An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study.

Full Abstract

INTRODUCTION: The goal of personalised medicine in the intensive care unit (ICU) is to predict which diagnostic tests, monitoring interventions and treatments translate to improved outcomes given the variation between patients. Unfortunately, processes such as gene transcription and drug metabolism are dynamic in the critically ill; that is, information obtained during static non-diseased conditions may have limited applicability. We propose an alternative way of personalising medicine in the ICU on a real-time basis using information derived from the application of artificial intelligence on a high-resolution database. Calculation of maintenance fluid requirement at the height of systemic inflammatory response was selected to investigate the feasibility of this approach. METHODS: The Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) is a database of patients admitted to the Beth Israel Deaconess Medical Center ICU in Boston. Patients who were on vasopressors for more than six hours during the first 24 hours of admission were identified from the database. Demographic and physiological variables that might affect fluid requirement or reflect the intravascular volume during the first 24 hours in the ICU were extracted from the database. The outcome to be predicted is the total amount of fluid given during the second 24 hours in the ICU, including all the fluid boluses administered. RESULTS: We represented the variables by learning a Bayesian network from the underlying data. Using 10-fold cross-validation repeated 100 times, the accuracy of the model in predicting the outcome is 77.8%. The network generated has a threshold Bayes factor of seven representing the posterior probability of the model given the observed data. This Bayes factor translates into p < 0.05 assuming a Gaussian distribution of the variables. CONCLUSIONS: Based on the model, the probability that a patient would require a certain range of fluid on day two can be predicted. In the presence of a larger database, analysis may be limited to patients with identical clinical presentation, demographic factors, co-morbidities, current physiological data and those who did not develop complications as a result of fluid administration. By better predicting maintenance fluid requirements based on the previous day's physiological variables, one might be able to prevent hypotensive episodes requiring fluid boluses during the course of the following day.

Author information

Author/s: Celi, Leo Anthony (LA); Hinske, L Christian (LC); Alterovitz, Gil (G); Szolovits, Peter (P);

Affiliation: Laboratory of Computer Science, Massachusetts General Hospital, 50 Staniford Street, 7th floor, Boston, MA 02114, USA. lceli(-atsign-)mit.edu

Grants: 2T15LM007092-16 (Agency:NLM NIH HHS) ; P41 RR013622-05S10056 (Agency:NCRR NIH HHS) ; P41 RR013622-076024 (Agency:NCRR NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Critical care (London, England) (Crit Care), published in England. (Language: eng)

Reference: 2008-; vol 12 (issue 6) : pp R151

Dates: Created 2009/02/23; Completed 2009/05/27; Revised 2009/06/01;

PMID: 19046450, status: MEDLINE (last retrieval date: 6/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

Comments and Corrections

CommentIn: Crit Care. 2009;13(1):111. (PMID: 19232073)

External Links for this article
(including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.

MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Associated Chemicals: Vasoconstrictor Agents (0)

This article has not been indexed for related articles as yet, however you can still use the live related article search links below.

See 100+ related articles.

See a large map of 100+ related articles.