Algorithm-based arterial blood sampling recognition increasing safety in point-of-care diagnostics
| Autor: | Wilfried Klingert, Karolin Thiel, Jörg Peter, Kathrin Klingert, Daniel Wulff, Martin Schenk, Wolfgang Rosenstiel, Alfred Königsrainer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Patient monitoring
medicine.medical_specialty Evidence-Based Medicine Remote patient monitoring business.industry Point-of-care testing Arterial blood gas analysis Point-of-care diagnostics 030204 cardiovascular system & hematology 03 medical and health sciences 0302 clinical medicine Arterial blood sampling Sample dating algorithm medicine Blood withdrawal detection 030212 general & internal medicine Intensive care medicine business Algorithm |
| Zdroj: | World Journal of Critical Care Medicine |
| ISSN: | 2220-3141 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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