Clinical Decision Rules for Diagnostic Imaging in the Emergency Department: A Research Agenda

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Major gaps persist in the development, validation, and implementation of clinical decision rules (CDRs) for diagnostic imaging.

Related collections

Most cited references 79

Record: found
Abstract: found
Article: not found

Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

Brendan M Reilly, Arthur T Evans (2006)

Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.

0 comments Cited 212 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Validation, updating and impact of clinical prediction rules: a review.

D B Toll, K J M Janssen, Y. Vergouwe … (2008)

To provide an overview of the research steps that need to follow the development of diagnostic or prognostic prediction rules. These steps include validity assessment, updating (if necessary), and impact assessment of clinical prediction rules. Narrative review covering methodological and empirical prediction studies from primary and secondary care. In general, three types of validation of previously developed prediction rules can be distinguished: temporal, geographical, and domain validations. In case of poor validation, the validation data can be used to update or adjust the previously developed prediction rule to the new circumstances. These update methods differ in extensiveness, with the easiest method a change in model intercept to the outcome occurrence at hand. Prediction rules -- with or without updating -- showing good performance in (various) validation studies may subsequently be subjected to an impact study, to demonstrate whether they change physicians' decisions, improve clinically relevant process parameters, patient outcome, or reduce costs. Finally, whether a prediction rule is implemented successfully in clinical practice depends on several potential barriers to the use of the rule. The development of a diagnostic or prognostic prediction rule is just a first step. We reviewed important aspects of the subsequent steps in prediction research.

0 comments Cited 179 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Prediction of pulmonary embolism in the emergency department: the revised Geneva score.

Grégoire Le Gal, Marc Righini, Pierre-Marie Roy … (2006)

Diagnosis of pulmonary embolism requires clinical probability assessment. Implicit assessment is accurate but is not standardized, and current prediction rules have shortcomings. To construct a simple score based entirely on clinical variables and independent from physicians' implicit judgment. Derivation and external validation of the score in 2 independent management studies on pulmonary embolism diagnosis. Emergency departments of 3 university hospitals in Europe. Consecutive patients admitted for clinically suspected pulmonary embolism. Collected data included demographic characteristics, risk factors, and clinical signs and symptoms suggestive of venous thromboembolism. The variables statistically significantly associated with pulmonary embolism in univariate analysis were included in a multivariate logistic regression model. Points were assigned according to the regression coefficients. The score was then externally validated in an independent cohort. The score comprised 8 variables (points): age older than 65 years (1 point), previous deep venous thrombosis or pulmonary embolism (3 points), surgery or fracture within 1 month (2 points), active malignant condition (2 points), unilateral lower limb pain (3 points), hemoptysis (2 points), heart rate of 75 to 94 beats/min (3 points) or 95 beats/min or more (5 points), and pain on lower-limb deep venous palpation and unilateral edema (4 points). In the validation set, the prevalence of pulmonary embolism was 8% in the low-probability category (0 to 3 points), 28% in the intermediate-probability category (4 to 10 points), and 74% in the high-probability category (> or =11 points). Interobserver agreement for the score items was not studied. The proposed score is entirely standardized and is based on clinical variables. It has sustained internal and external validation and should now be tested for clinical usefulness in an outcome study.

0 comments Cited 178 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Title: Academic Emergency Medicine

Abbreviated Title: Acad Emerg Med

Publisher: Wiley

ISSN: 10696563

Publication date Created: December 2015

Publication date (Print): December 2015

Publication date (Electronic): November 14 2015

Volume: 22

Issue: 12

Pages: 1406-1416

Affiliations

[1 ]Department of Emergency Medicine; The Ohio State University College of Medicine; Columbus OH

[2 ]Department of Emergency Medicine; University of California San Francisco School of Medicine; San Francisco CA

[3 ]Department of Emergency Medicine; Division of Emergency Medicine; Washington University in St. Louis School of Medicine; St. Louis MO

[4 ]Department of Emergency Medicine; Oregon Health & Science University; Portland OR

[5 ]Department of Emergency Medicine; University of Michigan Medical School; Ann Arbor MI

[6 ]Department of Emergency Medicine; University of Ottawa; Ottawa Ontario Canada

[7 ]Department of Emergency Medicine; Hennepin County Medical Center; Minneapolis MN

[8 ]Department of Internal Medicine; Hennepin County Medical Center; Minneapolis MN

[9 ]Department of Emergency Medicine; Baystate Medical Center; Tufts University School of Medicine; Springfield MA

[10 ]Department of Emergency Medicine; University of Minnesota Medical School; Minneapolis MN

[11 ]Department of Emergency Medicine; Indiana University School of Medicine; Indianapolis IN

[12 ]Department of Emergency Medicine; UC Davis School of Medicine; Sacramento CA

Article

DOI: 10.1111/acem.12828

PubMed ID: 26567885

SO-VID: e00adf68-cd6f-4f95-b6c2-efc0a8eb5591

License:

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

History

Data availability:

Comments

Comment on this article

scite_

Cited by 12

See all cited by

Most referenced authors 1,161

See all reference authors

- Version 1

Clinical Decision Rules for Diagnostic Imaging in the Emergency Department: A Research Agenda

Read this article at

Abstract

Related collections

Thermal Imaging in Social Decision Making

Most cited references 79

Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

Validation, updating and impact of clinical prediction rules: a review.

Prediction of pulmonary embolism in the emergency department: the revised Geneva score.

Author and article information

Journal

Affiliations

Article

History

Comments

Comment on this article

Similar content 2,401

Cited by 12

Most referenced authors 1,161