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Abstract
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 catheterisation (RHC). Patients were classified into three groups: non-PH, precapillary PH and postcapillary PH, based on values obtained from RHC. Using 24 parameters, we created predictive models employing four different classifiers and selected the one with the highest area under the curve. We then calculated the macro-average classification accuracy for PH on the derivation cohort (n=720) and prospective validation data set (n=165), comparing the results with guideline-based echocardiographic assessment obtained from each cohort.
Results Logistic regression with elastic net regularisation had the highest classification accuracy, with areas under the curves of 0.789, 0.766 and 0.742 for normal, precapillary PH and postcapillary 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 data set, 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.
- Echocardiography
- Heart Failure, Diastolic
- Hypertension, Pulmonary
Data availability statement
Data are available upon reasonable request.
Statistics from Altmetric.com
Data availability statement
Data are available upon reasonable request.
Footnotes
Contributors Design of the study: KK. Conduct of the study and data acquisition: YH. Data analysis and interpretation: TT and JK. Drafting the manuscript: KK and YH. Reviewing the manuscript and providing input: all authors. Final approval: all authors. Guarantor: KK and YH.
Funding This research was supported by the Japan Society for the Promotion of Science Kakenhi (grant numbers 21K12706 to YH and 23K07509 to KK) and AMED (grant number JP22uk1024007 to KK). The funding source had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Competing interests None declared.
Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
Provenance and peer review Not commissioned; externally peer reviewed.
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