AI Article Synopsis
- Abnormal vital parameter values, like low blood pressure or rapid heart rate, can happen during anesthesia and can be tracked using data from Anesthesia Information Management Systems.
- When combined with data from Hospital Information Systems, these abnormalities are associated with complications after surgery, but the methods for detecting them automatically aren't well-documented and vary across studies.
- This paper proposes a new algorithm for automatically detecting abnormal vital signs, allowing settings for threshold values and handling missing data, and it demonstrates its application on heart rate, SpO2, and mean arterial pressure from 2014 anesthesia records.
Article Abstract
Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.
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| http://dx.doi.org/10.1016/j.cmpb.2016.01.004 | DOI Listing |
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