Kheyfets, V.O., Sweatt, A.J., Gomberg-Maitland, M. et al. (7 more authors) (2023) Computational platform for doctor–artificial intelligence cooperation in pulmonary arterial hypertension prognostication: a pilot study. ERJ Open Research, 9 (1). 00484-2022. ISSN 2312-0541
Abstract
Background Pulmonary arterial hypertension (PAH) is a heterogeneous and complex pulmonary vascular disease associated with substantial morbidity. Machine-learning algorithms (used in many PAH risk calculators) can combine established parameters with thousands of circulating biomarkers to optimise PAH prognostication, but these approaches do not offer the clinician insight into what parameters drove the prognosis. The approach proposed in this study diverges from other contemporary phenotyping methods by identifying patient-specific parameters driving clinical risk.
Methods We trained a random forest algorithm to predict 4-year survival risk in a cohort of 167 adult PAH patients evaluated at Stanford University, with 20% withheld for (internal) validation. Another cohort of 38 patients from Sheffield University were used as a secondary (external) validation. Shapley values, borrowed from game theory, were computed to rank the input parameters based on their importance to the predicted risk score for the entire trained random forest model (global importance) and for an individual patient (local importance).
Results Between the internal and external validation cohorts, the random forest model predicted 4-year risk of death/transplant with sensitivity and specificity of 71.0–100% and 81.0–89.0%, respectively. The model reinforced the importance of established prognostic markers, but also identified novel inflammatory biomarkers that predict risk in some PAH patients.
Conclusion These results stress the need for advancing individualised phenotyping strategies that integrate clinical and biochemical data with outcome. The computational platform presented in this study offers a critical step towards personalised medicine in which a clinician can interpret an algorithm's assessment of an individual patient.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright ©The authors 2023. This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0 (http://creativecommons.org/licenses/by-nc/4.0/). For commercial reproduction rights and permissions contact permissions@ersnet.org |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection, Immunity and Cardiovascular Disease |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Mar 2023 16:52 |
Last Modified: | 21 Mar 2023 16:52 |
Published Version: | http://dx.doi.org/10.1183/23120541.00484-2022 |
Status: | Published |
Publisher: | European Respiratory Society (ERS) |
Refereed: | Yes |
Identification Number: | 10.1183/23120541.00484-2022 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197533 |