Pilot study for the development of an automatically generated and wearable-based early warning system for the detection of deterioration of hospitalized patients of an acute care hospital.

Autor: Reichl JJ; Department of Internal Medicine, University Hospital of Basel, Petersgraben 4, CH-4031, Basel, Switzerland.; Innovationmanagement, University of Basel, Basel, Switzerland., Leifke M; Innovationmanagement, University of Basel, Basel, Switzerland., Wehrli S; School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Group Biosensor Analysis and Digital Health, Zurich, Switzerland., Kunz D; School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Group Biosensor Analysis and Digital Health, Zurich, Switzerland., Geissmann L; Leitwert AG, Zurich, Switzerland., Broisch S; Innovationmanagement, University of Basel, Basel, Switzerland., Illien M; Innovationmanagement, University of Basel, Basel, Switzerland., Wellauer D; Innovationmanagement, University of Basel, Basel, Switzerland., von Dach N; Innovationmanagement, University of Basel, Basel, Switzerland., Diener S; Innovationmanagement, University of Basel, Basel, Switzerland., Manser V; Innovationmanagement, University of Basel, Basel, Switzerland., Herren V; Innovationmanagement, University of Basel, Basel, Switzerland., Angerer A; School of Management and Law, Zurich University of Applied Sciences, Head of Management in Health Care, Zurich, Switzerland., Hirsch S; School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Centre for Computational Health, Zurich, Switzerland., Hölz B; Innovationmanagement, University of Basel, Basel, Switzerland., Eckstein J; Department of Internal Medicine, University Hospital of Basel, Petersgraben 4, CH-4031, Basel, Switzerland. jens.eckstein@usb.ch.; Innovationmanagement, University of Basel, Basel, Switzerland. jens.eckstein@usb.ch.
Jazyk: angličtina
Zdroj: Archives of public health = Archives belges de sante publique [Arch Public Health] 2024 Oct 08; Vol. 82 (1), pp. 179. Date of Electronic Publication: 2024 Oct 08.
DOI: 10.1186/s13690-024-01409-y
Abstrakt: Background: Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals.
Methods: We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland-Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation.
Results: Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high.
Conclusions: In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods.
Trial Registration: January 26, 2023, NCT05699967.
(© 2024. The Author(s).)
Databáze: MEDLINE
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