Evidence map›Paper›PMID 28751970›Full record

ArticleF1000Research2017

Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury.

Alistair Connell, Hugh Montgomery, Stephen Morris, Claire Nightingale, Sarah Stanley, Mary Emerson, Gareth Jones, Omid Sadeghi-Alavijeh, Charles Merrick, Dominic King and 7 more

Abstract read
In one paragraph

Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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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

17 authors.

Alistair ConnellCentre for Human Health and Performance, University College London, 170 Tottenham Court Road, London, W1T 7HA, UK.ORCID https://orcid.org/0000-0003-0318-1049
Hugh MontgomeryCentre for Human Health and Performance, University College London, 170 Tottenham Court Road, London, W1T 7HA, UK.
Stephen MorrisDepartment of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Claire NightingaleDepartment of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Sarah StanleyRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Mary EmersonRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Gareth JonesRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Omid Sadeghi-AlavijehRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Charles MerrickRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.
Dominic KingDeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
Alan KarthikesalingamDeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
Cian HughesDeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.ORCID https://orcid.org/0000-0001-6901-0985
Joseph LedsamDeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
Trevor BackDeepMind Health, 5 New Street Square, London, EC4A 3TW, UK.
Geraint ReesUniversity College London, Gower Street, London, WC1E 6BT, UK.
Rosalind RaineDepartment of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Christopher LaingRoyal Free London NHS Foundation Trust, Pond Street, London, NW3 2QG, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute Kidney Injury (AKI), an abrupt deterioration in kidney function, is defined by changes in urine output or serum creatinine. AKI is common (affecting up to 20% of acute hospital admissions in the United Kingdom), associated with significant morbidity and mortality, and expensive (excess costs to the National Health Service in England alone may exceed £1 billion per year). NHS England has mandated the implementation of an automated algorithm to detect AKI based on changes in serum creatinine, and to alert clinicians. It is uncertain, however, whether 'alerting' alone improves care quality. We have thus developed a digitally-enabled care pathway as a clinical service to inpatients in the Royal Free Hospital (RFH), a large London hospital. This pathway incorporates a mobile software application - the "Streams-AKI" app, developed by DeepMind Health - that applies the NHS AKI algorithm to routinely collected serum creatinine data in hospital inpatients. Streams-AKI alerts clinicians to potential AKI cases, furnishing them with a trend view of kidney function alongside other relevant data, in real-time, on a mobile device. A clinical response team comprising nephrologists and critical care nurses responds to these AKI alerts by reviewing individual patients and administering interventions according to existing clinical practice guidelines. We propose a mixed methods service evaluation of the implementation of this care pathway. This evaluation will assess how the care pathway meets the health and care needs of service users (RFH inpatients), in terms of clinical outcome, processes of care, and NHS costs. It will also seek to assess acceptance of the pathway by members of the response team and wider hospital community. All analyses will be undertaken by the service evaluation team from UCL (Department of Applied Health Research) and St George's, University of London (Population Health Research Institute).

Indexed as

acute kidney injuryAKIe-alertnephrology

Identifiers

PMID28751970
PMCPMC5510018

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.