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Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics

World Journal of Critical Care Medicine 2017年第3期6卷 172-178页

作者： Jorg Peter Wilfried Klingert Kathrin Klingert Karolin Thiel Daniel Wulff Alfred Konigsrainer Wolfgang Rosenstiel Martin Schenk Department of Computer Engineering University of Tübingen Department of General Visceral and Transplant Surgery University Hospital Tübingen Department of Anesthesiology and Intensive Care Medicine University Hospital Tübingen

AIM To detect blood withdrawal for patients with arterial blood pressure monitoring to increase patient safety and provide better sample *** 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 *** 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 *** Arterial blood pressure monitoring data can be used toperform an accurate identification of individual blood samplings in order to reduce sample mix-ups and thereby increase patient safety.

关键词： blood withdrawal detection Sample dating algorithm Arterial blood gas analysis Patient monitoring Point-of-care diagnostics

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