Evidence map›Paper›PMID 34328843›Full record

ArticleJournal of hospital medicine2021

Identifying the Sickest During Triage: Using Point-of-Care Severity Scores to Predict Prognosis in Emergency Department Patients With Suspected Sepsis.

Priya A Prasad, Margaret C Fang, Sandra P Martinez, Kathleen D Liu, Kirsten N Kangelaris

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of hospital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. A machine learning model to predict optimal antibiotic use in hospital medicine patients.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025
    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

5 authors at 1 institution in 1 country.

Priya A PrasadDivision of Hospital Medicine, Department of Medicine, University of California, San Francisco, California.
Margaret C FangDivision of Hospital Medicine, Department of Medicine, University of California, San Francisco, California.
Sandra P MartinezDivision of Hospital Medicine, Department of Medicine, University of California, San Francisco, California.
Kathleen D LiuDivisions of Nephrology and Critical Care Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California.
Kirsten N KangelarisDivision of Hospital Medicine, Department of Medicine, University of California, San Francisco, California.
University of California, San Francisco · US

Funding

Advancing acute kidney injury phenotyping using biological and clinical criteriaK24DK113381 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LIU, KATHLEEN D · 2017 to 2021
$915k
Use and outcomes of anticoagulants for the treatment and prevention of thrombosis among hospitalized patientsK24HL141354 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FANG, MARGARET C. · 2018 to 2022
$731k
NHLBI NIH HHS K24 HL141354NIDDK NIH HHS K24 DK113381
6 · The paper itself

Abstract

backgroundSepsis progresses rapidly and is associated with considerable morbidity and mortality. Bedside risk stratification scores can quickly identify patients at greatest risk of poor outcomes; however, there is lack of consensus on the best scale to use.

objectiveTo compare the ability of quick Sequential Organ Failure Assessment (qSOFA), the National Early Warning System (NEWS2), and the Shock Index-which does not require mental status assessment-to predict poor outcomes among patients with suspected sepsis during triage. DESIGN, SETTING, AND

participantsRetrospective cohort study of adults presenting to an academic emergency department (ED) from June 2012 to December 2018 who had blood cultures and intravenous antibiotics within 24 hours. MAIN OUTCOMES AND MEASURES: Clinical data were collected from the electronic health record. Patients were considered positive at qSOFA ≥2, Shock Index >0.7, or NEWS2 ≥5 scores. We calculated test characteristics and area under the receiver operating characteristics curves (AUROCs) to predict in-hospital mortality and ED-to-intensive care unit (ICU) admission.

resultsWe included 23,837 ED patients; 1,921(8.1%) were qSOFA-positive, 4,273 (17.9%) Shock Index-positive, and 11,832 (49.6%) NEWS2-positive. There were 1,427 (6.0%) deaths and 3,149 (13.2%) ED-to-ICU admissions in the sample. NEWS2 had the highest sensitivity for in-hospital mortality (76.0%) and ED-to-ICU admission (78.9%). qSOFA had the highest specificity for in-hospital mortality (93.4%) and ED-to-ICU admission (95.2%). Shock Index exhibited the highest AUROC for in-hospital mortality (0.648; 95 CI, 0.635-0.662) and ED-to-ICU admission (0.680; 95% CI, 0.617-0.689). Test characteristics were similar among those with sepsis.

conclusionsInstitution priorities should drive score selection, balancing sensitivity and specificity. In our study, qSOFA was highly specific and NEWS2 was the most sensitive for ruling out patients at high risk. Performance of the Shock Index fell between qSOFA and NEWS2 and could be considered because it is easy to implement.

Indexed as

SepsisTriageAdultEmergency Service, HospitalHospital MortalityHumansOrgan Dysfunction ScoresPoint-of-Care SystemsPrognosisRetrospective StudiesROC Curve

Identifiers

PMID34328843
PMCPMC8340957
OpenAlexW3183804042

What OpenQuestion holds

Textmetadata
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.