Evidence-Based Medicine
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. Aug 4, 2017; 6(3): 172-178
Published online Aug 4, 2017. doi: 10.5492/wjccm.v6.i3.172
Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics
Jörg Peter, Wilfried Klingert, Kathrin Klingert, Karolin Thiel, Daniel Wulff, Alfred Königsrainer, Wolfgang Rosenstiel, Martin Schenk
Jörg Peter, Wolfgang Rosenstiel, Department of Computer Engineering, University of Tübingen, 72076 Tübingen, Germany
Wilfried Klingert, Karolin Thiel, Daniel Wulff, Alfred Königsrainer, Martin Schenk, Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, 72076 Tübingen, Germany
Kathrin Klingert, Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, 72076 Tübingen, Germany
Author contributions: All authors contributed to this manuscript.
Conflict-of-interest statement: There are no conflicts of interest arising from this work.
Data sharing statement: No further data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Dr. Martin Schenk, Department of General, Visceral and Transplant Surgery, University Hospital Tübingen, Paul-Ehrlich-Straße 36, 72076 Tübingen, Germany. martin.schenk@med.uni-tuebingen.de
Telephone: +49-7071-2982968
Received: February 16, 2017
Peer-review started: February 17, 2017
First decision: April 14, 2017
Revised: May 2, 2017
Accepted: May 12, 2017
Article in press: May 15, 2017
Published online: August 4, 2017
Processing time: 166 Days and 14.1 Hours
Abstract
AIM

To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample dating.

METHODS

Blood pressure information obtained from a patient monitor was fed as a real-time data stream to an experimental medical framework. This framework was connected to an analytical application which observes changes in systolic, diastolic and mean pressure to determine anomalies in the continuous data stream. Detection was based on an increased mean blood pressure caused by the closing of the withdrawal three-way tap and an absence of systolic and diastolic measurements during this manipulation. For evaluation of the proposed algorithm, measured data from animal studies in healthy pigs were used.

RESULTS

Using this novel approach for processing real-time measurement data of arterial pressure monitoring, the exact time of blood withdrawal could be successfully detected retrospectively and in real-time. The algorithm was able to detect 422 of 434 (97%) blood withdrawals for blood gas analysis in the retrospective analysis of 7 study trials. Additionally, 64 sampling events for other procedures like laboratory and activated clotting time analyses were detected. The proposed algorithm achieved a sensitivity of 0.97, a precision of 0.96 and an F1 score of 0.97.

CONCLUSION

Arterial blood pressure monitoring data can be used to perform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety.

Keywords: Blood withdrawal detection; Sample dating algorithm; Arterial blood gas analysis; Patient monitoring; Point-of-care diagnostics

Core tip: Blood samplings for point-of-care analysis are essential procedures performed in large quantities in hospital wards every day. Whereas many guidelines and good practices exist, human error may still occur and additional safeguards are needed to avoid mix-ups. Using data from arterial blood pressure monitoring, which regularly is present in critical patients for whom errors would be most severe, different features, even the absence of information, may be used for analysis. We developed a novel approach accounting for lack of data in arterial blood pressure monitoring to determine the exact time of blood withdrawal for better sample dating and patient identification.