ArticleThe international journal of cardiovascular imaging2022
Can echocardiographic assessment of diastolic function be automated?
Article in The international journal of cardiovascular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Automated algorithms in diastology: how to move forward?The international journal of cardiovascular imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Echocardiographic evaluation of left ventricular diastolic function relies on a multi-pronged algorithm, which incorporates Doppler-based and volumetric parameters. Integration of clinical data in diastolic assessment is recommended, though not clearly outlined. We sought to develop an automated tool for diastolic function, compare its performance to human-generated diagnoses and identify the common sources of error. Our software tool is based on the 2016 diastolic guidelines algorithm, which uses 8 parameters as input, with 10 conditions as the logic and 5 possible outputs as final diagnoses. Initially, we prospectively studied 563 patients whose diastolic function was independently evaluated by an expert echocardiographer and by the automated tool. Incongruent cases were further analyzed, after which features of myocardial disease were integrated into a refined version of the software that was tested in an independent cohort of 1106 patients. In the initial analysis, 202/563 grades (36%) were incongruent between the automated and human reads, with the highest rate of discordance for mild and indeterminate categories. In 17% of cases, human diagnoses differed from that dictated by the algorithm due to integration of clinical factors. Follow-up analysis using the refined automated tool did not improve the discordance rate (440/1106; 40%). There was more discordance in cases of: age > 40 years, impaired mitral inflow patterns (E/A < 0.8) and reduced mitral e' values. Further analysis revealed differences in how readers interpreted the interaction between these factors and diastolic function, which could not be incorporated into the automated tool. In conclusion, although assessment of diastolic function relies on an algorithm that can be automated, this algorithm does not include clear guidance on how to incorporate age, or age-related changes in Doppler-based parameters, often resulting in discordant diagnoses. Standardized interpretation of these factors is needed to improve the reproducibility of diastolic function grading by human readers and the accuracy of the automated classification.
Indexed as
Identifiers
34882301What Socratic 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 Socratic graph.