Evidence map›Paper›PMID 31171026›Full record

ArticleCritical care (London, England)2019

Examining the weekend effect across ICU performance metrics.

Louis Faust, Keith Feldman, Nitesh V Chawla

Abstract read
In one paragraph

Article in Critical care (London, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Louis FaustDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, USA.
Keith FeldmanDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, USA.
Nitesh V ChawlaDepartment of Computer Science & Engineering, University of Notre Dame, Notre Dame, USA. nchawla@nd.edu.ORCID http://orcid.org/0000-0003-3932-5956

Funding

NetHealth: Modeling the Co-Evolution of Social Networks and Health BehaviorsR01HL117757 · NHLBI · UNIVERSITY OF NOTRE DAME · PI HACHEN, DAVID S. · 2014 to 2017
$2.9M
NHLBI NIH HHS R01 HL117757NHLBI NIH HHS R01HL117757
6 · The paper itself

Abstract

backgroundKnown colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests. Rather, to truly understand the effect, investigations into its underlying factors must be considered. As such, the work presented in this manuscript serves to address this consideration by moving beyond identification of patient cohorts to examining the role of ICU performance.

methodsEmploying a comprehensive, publicly available database of electronic medical records (EMR), we began by utilizing multiple logistic regression to identify and isolate a specific cohort in which the weekend effect was present. Next, we leveraged the highly detailed nature of the EMR to evaluate ICU performance using well-established ICU quality scorecards to assess differences in clinical factors among patients admitted to an ICU on the weekend versus weekday.

resultsOur results demonstrate the weekend effect to be most prevalent among emergency surgery patients (OR 1.53; 95% CI 1.19, 1.96), specifically those diagnosed with circulatory diseases (P<.001). Differences between weekday and weekend admissions for this cohort included a variety of clinical factors such as ventilatory support and night-time discharges.

conclusionsThis work reinforces the importance of accounting for differences in clinical factors as well as patient cohorts in studies investigating the weekend effect.

Indexed as

Time FactorsAdultAgedAged, 80 and overFemaleHospitalizationHospital MortalityHumansIntensive Care UnitsLogistic ModelsMaleMiddle AgedQuality Indicators, Health CareQuality of Health CareRisk FactorsHealthcareHealthcare informaticsWeekend effect

Identifiers

PMID31171026
PMCPMC6554947

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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.