The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

Abstract

Background Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. Objective The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. Methods Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. Results The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. Conclusions Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.

Benchmarking machine learning models on multi-centre eICU critical care dataset

“…While our definition is focused on diagnosis using ICD codes for this task, the definition of phenotyping may encompass other domains, such as procedures [9] [10] for example. However, expanding the definition of phenotyping beyond standardised ICD codes would have required development of non-standardised rules, as no common standard approach for [12].…”

“…In this regard, Tu et al[29] applied neural network based methods on a Canadian private dataset, which includes patients with cardiac surgery. The developed model was able to detect the patient with low, intermediate, and high prolonged stay in ICU.Phenotyping has been a popular task in recent years[30] [31], although problem definition varies widely, from focusing on ICD based diagnosis[24] up to including clinical procedures and medications[9] [10]. Several works on phenotyping from clinical time series have focused on variations of tensor factorization and related models[30] [31][32], and the most recently published studies on phenotyping are focused on deep learning methods.…”

Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset

“…In this study, we used the eICU Collaborative Research Database [ 21 ], which contains remotely monitored critical care data from adult patients admitted to over 200 hospitals in the United States from 2014-2015 [ 22 ]. The database includes basic patient characteristics as well as medications, laboratory values, vital signs, and other discrete physiological variables measured at the bedside ICU and interfaced with the tele-ICU.…”

Telehealth in emergency medicine: A consensus conference to map the intersection of telehealth and emergency medicine

“…[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67] In trauma and critical illness, evidence suggests that telehealth can optimize triage, reduce transfers, and better stabilize transferred patients, with decreased mortality and ICU admission rates. [68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84] Among pediatric critical care patients, telehealth has been shown to decrease ICU admissions, transfers, medication errors, and improve provider satisfaction. 29,85-10 0 In behavioral health, telehealth has reduced length of stay, improved transfer processes, and enabled "psychiatry clearance" and discharge planning.…”