Predicting Acute Brain Dysfunction in Older Patients: A Quality Improvement Tool
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DESCRIPTION (provided by applicant): Acute brain dysfunction in the form of delirium or coma affects 50% of critically ill patients and is extremely hazardous. Those suffering from intensive care unit (ICU) delirium or coma experience substantial increases in healthcare utilization, mortality, and long-term cognitive impairment. Moreover, growing evidence that many risk factors are avoidable (or reducible) has resulted in increased attention to acute brain dysfunction as an avoidable hospital outcome. In 2007, the Centers for Medicare and Medicaid Services proposed the inclusion of hospital-acquired delirium as a quality outcome measure. The designation of delirium as a quality outcome has been held back by the lack of risk-adjustment. Similar to any hospital quality outcome (e.g. mortality, surgical site infection), methods are needed to account for pre-existing risk factors. In order for acute brain dysfunction to serve as a useful and reliable outcome of hospital quality and safety, a validated prediction model for acute brain dysfunction is needed. Because levels of brain dysfunction fluctuate from day to day, models must also account for daily changes in risk. This proposal describes a career development plan that enables me to develop expertise in acute brain dysfunction, build dynamic prediction modeling tools for innovative quality measures, and develop informatics skills for future implementation studies. The Specific Aims of the research are a) To identify clinical risk factors for acute brain dysfunction among critically ill patients through a systematic literature review, b) To develop dynamic transition models that predict daily changes in acute brain dysfunction in critically ill elderly, and c) To externally validate and update the candidate risk prediction models in a cohort of critically ill patients, utilizing clinical data sources. The career development plan integrates; a) advanced coursework in health system improvement, biostatistics, and bioinformatics, b) participation in local / national meetings to develop expertise in geriatrics, predictive modeling, and quality improvement, c) a multidisciplinary mentored research experience, and d) a highly supportive research environment. This environment includes an internationally recognized NIA funded ICU Delirium and Cognitive Impairment Study Group, an AHRQ funded Evidence-based Practice Center, a distinguished Department of Biomedical Informatics, NIH funded Clinical and Translational Science Award, and a Master of Public Health Program that places 90% of graduates into academic careers. Overall, this career development award will advance my career in quality and aging research. In addition, the award will provide critical support to guide translational studies / R01 applications. Future studies will assess the impact of daily acute brain dysfunction prediction models for risk-targeted interventions and study the impact of acute brain dysfunction surveillance to measure quality.
