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001040923 1001_ $$0P:(DE-HGF)0$$aBreteler, Martine J. M.$$b0$$eCorresponding author
001040923 245__ $$aReliability of an all-in-one wearable sensor for continuous vital signs monitoring in high-risk patients: the NIGHTINGALE clinical validation study
001040923 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V.$$c2025
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001040923 520__ $$aContinuous vital signs monitoring with wearable systems may improve early recognition of patient deterioration on hospital wards. The objective of this study was to determine whether the wearable Checkpoint Cardio’s CPC12S, can accurately measure heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), blood pressure (BP) and temperature continuously. In an observational multicenter method comparison study of 70 high-risk surgical patients admitted to high-dependency wards; HR, RR, SpO2, BP and temperature were simultaneously measured with the CPC12S system and with ICU-grade monitoring systems in four European hospitals. Outcome measures were bias and 95% limits of agreement (LoA). Clinical accuracy was assessed with Clarke Error Grid analyses for HR and RR. A total of 3,212 h of vital signs data (on average 26 h per patient) were analyzed. For HR, bias (95% LoA) of the pooled analysis was 0.0 (-3.5 to 3.4), for RR 1.5 (-3.7 to 7.5) and for SpO2 0.4 (-3.1 to 4.0). The CPC12S system overestimated BP, with a bias of 8.9 and wide LoA (-23.3 to 41.2). Temperature was underestimated with a bias of -0.6 and LoA of -1.7 to 0.6. Clarke Error Grid analyses showed that adequate treatment decisions regarding changes in HR and RR would have been made in 99.2% and 92.0% of cases respectively. The CPC12S system showed high accuracy for measurements of HR. The accuracy of RR, SpO2 were slightly overestimated and core temperature underestimated, with LoA outside the predefined clinical acceptable range. The accuracy of BP was unacceptably low.
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001040923 536__ $$0G:(EU-Grant)727534$$aNIGHTINGALE - Connecting Patients and Carers using wearable sensor technology (727534)$$c727534$$fH2020-SC1-2016-CNECT$$x2
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001040923 7001_ $$0P:(DE-HGF)0$$aLeigard, Ellen$$b1
001040923 7001_ $$0P:(DE-HGF)0$$aHartung, Lisa C.$$b2
001040923 7001_ $$0P:(DE-HGF)0$$aWelch, John R.$$b3
001040923 7001_ $$0P:(DE-HGF)0$$aBrealey, David A.$$b4
001040923 7001_ $$0P:(DE-Juel1)185651$$aFritsch, Sebastian J.$$b5$$ufzj
001040923 7001_ $$0P:(DE-HGF)0$$aKonrad, David$$b6
001040923 7001_ $$0P:(DE-HGF)0$$aHertzberg, Daniel$$b7
001040923 7001_ $$0P:(DE-HGF)0$$aBell, Max$$b8
001040923 7001_ $$0P:(DE-HGF)0$$aRienstra, Heleen$$b9
001040923 7001_ $$0P:(DE-HGF)0$$aRademakers, Frank E.$$b10
001040923 7001_ $$0P:(DE-HGF)0$$aKalkman, Cor J.$$b11
001040923 773__ $$0PERI:(DE-600)2010139-9$$a10.1007/s10877-025-01279-x$$p1087–1100$$tJournal of clinical monitoring and computing$$v39$$x0748-1977$$y2025
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