ArticleJAMA network open2018
Assessment of Intensive Care Unit Laboratory Values That Differ From Reference Ranges and Association With Patient Mortality and Length of Stay.
Article in JAMA network open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Phenobarbital as an Adjuvant to benzodiazepines when compared to Single-agent benzodiazepine Treatment (usual care) for Alcohol withdrawal syndrome in the intensive care unit (PASTA): a study protocol.Pilot and feasibility studies · 2026Article
- A multidimensional hierarchical framework for sources of bias in real-world healthcare evidence: a scoping review.Journal of biomedical informatics · 2026Article
- Safety recommendations on laboratory and blood count values for early mobilization in critically ill adults: an expert consensus.Critical care science · 2026Article
- Novel prehospital lactate cut-off estimation for mortality: a multicentre observational study.BMJ open · 2024Observational
- Septic Shock Requiring Three Vasopressors: Patient Demographics and Outcomes.Critical care explorations · 2024Article
- Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study.JMIR medical informatics · 2022Article
- Human acute inflammatory recovery is defined by co-regulatory dynamics of white blood cell and platelet populations.Nature communications · 2022Article
- Article
- Impact of Oxygen Saturation on Mortality in Obese and Non-obese Critically Ill Patients With Mechanical Ventilation: A Retrospective Observational Study.Frontiers in medicine · 2022Article
- Varying association of laboratory values with reference ranges and outcomes in critically ill patients: an analysis of data from five databases in four countries across Asia, Europe and North America.BMJ health & care informatics · 2021Article
- White Blood Cell and Platelet Dynamics Define Human Inflammatory Recovery.medRxiv : the preprint server for health sciences · 2021Article
- Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation.JMIR medical informatics · 2021Article
- A retrospective cross-sectional study for predicting 72-h mortality in patients with serum aspartate aminotransferase levels ≥ 3000 U/L.Scientific reports · 2021Article
- [Laboratory testing in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2020Review
- Healthcare Transformation in Singapore With Artificial Intelligence.Frontiers in digital health · 2020Article
- Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.NPJ digital medicine · 2019Article
- Critical Care, Critical Data.Biomedical engineering and computational biology · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Importance: Laboratory data are frequently collected throughout the care of critically ill patients. Currently, these data are interpreted by comparison with values from healthy outpatient volunteers. Whether this is the most useful comparison has yet to be demonstrated. Objectives: To understand how the distribution of intensive care unit (ICU) laboratory values differs from the reference range, and how these distributions are related to patient outcomes. Design, Setting, and Participants: Cross-sectional study of a large critical care database, the Medical Information Mart for Intensive Care database, from January 1, 2001, to October 31, 2012. The database is collected from ICU data from a large tertiary medical center in Boston, Massachusetts. The data are collected from medical, cardiac, neurologic, and surgical ICUs. All patients in the database from all ICUs for 2001 to 2012 were included. Common laboratory measurements over the time window of interest were sampled. The analysis was conducted from March to June 2017. Main Outcomes and Measures: The overlapping coefficient and Cohen standardized mean difference between distributions were calculated, and kernel density estimate visualizations for the association between laboratory values and the probability of death or quartile of ICU length of stay were created. Results: Among 38 605 patients in the ICU (21 852 [56.6%] male; mean [SD] age, 74.5 [55.1] years), 8878 (23%) had the best outcome (ICU survival, shortest quartile length of stay) and 3090 (8%) had the worst outcome (ICU nonsurvival). Distribution curves based on ICU data differed significantly from the hospital standard range (mean [SD] overlapping coefficient, 0.51 [0.32-0.69]). All laboratory values for the best outcome group differed significantly from those in the worst outcome group. Both the best and worst outcome group curves revealed little overlap with and marked divergence from the reference range. Conclusions and Relevance: The standard reference ranges obtained from healthy volunteers differ from the analogous range generated from data from patients in intensive care. Laboratory data interpretation may benefit from greater consideration of clinically contextual and outcomes-related factors.
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