Evidence map›Paper›PMID 28342442›Full record

ArticleCritical care (London, England)2017

Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit.

Eli J Finkelsztein, Daniel S Jones, Kevin C Ma, Maria A Pabón, Tatiana Delgado, Kiichi Nakahira, John E Arbo, David A Berlin, Edward J Schenck, Augustine M K Choi and 1 more

Abstract read
In one paragraph

Article in Critical care (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 7 pooled it
–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

111 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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51 more citing papers are in PubMed but not listed here.

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

11 authors.

Eli J FinkelszteinDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Daniel S JonesDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Kevin C MaDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Maria A PabónDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Tatiana DelgadoDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Kiichi NakahiraDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
John E ArboDepartment of Medicine, Division of Emergency Medicine and Pulmonary Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
David A BerlinDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Edward J SchenckDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Augustine M K ChoiDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA.
Ilias I SiemposDepartment of Medicine, Division of Pulmonary and Critical Care Medicine, New York-Presbyterian Hospital-Weill Cornell Medical Center, Weill Cornell Medicine, New York, NY, USA. isiempos@yahoo.com.

Funding

Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Mesenchymal Stromal Cell Conditioning by Carbon MonoxideP01HL108801 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHOI, AUGUSTINE M · 2011 to 2015
$13.8M
Institutional Career Development CoreKL2TR002385 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI YUAN-SHAN ZHU · 2017 to 2026
$13.3M
The Role of HEME Oxygenase in Hyperoxic Lung InjuryR01HL055330 · NHLBI · YALE UNIVERSITY · PI CHOI, AUGUSTINE M, CHOI, MARY E · 1998 to 2021
$6.6M
Clinical and Translational Science CenterKL2TR000458 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI IMPERATO-MCGINLEY, JULIANNE L · 2012 to 2016
$2.1M
NCATS NIH HHS KL2 TR000458NCATS NIH HHS KL2 TR002385NCATS NIH HHS UL1 TR002384NHLBI NIH HHS P01 HL108801NHLBI NIH HHS R01 HL055330
6 · The paper itself

Abstract

backgroundThe Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) Task Force recently introduced a new clinical score termed quick Sequential (Sepsis-related) Organ Failure Assessment (qSOFA) for identification of patients at risk of sepsis outside the intensive care unit (ICU). We attempted to compare the discriminatory capacity of the qSOFA versus the Systemic Inflammatory Response Syndrome (SIRS) score for predicting mortality, ICU-free days, and organ dysfunction-free days in patients with suspicion of infection outside the ICU.

methodsThe Weill Cornell Medicine Registry and Biobank of Critically Ill Patients is an ongoing cohort of critically ill patients, for whom biological samples and clinical information (including vital signs before and during ICU hospitalization) are prospectively collected. Using such information, qSOFA and SIRS scores outside the ICU (specifically, within 8 hours before ICU admission) were calculated. This study population was therefore comprised of patients in the emergency department or the hospital wards who had suspected infection, were subsequently admitted to the medical ICU and were included in the Registry and Biobank.

resultsOne hundred fifty-two patients (67% from the emergency department) were included in this study. Sixty-seven percent had positive cultures and 19% died in the hospital. Discrimination of in-hospital mortality using qSOFA [area under the receiver operating characteristic curve (AUC), 0.74; 95% confidence intervals (CI), 0.66-0.81] was significantly greater compared with SIRS criteria (AUC, 0.59; 95% CI, 0.51-0.67; p = 0.03). The qSOFA performed better than SIRS regarding discrimination for ICU-free days (p = 0.04), but not for ventilator-free days (p = 0.19), any organ dysfunction-free days (p = 0.13), or renal dysfunction-free days (p = 0.17).

conclusionsIn patients with suspected infection who eventually required admission to the ICU, qSOFA calculated before their ICU admission had greater accuracy than SIRS for predicting mortality and ICU-free days. However, it may be less clear whether qSOFA is also better than SIRS criteria for predicting ventilator free-days and organ dysfunction-free days. These findings may help clinicians gain further insight into the usefulness of qSOFA.

Indexed as

Organ Dysfunction ScoresSeverity of Illness IndexAgedAPACHECritical IllnessEmergency Service, HospitalFemaleHospital MortalityHumansMaleMiddle AgedPatients' RoomsPrognosisRegistriesRisk AssessmentSepsisCritical careInfectionMortalityOrgan failureSevere sepsis

Identifiers

PMID28342442
PMCPMC5366240

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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.