ArticleHeart, lung and vessels2015
Clinical utility of semi-automated estimation of ejection fraction at the point-of-care.
Article in Heart, lung and vessels, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Automated echocardiography for measuring and tracking cardiac output after cardiac surgery: a validation study.Journal of clinical monitoring and computing · 2020Trial
- Quantification of left ventricular ejection fraction and cardiac output using a novel semi-automated echocardiographic method: a prospective observational study in coronary artery bypass patients.BMC anesthesiology · 2023Observational
- Integrated Assessment of Heart, Lung and Lower Extremity Veins Using Hand-Held Ultrasound Device in COVID-19 Patients: Feasibility and Clinical Application.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device.European heart journal. Digital health · 2022Article
- Left ventricular end-diastolic pressure is associated with left atrial functional measures by echocardiography.The international journal of cardiovascular imaging · 2021Article
- The impact of cardiovascular risk factors on global longitudinal strain over a decade in the general population: the copenhagen city heart study.The international journal of cardiovascular imaging · 2020Article
- Quantifying Intermembrane Distances with Serial Image Dilations.Journal of visualized experiments : JoVE · 2018Article
- Overweight and obesity impair left ventricular systolic function as measured by left ventricular ejection fraction and global longitudinal strain.Cardiovascular diabetology · 2018Observational
- Improved Prediction of Sudden Cardiac Death Risk: Staying Within the Echocardiogram but Extending Beyond the Ejection Fraction.Circulation. Cardiovascular imaging · 2016Article
- Visual versus fully automated assessment of left ventricular ejection fraction.Avicenna journal of medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTo compare estimation of ejection fraction at the bedside by AutoEF compared with conventional methods and to assess feasibility and time consumption.
methodsA total of 102 relatively hemodynanically stable mixed medical and surgical patients were included. All patients underwent ultrasonography of the heart at the bedside performed by a novice examiner. Three assessments of ejection fraction were made: 1) Expert eyeballing by a single specialist in cardiology and expert in echocardiography; 2) Manual planimetry by an experienced examiner; 3) AutoEF by a novice examiner with limited experience in echocardiography.
resultsExpert eyeballing of ejection fraction was performed in 100% of cases. Manual planimetry was possible in 89% of cases and AutoEF was possible in 83% of cases. The correlation between expert eyeballing and AutoEF was r=0.82, p < 0.001, for manual planimetry and for AutoEF it was r=0.82, p < 0.001; for expert eyeballing and manual planimetry it was r=0.80, p < 0.001. The mean time consumption for manual planimetry was 98 ( 90-106 ) seconds; correspondingly the mean time spent for AutoEF was 41 ( 36-46 ) seconds, which was significantly less (p < 0.001).
conclusionsAutoEF seems to be a valid supplement to the clinical assessment of ejection fraction in the hands of less experienced examiners, yielding result similar to manual planimetry with less time consumption and less intra-observer variability. However, manual editing may be required and training is thus recommended before AutoEF is applicable for use by novices.
Indexed as
Identifiers
26495266PMC4593021What OpenQuestion holds
Registered trials
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.