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Feasibility and Reproducibility of Contemporary Diastolic Parameters and Classification
  • +2
  • Hashmat Bahrami,
  • Frederik Pedersen,
  • Katrine Myhr,
  • Rasmus Møgelvang,
  • Christian Hassager
Hashmat Bahrami
Rigshospitalet

Corresponding Author:[email protected]

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Frederik Pedersen
Rigshospitalet
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Katrine Myhr
Rigshospitalet
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Rasmus Møgelvang
Rigshospitalet
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Christian Hassager
Rigshospitalet
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Abstract

Aims To evaluate the feasibility, time consumption, intra- and inter-observer re-test reproducibility of echocardiographic indexes and classification algorithms of diastolic function. Methods A total of 356 patients were examined prior to coronary artery by-pass grafting and/or aortic valve replacement surgery. A subgroup of 50 were examined with 3 successive echocardiograms in conditions reflecting daily clinical practice. Diastolic parameters suggested by former (2009) and current (2016) guidelines were obtained and analysed. Acquisition and analysis time, plus intra- and inter-observer variability were assessed. Results Most of the parameters’ feasibility were between 93 and 99%, except the TR Vmax (65%). Mean acquisition and analysis time were highest for the left atrial volume (141±24 seconds), in contrast to other parameters which were obtained in approximately one minute. 368 and 360 seconds was in average needed to classify according to the 2009 and 2016 algorithms, respectively (NS). The overall reproducibility was moderate (CV between 10-35%), with TR Vmax having lowest (CV 9.9-12%) and E/e’ the highest (CV 22-35%) variation. The 2009 algorithm resulted in higher indeterminate cases vs. the 2016 algorithm. Comparing the old and recent guidelines, 20 and 8 patients were reclassified during inter-examiner analysis, respectively. Conclusion The diastolic parameters are, in general, feasible and time efficient. Reproducibility is moderate. The 2016 guidelines algorithm seemed superior to the 2009 algorithm in terms of its feasibility and precision to classify patients in a uniform matter. Time consumption was equal. The 2016 algorithm proved more restrictive than 2009 in classifying patients with advanced stages of DD.