Curation and description of a blood glucose management and nutritional support cohort using the eICU collaborative research database
Fitzgerald, O.; Perez-Concha, O.; Gallego Luxan, B.; Rudd, L.; Jorm, L.
Show abstract
Freely available electronic medical record (EMR) data collections have transformed data science and observational research in critical care medicine. Descriptive characterisation of these data collections can aid in highlighting variation in clinical practice and patient outcomes across Intensive Care Units (ICUs). Glycaemic control and nutritional management are important aspects of patient management in the ICU. Blood glucose on admission has a well-known U-shaped relationship with mortality and morbidity, with both hypo- and hyper-glycemia being associated with poor patient outcomes. The importance of nutritional support has been highlighted in critical care guidelines. However, both areas have open research questions and highly variable clinical practices that observational data may help highlight and inform. To aid in this research, we curated a database of patients using the eICU collaborative research data (eICU-CRD), which we describe in the current paper, focusing on patient blood glucose, insulin therapy and enteral nutrition. The eICU-CRD is derived from a telehealth EMR covering 208 United States hospitals from 2014-2015. In addition to descriptive statistics and graphical analysis, we highlight any limitations in data quality. Our results are in line with previous research suggesting the eICU-CRD cohort is of lower illness severity than the average ICU patient cohort and so receive less invasive interventions. Examinations of data missingness revealed issues with medication orders and non-reporting of nutrition by several hospitals. Overall, with care around missingness we believe the eICU-CRD to be a valuable resource in evidence generation for critical care research.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Replicating a COVID-19 study in a national England database to assess the generalisability of research with regional electronic health record data 93%
- Development and validation of automated computer aided-risk score for predicting the risk of in-hospital mortality using first electronically recorded blood test results and vital signs for COVID-19 hospital admissions: a retrospective development and validation study 92%
- Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study 92%
Similar papers in this journal
- Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19: a descriptive cohort study within the OpenSAFELY platform 90%
- TRACKing Excess Deaths (TRACKED): an interactive online tool to monitor excess deaths associated with COVID-19 pandemic in the United Kingdom 88%
- Quantitative biomarker profiling of serum samples in the By-Band-Sleeve trial 87%
Similar papers in this journal
- Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units 92%
- Derivation and validation of a triage tool for acutely ill adults with suspected COVID-19: The PRIEST observational cohort study 91%
- Emergency calls are early indicators of ICU bed requirement during the COVID-19 epidemic 91%
Similar papers in this journal
Similar papers in this journal
- Does Intermittent Nutrition Enterally Normalise hormonal and metabolic responses to feeding in critically ill adults? The DINE-Normal proof-of-concept study 92%
- More spice, less salt: how capsaicin affects liking for and perceived saltiness of foods in people with smell loss 87%
- The impact of the Covid-19 lockdown on the experiences and feeding practices of new mothers in the UK: Preliminary data from the COVID-19 New Mum Study 87%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.