Evidence map›Paper›PMID 39380078›Full record

ArticleArchives of public health = Archives belges de sante publique2024

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.

J J Reichl, M Leifke, S Wehrli, D Kunz, L Geissmann, S Broisch, M Illien, D Wellauer, N von Dach, S Diener and 6 more

Registry-linked trialAbstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05699967 (Pilot Project 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), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05699967 completednot on this map

Pilot Project 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

TypeobservationalSponsorUniversity Hospital, Basel, SwitzerlandRan2023 to 2023Enrolled210ConditionsDeterioration of Patient's State of HealthArmsdata collection
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

J J ReichlDepartment of Internal Medicine, University Hospital of Basel, Petersgraben 4, CH-4031, Basel, Switzerland.
M LeifkeInnovationmanagement, University of Basel, Basel, Switzerland.
S WehrliSchool of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Group Biosensor Analysis and Digital Health, Zurich, Switzerland.
D KunzSchool of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Group Biosensor Analysis and Digital Health, Zurich, Switzerland.
L GeissmannLeitwert AG, Zurich, Switzerland.
S BroischInnovationmanagement, University of Basel, Basel, Switzerland.
M IllienInnovationmanagement, University of Basel, Basel, Switzerland.
D WellauerInnovationmanagement, University of Basel, Basel, Switzerland.
N von DachInnovationmanagement, University of Basel, Basel, Switzerland.
S DienerInnovationmanagement, University of Basel, Basel, Switzerland.
V ManserInnovationmanagement, University of Basel, Basel, Switzerland.
V HerrenInnovationmanagement, University of Basel, Basel, Switzerland.
A AngererSchool of Management and Law, Zurich University of Applied Sciences, Head of Management in Health Care, Zurich, Switzerland.
S HirschSchool of Life Sciences and Facility Management, Zurich University of Applied Sciences, Research Centre for Computational Health, Zurich, Switzerland.
B HölzInnovationmanagement, University of Basel, Basel, Switzerland.
J EcksteinDepartment of Internal Medicine, University Hospital of Basel, Petersgraben 4, CH-4031, Basel, Switzerland. jens.eckstein@usb.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute 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.

methodsWe 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.

resultsOf 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.

conclusionsIn 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 registrationJanuary 26, 2023, NCT05699967.

Indexed as

Continuous monitoringEarly Warning ScoresWearables

Identifiers

PMID39380078
PMCPMC11459982

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.