ArticleMethods of information in medicine2013
Disease-based modeling to predict fluid response in intensive care units.
Article in Methods of information in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Machine learning in critical care: state-of-the-art and a sepsis case study.Biomedical engineering online · 2018Observational
- Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- Predicting intervention onset in the ICU with switching state space models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Machine Learning and Decision Support in Critical Care.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2016Article
- Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.TheScientificWorldJournal · 2015Article
- Dynamic clinical data mining: search engine-based decision support.JMIR medical informatics · 2014Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units.
methodsRetrospective cohort study involving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling.
resultsPatients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 ± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients).
conclusionsDisease-based predictive modeling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.