Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

Viktor Tóth,Douglas P Barnaby,Kevin R Bock,Karina W Davidson,Lance B Becker,Theodoros P Zanos,Jamie S Hirsch,Thomas Mcginn,Marsha Meytlis,Yousef Al-Abed,Michael I Oppenheim

doi:10.1038/s41746-020-00355-7

Abstract

Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

Highlights

Poor sleep in hospitals is a common problem, with up to half of admitted patients experiencing insomnia[1,2]
The algorithm ingests a parsimonious list of longitudinal features, including respiratory rate (RR), heart rate (HR), systolic blood pressure (BP), body temperature (Tmpr), patient age, and a calculated risk score (Modified Early Warning Score [MEWS]), and produces a nightly assessment of overnight stability
As shown in the left portion of the figure, the combination of the patient’s vital sign (VS) and MEWS risk score led to a model prediction of “stable” during the first night

Summary

Introduction

Poor sleep in hospitals is a common problem, with up to half of admitted patients experiencing insomnia[1,2]. The underlying causes of impaired sleep are multifactorial and include the effects of acute illness, pain, medications, and preexisting conditions (e.g., sleep apnea), as well as environmental factors, such as noise, light, unfamiliar surroundings, and patient care activities[3,5]. This latter group of factors is among the most common causes of sleep disruption in hospitalized patients and maybe the most amenable to targeted initiatives[2,7]. Recent studies demonstrated fewer sleep interruptions and improved patient experience through targeted reductions in overnight VS measurements and medication administrations; determining which patients can forgo VS was left to subjective clinical assessment[10]

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Conclusion