Children who are admitted to the hospital and experience deterioration have a high risk of mortality and poor long-term health. Current warning early scores indicating risk of deterioration are subjectively derived and have not reduced in-hospital mortality. In recent work, I developed a vital sign based statistical model that demonstrated improved accuracy over current risk scores at predicting clinical deterioration in hospitalized children 24 hours in advance. Within adults, the combination of longitudinal data analysis techniques, machine learning, and electronic health record (EHR) data have led to highly accurate early warning scores. Therefore, my aim in this grant proposal is to utilize longitudinal integration of EHR data in a machine learning framework to develop a model for predicting clinical deterioration in hospitalized children as early as possible. I will do this by first deriving and validating a prediction model using structured EHR data collected from three pediatric hospitals (Aim 1). Using the same cohort, I will then build and validate a prediction model using features derived from unstructured clinical notes (Aim 2). I will also compare if the addition of unstructured features improves the prediction accuracy of the model derived in Aim 1. Finally, I will determine the association between non-patient level environmental variables within the hospital ecosystem and risk of clinical deterioration in hospitalized children (Aim 3). I will also determine if the addition of these environmental risk factors improves performance of the prediction model derived through Aims 1 and 2. Completion of this proposal will result in a validated pediatric risk prediction model that will enable clinicians to recognize early signs of deterioration in hospitalized children. This will facilitate timely intervention, thereby saving lives and improving long-term health. In addition, this grant will also provide me with crucial data for a future R01 trial aimed at assessing the impact of the prediction model in reducing mortality, decreasing costs, and improving long-term outcomes in hospitalized children. To establish myself as an independent investigator in pediatric prediction modeling, I propose a training plan that includes comprehensive didactics and mentorship in the areas of longitudinal data analysis, advanced machine learning, natural language processing, and concepts in pediatric care. I have assembled a first-class mentorship team comprised of national experts in longitudinal data analysis techniques and EHR-based machine learning (Robert Gibbons PhD and Matthew Churpek MD, PhD). My advisory team is comprised of experts in natural language processing (Dmitriy Dligach PhD), clinical decision support around deterioration events (Dana Edelson MD, MS and Priti Jani MD), and pediatric early warning scores (Christopher Parshuram MB., ChB., D. Phil., FRACP). By completing my research and career development goals, I will develop into an independent expert investigator in developing pediatric prediction models for ultimately improving outcomes in hospitalized children.

Public Health Relevance

Clinical deterioration in hospitalized children is associated with high-risk of mortality as well as complications in their long-term health. An improved prediction model capable of identifying pediatric patients at risk of clinical deterioration as early as possible can facility timely and proper intervention, thereby decreasing preventable death and improving long-term outcomes in hospitalized children.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Scientist Development Award - Research & Training (K01)
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NHLBI Mentored Clinical and Basic Science Review Committee (MCBS)
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Ludlam, Shari
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University of Chicago
Schools of Medicine
United States
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