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Record W4211058426 · doi:10.1097/ccm.0000000000001847

Guidelines for the Appropriate Use of Bedside General and Cardiac Ultrasonography in the Evaluation of Critically Ill Patients—Part II: Cardiac Ultrasonography

2016· article· en· W4211058426 on OpenAlexaff
Alexander Levitov, Heidi L. Frankel, Michael Blaivas, Andrew W. Kirkpatrick, Erik Su, David P. Evans, Douglas T. Summerfield, Anthony D. Slonim, Raoul Breitkreutz, Susanna Price, Matthew McLaughlin, Paul E. Marik, Mahmoud Elbarbary

Bibliographic record

VenueCritical Care Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMcMaster UniversityFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineIntensivistGrading (engineering)TerminologyAppropriate Use CriteriaIntensive care medicineIntensive careInternal medicine

Abstract

fetched live from OpenAlex

Objective: To establish evidence-based guidelines for the use of bedside cardiac ultrasound, echocardiography, in the ICU and equivalent care sites. Methods: Grading of Recommendations, Assessment, Development and Evaluation system was used to rank the “levels” of quality of evidence into high (A), moderate (B), or low (C) and to determine the “strength” of recommendations as either strong (strength class 1) or conditional/weak (strength class 2), thus generating six “grades” of recommendations (1A–1B–1C–2A–2B–2C). Grading of Recommendations, Assessment, Development and Evaluation was used for all questions with clinically relevant outcomes. RAND Appropriateness Method, incorporating the modified Delphi technique, was used in formulating recommendations related to terminology or definitions or in those based purely on expert consensus. The process was conducted by teleconference and electronic-based discussion, following clear rules for establishing consensus and agreement/disagreement. Individual panel members provided full disclosure and were judged to be free of any commercial bias. Results: Forty-five statements were considered. Among these statements, six did not achieve agreement based on RAND appropriateness method rules (majority of at least 70%). Fifteen statements were approved as conditional recommendations (strength class 2). The rest (24 statements) were approved as strong recommendations (strength class 1). Each recommendation was also linked to its level of quality of evidence and the required level of echo expertise of the intensivist. Key recommendations, listed by category, included the use of cardiac ultrasonography to assess preload responsiveness in mechanically ventilated (1B) patients, left ventricular (LV) systolic (1C) and diastolic (2C) function, acute cor pulmonale (ACP) (1C), pulmonary hypertension (1B), symptomatic pulmonary embolism (PE) (1C), right ventricular (RV) infarct (1C), the efficacy of fluid resuscitation (1C) and inotropic therapy (2C), presence of RV dysfunction (2C) in septic shock, the reason for cardiac arrest to assist in cardiopulmonary resuscitation (1B–2C depending on rhythm), status in acute coronary syndromes (ACS) (1C), the presence of pericardial effusion (1C), cardiac tamponade (1B), valvular dysfunction (1C), endocarditis in native (2C) or mechanical valves (1B), great vessel disease and injury (2C), penetrating chest trauma (1C) and for use of contrast (1B–2C depending on indication). Finally, several recommendations were made regarding the use of bedside cardiac ultrasound in pediatric patients ranging from 1B for preload responsiveness to no recommendation for RV dysfunction. Conclusions: There was strong agreement among a large cohort of international experts regarding several class 1 recommendations for the use of bedside cardiac ultrasound, echocardiography, in the ICU. Evidence-based recommendations regarding the appropriate use of this technology are a step toward improving patient outcomes in relevant patients and guiding appropriate integration of ultrasound into critical care practice.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.116
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0100.005
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0070.004
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0050.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.418
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations436
Published2016
Admission routes1
Has abstractyes

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