A Machine Learning Approach to Predicting Iatrogenic Withdrawal in Critically-ill Children

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PROJECT ABSTRACT Iatrogenic withdrawal affects up to 57% of children who receive sedative and analgesic medications in the pediatric intensive care unit (ICU), contributing to delayed recovery, patient and parental distress and prolonged hospitalization in (an estimated) 70,000 children per year. Due to limitations in sample size and variable sets, studies on iatrogenic withdrawal in pediatric ICUs have primarily focused on the association of single risk factors, screening tools, and treatment regimens, without attention to early identification of at-risk children. This proposal will leverage a national, electronic health record derived database of over 200,000 pediatric ICU patients to investigate the full spectrum of risk factors, patient profiles, and practice patterns associated with iatrogenic withdrawal from sedatives and analgesic medications that could identify children at risk prior to withdrawal symptoms or early in their treatment course. I will achieve this by first identifying risk factors, patient profiles and practice patterns associated with iatrogenic withdrawal using traditional biostatistical techniques. Second, I will use the identified risk factors in addition to time dependent variables, such as vital signs and laboratory values, to develop a dynamic model to predict risk of developing iatrogenic withdrawal in pediatric ICU patients using novel supervised machine learning methodology. Third, I will externally validate the dynamic prediction model in a local dataset from my institution’s electronic health record to determine if the model can accurately predict those patients who develop clinically confirmed iatrogenic withdrawal. Successful completion of these aims will lead to the development of an analytical tool to identify iatrogenic withdrawal in children in ICUs using electronic-based resources which can be operationalized into clinical practice. The proposed studies are feasible because of 1) my strong and productive multi-disciplinary team of clinician and data science mentors who meet biweekly under the guidance of my mentorship team including Dr. Murray Pollack, a leader in the field of predictive modelling in pediatric critical care and Dr. Michael Bell, a national leader in neurocritical care, and 2) the recent availability of reliable, large, multi- institutional pediatric databases derived directly from the electronic health record (EHR). This K23 award proposal will also facilitate an integrated didactic and mentor-led experiential training program designed to develop and refine my knowledge and skills in big database research, predictive modelling, and morbidity associated with sedative and analgesic medication administration. The career development and research proposal will enable my long-term career goal, which is to become an independently funded clinical investigator focused on the prevention of healthcare-acquired morbidity through big data research and predictive analytics.