Evidence map›Paper›PMID 37827821›Full record

Trial reportEmergency medicine journal : EMJ2023

Impact on all-cause mortality of a case prediction and prevention intervention designed to reduce secondary care utilisation: findings from a randomised controlled trial.

Lucy M Bull, Bartlomiej Arendarczyk, Sara Reis, An Nguyen, Joachim Werr, Thomas Lovegrove-Bacon, Mark Stone, Christopher Sherlaw-Johnson

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Emergency medicine journal : EMJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Lucy M BullModelling and Insights, Health Navigator, London, UK lucy.bull@hn-company.co.uk.ORCID http://orcid.org/0000-0002-3926-3030
Bartlomiej ArendarczykModelling and Insights, Health Navigator, London, UK.
Sara ReisModelling and Insights, Health Navigator, London, UK.
An NguyenData Science and Strategy, Health Navigator, London, UK.
Joachim WerrFounder and Chair, Health Navigator, London, UK.
Thomas Lovegrove-BaconStrategic Development, East Kent Hospitals University NHS Foundation Trust, Canterbury, UK.
Mark StoneNorth Place Clinical Lead, Staffordshire and Stoke ICB, Stafford, UK.
Christopher Sherlaw-JohnsonQuality and Performance, Nuffield Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth coaching services could help to reduce emergency healthcare utilisation for patients targeted proactively by a clinical prediction model (CPM) predicting patient likelihood of future hospitalisations. Such interventions are designed to empower patients to confidently manage their own health and effectively utilise wider resources. Using CPMs to identify patients, rather than prespecified criteria, accommodates for the dynamic hospital user population and for sufficient time to provide preventative support. However, it is unclear how this care model would negatively impact survival.

methodsEmergency Department (ED) attenders and hospital inpatients between 2015 and 2019 were automatically screened for their risk of hospitalisation within 6 months of discharge using a locally trained CPM on routine data. Those considered at risk and screened as suitable for the intervention were contacted for consent and randomised to one-to-one telephone health coaching for 4-6 months, led by registered health professionals, or routine care with no contact after randomisation. The intervention involved motivational guidance, support for self-care, health education, and coordination of social and medical services. Co-primary outcomes were emergency hospitalisation and ED attendances, which will be reported separately. Mortality at 24 months was a safety endpoint.

resultsAnalysis among 1688 consented participants (35% invitation rate from the CPM, median age 75 years, 52% female, 1139 intervention, 549 control) suggested no significant difference in overall mortality between treatment groups (HR (95% CI): 0.82 (0.62, 1.08), pr(HR<1=0.92), but did suggest a significantly lower mortality in men aged >75 years (HR (95% CI): 0.57 (0.37, 0.84), number needed to treat=8). Excluding one site unable to adopt a CPM indicated stronger impact for this patient subgroup (HR (95% CI): 0.45 (0.26, 0.76)).

conclusionsEarly mortality in men aged >75 years may be reduced by supporting individuals at risk of unplanned hospitalisation with a clear outreach, out-of-hospital nurse-led, telephone-based coaching care model.

Indexed as

Models, StatisticalSecondary Health CareAgedFemaleHospitalizationHumansMalePatient DischargePrognosisdeathMachine Learningpatient supporturgent careutilisation

Identifiers

PMID37827821
PMCPMC11881050

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Registered trials

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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.