Evidence map›Paper›PMID 26083327›Full record

ReviewCurrent opinion in critical care2015

How can we identify the high-risk patient?

Ashwin Sankar, W Scott Beattie, Duminda N Wijeysundera

Abstract readReview
In one paragraph

Review in Current opinion in critical care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 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

26 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Arquivos brasileiros de cardiologia · 2017
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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

3 authors.

Ashwin SankaraDepartment of Anesthesia, University of Toronto bDepartment of Anesthesia, Toronto General Hospital cLi Ka Shing Knowledge Institute of St. Michael's Hospital dInstitute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
W Scott Beattie
Duminda N Wijeysundera

Funding

Canadian Institutes of Health Research
6 · The paper itself

Abstract

purpose of reviewAccurate and early identification of high-risk surgical patients allows for targeted use of perioperative monitoring and interventions that may improve their outcomes. This review summarizes current evidence on how information from the preoperative, operative, and immediate postoperative periods can help identify such individuals. RECENT

findingsSimple risk indices, such as the Revised Cardiac Risk Index or American Society of Anesthesiologists Physical Status scale, and online calculators allow risk to be estimated with moderate accuracy using readily available preoperative clinical information. Both specific specialized tests (i.e., cardiopulmonary exercise testing and cardiac stress testing) and promising novel biomarkers (i.e., troponins and natriuretic peptides) can help refine these risk estimates before surgery. Estimates of perioperative risk can be further informed by information acquired during the operative and immediate postoperative periods, such as risk indices (i.e., surgical Apgar score), individual risk factors (i.e., intraoperative hypotension), or postoperative biomarkers (i.e., troponins and natriuretic peptides). SUMMARY: Preoperative clinical risk indices and risk calculators estimate surgical risk with moderate accuracy. Although novel biomarkers, specialized preoperative testing, and immediate postoperative risk indices show promise as methods to refine these risk estimates, more research is needed on how best to integrate risk information from these different sources.

Indexed as

Risk AssessmentBiomarkersCritical CareExercise TestHumansNatriuretic PeptidesPostoperative ComplicationsPreoperative CareRisk FactorsSurgical Procedures, OperativeTroponinBiomarkersNatriuretic PeptidesTroponin

Identifiers

PMID26083327
PMCPMC4501694

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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.