Evidence map›Paper›PMID 31984322›Full record

ArticleJAMIA open2018

An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes: An observational cohort study.

Robert C Amland, Bharat B Sutariya

Abstract read
In one paragraph

Article in JAMIA open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Robert C AmlandPopulation Health, Cerner Corporation, Kansas City, Missouri, USA.ORCID 0000-0002-6830-1294
Bharat B SutariyaPopulation Health, Cerner Corporation, Kansas City, Missouri, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine the prevalence of initiating the sepsis 3-h bundle of care and estimate effects of bundle completion on risk-adjusted mortality among emergency department (ED) patients screened-in by electronic surveillance. MATERIALS AND

methodsThis was a multiple center observational cohort study conducted in 2016. The study population was comprised of patients screened-in by St. John Sepsis Surveillance Agent within 4 h of ED arrival, had a sepsis bundle initiated, and admitted to hospital. We built multivariable logistic regression models to estimate impact of a 3-h bundle completed within 3 h of arrival on mortality outcomes.

resultsApproximately 3% ED patients were screened-in by electronic surveillance within 4 h of arrival and admitted to hospital. Nearly 7 in 10 (69%) patients had a bundle initiated, with most bundles completed within 3 h of arrival. The fully-adjusted risk model achieved good discrimination on mortality outcomes [area under the receiver operating characteristic 0.82, 95% confidence interval (CI) 0.79-0.85] and estimated 34% reduced mortality risk among patients with a bundle completed within 3 h of arrival compared to non-completers. DISCUSSION: The sepsis bundle is an effective intervention for many vulnerable patients, and likely to be completed within 3 h after arrival when electronic surveillance with reliable alert notifications are integrated into clinical workflow. Beginning at triage, the platform and sepsis program enables identification and management of patients with greater precision, and increases the odds of good outcomes.

conclusionSepsis surveillance and clinical decision support accelerate accurate recognition and stratification of patients, and facilitate timely delivery of health care.

Indexed as

clinicalcloud computingdecision supportearly diagnosisemergency servicesepidemiologyhospitalsepsis

Identifiers

PMID31984322
PMCPMC6951936

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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.