Evidence map›Paper›PMID 27515164›Full record

ArticleCritical care (London, England)2016

External validation of a prehospital risk score for critical illness.

Daniel R Kievlan, Christian Martin-Gill, Jeremy M Kahn, Clifton W Callaway, Donald M Yealy, Derek C Angus, Christopher W Seymour

Abstract read
In one paragraph

Article in Critical care (London, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. 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

7 authors.

Daniel R KievlanDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA. kievlandr@upmc.edu.
Christian Martin-GillDepartment of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Jeremy M KahnDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.
Clifton W CallawayDepartment of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Donald M YealyDepartment of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Derek C AngusDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.
Christopher W SeymourDepartment of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.

Funding

Multidisciplinary Training in Critical Care Outcomes ResearchT32HL007820 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Timothy D Girard, Jeremy M Kahn · 1996 to 2026
$6.4M
Pre-hospital identification of high-risk sepsisK23GM104022 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SEYMOUR, CHRISTOPHER WARREN · 2013 to 2016
$674k
NHLBI NIH HHS T32 HL007820NIGMS NIH HHS K23 GM104022
6 · The paper itself

Abstract

backgroundIdentification of critically ill patients during prehospital care could facilitate early treatment and aid in the regionalization of critical care. Tools to consistently identify those in the field with or at higher risk of developing critical illness do not exist. We sought to validate a prehospital critical illness risk score that uses objective clinical variables in a contemporary cohort of geographically and temporally distinct prehospital encounters.

methodsWe linked prehospital encounters at 21 emergency medical services (EMS) agencies to inpatient electronic health records at nine hospitals in southwestern Pennsylvania from 2010 to 2012. The primary outcome was critical illness during hospitalization, defined as an intensive care unit stay with delivery of organ support (mechanical ventilation or vasopressor use). We calculated the prehospital risk score using demographics and first vital signs from eligible EMS encounters, and we tested the association between score variables and critical illness using multivariable logistic regression. Discrimination was assessed using the AUROC curve, and calibration was determined by plotting observed versus expected events across score values. Operating characteristics were calculated at score thresholds.

resultsAmong 42,550 nontrauma, non-cardiac arrest adult EMS patients, 1926 (4.5 %) developed critical illness during hospitalization. We observed moderate discrimination of the prehospital critical illness risk score (AUROC 0.73, 95 % CI 0.72-0.74) and adequate calibration based on observed versus expected plots. At a score threshold of 2, sensitivity was 0.63 (95 % CI 0.61-0.75), specificity was 0.73 (95 % CI 0.72-0.73), negative predictive value was 0.98 (95 % CI 0.98-0.98), and positive predictive value was 0.10 (95 % CI 0.09-0.10). The risk score performance was greater with alternative definitions of critical illness, including in-hospital mortality (AUROC 0.77, 95 % CI 0.7 -0.78).

conclusionsIn an external validation cohort, a prehospital risk score using objective clinical data had moderate discrimination for critical illness during hospitalization.

Indexed as

Injury Severity ScoreAgedAged, 80 and overChi-Square DistributionCohort StudiesCritical IllnessDecision Support TechniquesElectronic Health RecordsEmergency Medical ServicesFemaleHospitalizationHospital MortalityHumansLength of StayLogistic ModelsMaleClinical decision support systemsCritical illnessEmergency medical servicesForecastingPrognosisTriage

Identifiers

PMID27515164
PMCPMC5050704

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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.