Use of contrast agents with echocardiography in patients with suboptimal echocardiography: an evidence-based analysis.
Bibliographic record
Abstract
OBJECTIVE: The objective of this report is to assess the ability of microbubble contrast agents to enhance the visualization of cardiac structures in patients with suboptimal echocardiography results. CONTRAST ECHOCARDIOGRAPHY: The most common use of contrast echocardiography is the enhancement of the endocardial border. Left ventricular (LV) opacification with contrast echocardiography has the potential to improve the definition of the LV border. The aim of contrast echocardiography is to provide better quantification of LV volume and assessment of LV wall motion analysis than echocardiography alone. Some patients, however, are more likely to exhibit poor echocardiograms than others. These patients include critically ill patients on ventilators or with lung problems, patients who've had recent chest operations, and obese patients. Echocardiography studies performed in the intensive care unit (ICU) are frequently inadequate or suboptimal because of the difficulties in positioning patients properly, poor lighting, chest tubes and bandages. Contrast agents could potentially be used in 5% to 10% of resting echocardiography exams and in an estimated 30% of stress echocardiography tests due to suboptimal echocardiograms. The American Society of Echocardiography guidelines stated that 75% to 90% of suboptimal echocardiography results can yield interpretable results with the use of contrast agents. RESEARCH QUESTION: Do contrast agents improve the visualization of the cardiac structures in patients exhibiting suboptimal echocardiograms? LITERATURE SEARCH: A literature searches was performed on June 22, 2009 using OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, the Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published since 1950. Abstracts were reviewed by a single reviewer and, for those studies meeting the eligibility criteria; full-text articles were obtained. Reference lists were also examined for any additional relevant studies not identified through the search. INCLUSION CRITERIA: Systematic reviews, meta-analyses, randomized controlled trials, observational studiesMinimum sample size of 20 enrolled patientsThe contrast agent used in the study must be licensed by Health Canada (at least Notice of Compliance)Patient population must include patients with suboptimal echocardiography resultsCompares echocardiography without contrast to echocardiography with contrastEnglishHuman EXCLUSION CRITERIA: Non-systematic reviews, case reportsGrey literature (e.g. conference abstracts) OUTCOMES OF INTEREST: Change in visualization with and without contrast agent SUMMARY OF FINDINGS: Based on the results of this review: Five studies consistently demonstrated that the addition of contrast to echocardiography improves heart visualization in patients with previously uninterpretable or suboptimal echocardiography results.Suboptimal echocardiography was consistently defined as >2 contiguous segments not seen in non- contrast images.The additional cost of using contrast agents in Ontario would range from approximately $5M to $30M annually.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".