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.