Evidence map›Paper›PMID 38924010›Full record

ArticlePloS one2024

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score.

Alex M Cressman, Bijun Wen, Sudipta Saha, Hae Young Jun, Riley Waters, Sharan Lail, Aneela Jabeen, Radha Koppula, Lauren Lapointe-Shaw, Kathleen A Sheehan and 5 more

Abstract readMulticenter Study
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Alex M CressmanTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-8226-4709
Bijun WenLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Sudipta SahaLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Hae Young JunLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Riley WatersLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Sharan LailUnity Health Toronto, Toronto, Ontario, Canada.
Aneela JabeenLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Radha KoppulaLi Ka Shing Knowledge Institute, Toronto, Ontario, Canada.
Lauren Lapointe-ShawTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.
Kathleen A SheehanTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-8700-0809
Adina WeinermanTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.
Nick DanemanTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.
Amol A VermaTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-6857-931X
Fahad RazakTemerty Faculty of Medicine, University of Toronto, Toronto, Toronto, Ontario, Canada.
Derek MacFaddenThe Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCurrent scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort.

designA simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study.

settingInternal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). PATIENTS: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. MEASUREMENTS AND MAIN

resultsThe primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality.

conclusionsAn EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Indexed as

Electronic Health RecordsHospital MortalitySepsisSeverity of Illness IndexAgedAged, 80 and overFemaleHumansIntensive Care UnitsLength of StayMaleMiddle AgedRetrospective StudiesROC Curve

Identifiers

PMID38924010
PMCPMC11206954

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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.