Introduction
Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes. Our aim is to reduce unnecessary blood draws for ICU laboratory tests by predicting which tests are likely to return as normal or abnormal and therefore influence clinical management around gastrointestinal (GI) bleeding.
Methods
An artificial intelligence tool, namely fuzzy systems, was applied to 1,092 GI bleed patients extracted from a large ICU database with over 32,000 patients. A classification approach for laboratory test outcome was utilized for a total of seven outcome variables shown in Table 1. The outcome for each test was binarized as normal or abnormal. Input variables included 10 physiological variables such as heart rate, temperature and urine output, as well as further data on transfusions for platelets, red blood cells and plasma.
Results
Classification accuracy of greater than 80% was achieved for all of the seven outcome variables (Table 1). Sensitivity and specificity were satisfactory for all the outcomes. Input variables frequently selected as most predictive of normal or abnormal results include urine output and red blood cell transfusion.
Conclusion
Reducing frequent laboratory testing, and potential phlebotomy complications, is a major concern in critical care medicine. If one could predict in advance whether a laboratory test would be normal or abnormal then that particular laboratory test may not be ordered, and thereby reducing potential complications and costs. In this work we present an artificial intelligence method for the classifying the likelihood of a blood test being normal or abnormal. Our results show acceptable classification accuracy both in terms of sensitivity and specificity.
Author information
Authors and Affiliations
Rights and permissions
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Cismondi, F., Fialho, A., Vieira, S. et al. Reducing ICU blood draws with artificial intelligence. Crit Care 16 (Suppl 1), P436 (2012). https://doi.org/10.1186/cc11043
Published:
DOI: https://doi.org/10.1186/cc11043