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ID 118966
Author
Hirata, Yukina Tokushima University
Tsuji, Takumasa Teikyo University
Kotoku, Jun’ichi Teikyo University
Keywords
Pulmonary hypertension
left heart disease
classification
echocardiography
artificial intelligence
machine learning
Content Type
Journal Article
Description
Objective: The classification of pulmonary hypertension (PH) is crucial for determining the appropriate therapeutic strategy. We investigated whether machine learning (ML) algorithms may assist in echocardiographic PH prediction, where current guidelines recommend integrating several different parameters.
Methods: We obtained physical and echocardiographic data from 885 patients who underwent right heart catheterization (RHC). Patients were classified into three groups: non-PH, pre-capillary PH, and post-capillary PH, based on values obtained from RHC. Utilizing 24 parameters, we created predictive models employing four different classifiers and selected the one with the highest area under the curve (AUC). We then calculated the macro-average classification accuracy for PH on the derivation cohort (n=720) and prospective validation dataset (n=165), comparing the results with guideline-based echocardiographic assessment obtained from each cohort.
Results: Logistic regression with elastic net regularization had the highest classification accuracy, with AUCs of 0.789, 0.766, and 0.742 for normal, pre-capillary PH, and post-capillary PH, respectively. The ML model demonstrated significantly better predictive accuracy than the guideline-based echocardiographic assessment in the derivation cohort (59.4% vs. 51.6%, p<0.01). In the independent validation dataset, the ML model's accuracy was comparable to the guideline-based PH classification (59.4% vs. 57.8%, p=0.638).
Conclusions: This preliminary study suggests promising potential for our ML model in predicting echocardiographic PH. Further research and validation are needed to fully assess its clinical utility in PH diagnosis and treatment decision-making.
Journal Title
Heart
ISSN
1468201X
13556037
NCID
AA12780139
AA11059124
Publisher
BMJ Publishing Group|British Cardiovascular Society
Volume
110
Start Page
586
End Page
593
Published Date
2024-01-30
Rights
This article has been accepted for publication in Heart, 2024 following peer review, and the Version of Record can be accessed online at https://doi.org/10.1136/heartjnl-2023-323320
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DOI (Published Version)
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language
eng
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departments
Medical Sciences
University Hospital