Evidence map›Paper›PMID 35794669›Full record

ArticlePilot and feasibility studies2022

A pilot study to investigate real-time digital alerting from wearable sensors in surgical patients.

Meera Joshi, Hutan Ashrafian, Sonal Arora, Mansour Sharabiani, Kenny McAndrew, Sadia N Khan, Graham S Cooke, Ara Darzi

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Pilot and feasibility studies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04638738 (Implementation of Wearable Sensors and Digital Alerting Systems in Secondary Care), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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.

NCT04638738 unknown statusnot on this map

Implementation of Wearable Sensors and Digital Alerting Systems in Secondary Care: a Prospective Evaluation of Clinical Outcomes

TypeobservationalSponsorImperial College LondonRan2017 to 2021Enrolled650ConditionsSepsis, Deterioration, ClinicalArmsSensiumVitals wearable patch sensor
3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 11 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Meera JoshiDepartment of Surgery and Cancer, Imperial College London, 10th Floor Academic Surgical Unit, QEQM Building, St Mary's Hospital, Praed Street, Paddington, London, W2 1NY, UK. meera.joshi03@imperial.ac.uk.ORCID http://orcid.org/0000-0003-4417-1437
Hutan AshrafianDepartment of Surgery and Cancer, Imperial College London, 10th Floor Academic Surgical Unit, QEQM Building, St Mary's Hospital, Praed Street, Paddington, London, W2 1NY, UK.
Sonal AroraChelsea and Westminster Hospital, NHS Foundation Trust, London, UK.
Mansour SharabianiDepartment of Primary Care & Public Health, School of Public Health, Imperial College London, London, UK.
Kenny McAndrewChelsea and Westminster Hospital, NHS Foundation Trust, London, UK.
Sadia N KhanChelsea and Westminster Hospital, NHS Foundation Trust, London, UK.
Graham S CookeDivision of Infectious Diseases, Imperial College London, London, UK.
Ara DarziDepartment of Surgery and Cancer, Imperial College London, 10th Floor Academic Surgical Unit, QEQM Building, St Mary's Hospital, Praed Street, Paddington, London, W2 1NY, UK.
Chelsea and Westminster Hospital NHS Foundation Trust · GBImperial College London · GBSt Mary's Hospital · GB

Funding

Royal College of Surgeons of England Research Fellowship
6 · The paper itself

Abstract

backgroundContinuous vital sign monitoring may identify changes sooner than current standard monitoring.

objectiveTo investigate if the use of real-time digital alerts sent to healthcare staff can improve the time taken to identify unwell patients and those with sepsis.

designA prospective cohort study design.

settingWest Middlesex University Hospital, UK.

participantsFifty acutely unwell surgical patients admitted to hospital.

interventionPatients wore a lightweight wearable sensor measuring heart rate (HR), respiratory rate (RR) and temperature every 2 min whilst standard intermittent ward monitoring of vital signs was performed by nurses. Digital alerts were sent to healthcare staff from the sensor to a smartphone device. All alerts were reviewed for recruited patients to identify the exact time on the sensor in which deterioration occurred. The time to acknowledgement was then reviewed for each action and an average time to acknowledgement calculated.

resultsThere were 50 patients recruited in the pilot study, of which there were vital sign alerts in 18 patients (36%). The total number of vital sign alerts generated in these 18 patients was 51. Of these 51 alerts, there were 7 alerts for high HR (13.7%), 33 for RR (64.7%) and 11 for temperature (21.6%). Out of the 27 acknowledged alerts, there were 2 alerts for HR, 17 for RR and 8 for temperature. The average time to staff acknowledgement of the notification for all alerts was 154 min (2.6 h). There were some patients which had shown signs of deterioration in the cohort. The frequency of routine observation monitoring was increased in 2 cases, 3 patients were referred to a senior clinician and 2 patients were initiated on the sepsis pathway.

conclusionThis study demonstrates the evaluation of digital alerts to nurses in real time. Although not all alerts were acknowledged, deterioration on the ward observations was detected and actions were taken accordingly. Patients were started on the sepsis pathway and escalation to senior clinicians occurred. Further research is required to review why only some alerts were acknowledged and the effects of digital alerting on patient outcomes.

trial registrationClinicalTrials.gov, NCT04638738.

Indexed as

Continuous monitoringVital signsWearable sensors

Identifiers

PMID35794669
PMCPMC9258087
OpenAlexW4283824653

What OpenQuestion holds

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LicenceCC BY
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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.