Radarbasierte Atemerfassung auf einer Palliativstation

Zusammenfassung

Ziel der Studie

In der Palliativversorgung werden technische Hilfsmittel wie EKG oder Pulsoxymetrie nur sparsam eingesetzt, um Zuwendung und Beziehungsarbeit nicht zu stören. Gleichzeitig ist Symptomerkennung essenziell und viele Patient*innen können diese nicht selbst äußern. Diese Studie untersucht erstmals den Einsatz eines Radarsystems zum berührungslosen Atemfrequenzmonitoring bei Palliativpatient*innen.

Methode

In einer prospektiven Studie wurde radarbasiertes Atemfrequenzmonitoring mit einer Goldstandardmethode bei stationär behandelten Palliativpatient*innen verglichen.

Ergebnisse

Die Auswertung bei 13 Patient*innen zeigte eine Übereinstimmung der Atemfrequenzerfassungen durch Radar und Gold-Standardverfahren (n=13, Korrelation r=0,93; 88% der Atemfrequenzen innerhalb ±2 Atemzügen im Vergleich zu Goldstandard). Verlaufsanalysen ergaben Hinweise auf ein Potenzial zur Symptomerkennung.

Schlussfolgerung

Radarbasiertes Atemfrequenzmonitoring unter klinischen Bedingungen ist zuverlässig und hat das Potenzial, Symptome zu erkennen.

Abstract

Objective

In palliative care, technical aids such as ECG are employed sparingly to avoid interference with the provision of care. At the same time, it is vital to recognise symptoms, whereas many patients are unable to express themselves. This study is the first investigation into a radar-based, non-contact monitoring of respiratory rate in palliative care patients.

Methods

In a prospective study, radar-based monitoring of respiratory rate was evaluated in comparison with a gold standard method in a cohort of hospitalised palliative care patients.

Results

The results demonstrated a high level of agreement between radar-based and gold standard respiratory rate monitoring (n=13, correlation r=0.93, 88% of respiratory rates within ±2 breaths). Further analyses indicated a potential for symptom recognition.

Conclusion

The findings suggest that radar-based respiratory rate monitoring is reliable under clinical conditions and has the potential to detect symptoms.

Schlüsselwörter

Digitalisierung - Radarsensor - Atemfrequenz

Keywords

digitalization - radar sensor - breathing frequency

Publication History

Received: 06 November 2024

Accepted after revision: 17 March 2025

Article published online:
28 April 2025

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