Abstract
Poorly controlled glucose levels are associated with serious morbidity and mortality in hospitalized patients. Hospital diabetes management aims to maintain the glucose level within a desired range, primarily via insulin administration. Current inpatient glucose control relies significantly on expert knowledge, but this results in large variability and often suboptimal blood sugars in practice. We applied supervised machine learning methods to electronic health record (EHR) data to build predictive models that can inform inpatient insulin management. We found that individual blood glucose levels and insulin dosing are highly erratic and cannot be predicted precisely (MAE 28mg/dL, R2 0.2). However, prescribing decisions can still be driven by the more reliable predictions of average daily glucose levels (MAE 21mg/dL, R2 0.4) and whether any patient’s glucose levels will be higher than the clinically desired range in the next day (sens 0.73, spec 0.79).
Competing Interest Statement
Jonathan H. Chen: 1. Co-founder of Reaction Explorer LLC that develops and licenses organic chemistry education software. 2. Paid consulting or speaker fees from National Institute of Drug Abuse Clinical Trials Network, Tuolc Inc., Roche Inc.
Funding Statement
This research was supported in part by the NIH Big Data 2 Knowledge initiative via the National Institute of Environmental Health Sciences under Award Number K01ES026837, the Gordon and Betty Moore Foundation through Grant GBMF8040, and a Stanford Human-Centered Artificial Intelligence Seed Grant. Additional support comes from Diabetes, Endocrinology and Metabolism Training Grant 5T32DK007217-44 and the Enlight Foundation Graduate Fellowship.
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Data Availability
This research used data or services provided by STARR, "STAnford medicine Research data Repository", a clinical data warehouse containing live Epic data from Stanford Health Care (SHC), the University Healthcare Alliance (UHA) and Packard Children’s Health Alliance (PCHA) clinics and other auxiliary data from Hospital applications such as radiology PACS. The STARR platform is developed and operated by Stanford Medicine Research IT team and is made possible by Stanford School of Medicine Research Office. The study was approved by the Stanford Institutional Review Board. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, VA, or Stanford Healthcare.