Point-of-care Ultrasonography for the Diagnosis of Acute Cardiogenic Pulmonary Edema in Patients Presenting With Acute Dyspnea: A Systematic Review and Meta-analysis

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Abstract

Acute dyspnea is a common presenting complaint to the emergency department (ED), and point-of-care (POC) lung ultrasound (US) has shown promise as a diagnostic tool in this setting. The primary objective of this systematic review was to determine the sensitivity and specificity of US using B-lines in diagnosing acute cardiogenic pulmonary edema (ACPE) in patients presenting to the ED with acute dyspnea.

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Most cited references 31

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QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

Penny F Whiting, Anne W S Rutjes, Marie E Westwood … (2011)

In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.

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Assessing bias in studies of prognostic factors.

Jill Hayden, Danielle van der Windt, Jennifer L Cartwright … (2013)

Previous work has identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors: participation, attrition, prognostic factor measurement, confounding measurement and account, outcome measurement, and analysis and reporting. This article describes the Quality In Prognosis Studies tool, which includes questions related to these areas that can inform judgments of risk of bias in prognostic research.A working group comprising epidemiologists, statisticians, and clinicians developed the tool as they considered prognosis studies of low back pain. Forty-three groups reviewing studies addressing prognosis in other topic areas used the tool and provided feedback. Most reviewers (74%) reported that reaching consensus on judgments was easy. Median completion time per study was 20 minutes; interrater agreement (κ statistic) reported by 9 review teams varied from 0.56 to 0.82 (median, 0.75). Some reviewers reported challenges making judgments across prompting items, which were addressed by providing comprehensive guidance and examples. The refined Quality In Prognosis Studies tool may be useful to assess the risk of bias in studies of prognostic factors.

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Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations.

L E Moses, D. Shapiro, B Littenberg (1993)

We consider how to combine several independent studies of the same diagnostic test, where each study reports an estimated false positive rate (FPR) and an estimated true positive rate (TPR). We propose constructing a summary receiver operating characteristic (ROC) curve by the following steps. (i) Convert each FPR to its logistic transform U and each TPR to its logistic transform V after increasing each observed frequency by adding 1/2. (ii) For each study calculate D = V - U, which is the log odds ratio of TPR and FPR, and S = V + U, an implied function of test threshold; then plot each study's point (Si, Di). (iii) Fit a robust-resistant regression line to these points (or an equally weighted least-squares regression line), with V - U as the dependent variable. (iv) Back-transform the line to ROC space. To avoid model-dependent extrapolation from irrelevant regions of ROC space we propose defining a priori a value of FPR so large that the test simply would not be used at that FPR, and a value of TPR so low that the test would not be used at that TPR. Then (a) only data points lying in the thus defined north-west rectangle of the unit square are used in the data analysis, and (b) the estimated summary ROC is depicted only within that subregion of the unit square. We illustrate the methods using simulated and real data sets, and we point to ways of comparing different tests and of taking into account the effects of covariates.

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Author and article information

Journal

Title: Academic Emergency Medicine

Abbreviated Title: Acad Emerg Med

Publisher: Wiley

ISSN: 10696563

Publication date Created: August 2014

Publication date (Print): August 2014

Publication date (Electronic): August 30 2014

Volume: 21

Issue: 8

Pages: 843-852

Affiliations

[1 ]The Clinical Pharmacology and Toxicology Fellowship Program; McGill University Health Centre; Montreal Quebec Canada

[2 ]Emergency Medicine; King Abdulaziz Medical City Riyadh Riyadh Saudi Arabia

[3 ]The SAMI Fellowship Program; Centre for Addictions and Mental Health; Toronto Ontario Canada

[4 ]The Alberta Research Centre for Health Evidence; Department of Pediatrics; University of Alberta; Edmonton Alberta Canada

[5 ]The Department of Emergency Medicine; Jewish General Hospital; Montreal Quebec Canada

[6 ]The Department of Emergency Medicine; Sunnybrook Health Sciences Centre; Toronto Ontario Canada

Article

DOI: 10.1111/acem.12435

PubMed ID: 25176151

SO-VID: 9ef37260-d91d-4c11-8a40-018a48170fba

License:

http://doi.wiley.com/10.1002/tdm_license_1.1

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