in the integration of the EMR and CDSS with physician workflow. Dr. Douglas Landsittel, a statistician with an interest in the classification of disease outcomes using decision trees, has extensive experience in building and validating predictive models. This interdisciplinary team combines uniquely qualified investigators with the diversity of experience and expertise necessary for the successful completion of the proposed research and the candidate's training. To complement these hands-on experiences and mentorship activities, the candidate will undertake formal coursework through the University of Pittsburgh's Graduate School of Public Health, Department of Biomedical Informatics, and the Institute for Clinical Research Education (part of the university's Clinical and Translational Science Institute). These courses will include formal training in research design and clinical trial implementation, applied medical informatics and decision analysis, and methods in health services research and biostatistics. In addition, the medical center has numerous seminars, workshops, and leadership courses that the candidate will participate in to form collaborative relationships and enhance his skills. In summary, the candidate's research interest in improving the quality of CKD care delivery coupled with a sound background in medical education and early training in clinical research make him an ideal candidate to use this CDA to investigate EMR interventions that can broadly improve CKD screening by PCPs. The applicant's experienced, multidisciplinary mentorship team, strong institutional resources and support, and the formal training he will complete under this award will ensure that he continues to develop into a successful independent clinical investigator studying methods to improve PCP care delivery to CKD patients.
Chronic kidney disease is a growing public health problem with over 10% of adults affected by the disorder. Evidence indicates that late recognition and suboptimal care of chronic kidney disease contributes to poor health in these patients. This study will develop a model that can predict the presence of chronic kidney disease without the use of blood work. This model will be implemented to alert doctors in real-time to screen high-risk patients who may have unrecognized chronic kidney disease with the potential to substantially improve the care of these patients.
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