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A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning

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Abstract

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes also increases. Peripartum cardiomyopathy is a rare and poorly-characterised condition that presents as heart failure in the last trimester prior to delivery or within 5–6 months postpartum. The lack of a definitive understanding of the molecular causes and clinical progress of this condition suggests that bibliometrics will be well-suited to creating new insights into this serious clinical problem. We examine similarities and differences between peripartum and its closely related familial dilated cardiomyopathy and idiopathic dilated cardiomyopathy. Using PubMed as the source of bibliometric data, we apply artificial intelligence–supported natural language processing to compare extracted data and genes association with these cardiomyopathies. Gene data were enhanced with additional metadata from third-party datasets and then analysed for their impact and specificity for peripartum cardiomyopathy. Artificial intelligence identified 14 genes that distinguished peripartum from both dilated and familial dilated cardiomyopathy. They are as follows: CTSD, RLN2, MMP23B*, SLC17A5, ST2*, PTHLH, CFH*, CFI, GPT, MR1, Rln1, SRI, STAT5A* and THBD. We then used the Human Protein Atlas website that uses affinity-purified rabbit polyclonal antibodies to identify genes that are expressed at the protein level (bold), or as RNA transcripts (*) in healthy human left ventricles. Additional analysis focussed on the full set of peripartum genes on linkage and specificity to cardiomyopathy yielded a different set of thirteen genes (bold font indicates those expressed in cardiomyocytes: PRL, RLN2, PLN, ST2, CTSD, F2, ACE, STAT3, TTN, SPP1, LGALS3, miR-146a, GNB3, SRI). This type of analysis can highlight new avenues for research, aimed at improving genomics-driven peripartum cardiomyopathy diagnosis as well as potential pathological and clinical sub-classification. We expect that this will allow for future improvements in identification, treatment and management of this condition. The first step in the application of these bibliometric-based artificial intelligence methods is to understand the current knowledge, and it is the aim of this paper to show how this might be achieved.

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Authors and Affiliations

  1. 23 Strands Pty Ltd, 107, 26 Pirrama Rd, Pyrmont, NSW, Australia

    M. Grosser, H. Lin, S. Tipper & D. Venter

  2. University Technology Sydney, 15 Broadway, Ultimo, NSW, Australia

    M. Wu, Y. Zhang & J. Lu

  3. Victor Chang Cardiac Research Institute, 405 Liverpool St, Darlinghurst, Australia

    C. G. dos Remedios

  4. Sydney Heart Bank, University of Sydney, Sydney, Australia

    C. G. dos Remedios

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  1. M. Grosser
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  2. H. Lin
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  3. M. Wu
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  4. Y. Zhang
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  5. S. Tipper
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  6. D. Venter
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  7. J. Lu
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  8. C. G. dos Remedios
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Contributions

MG is the founder and CEO of 23 Strands Pty Ltd (Australia) and was the primary author of this manuscript. HL is the Chief Data Officer of 23Strands and co-ordinated the development and implementation of the techniques shown with MW, YJ and JL from the Australian Artificial Intelligence Institute at the University of Technology, Sydney. DV is the Chief Medical Advisor, and ST is the Chief Strategy Officer at 23Strands and both were involved in writing the manuscript. CdR is from the Victor Chang Cardiac Research Institute and conceived of the work and was involved in writing the manuscript.

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Correspondence to M. Grosser.

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MG, HL, DV, and ST are employees of 23 Strands Pty Ltd (Australia), a privately held company, but their employment does not alter the authors’ adherence to the publication policies.

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Grosser, M., Lin, H., Wu, M. et al. A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning. Biophys Rev 14, 381–401 (2022). https://doi.org/10.1007/s12551-022-00933-x

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  • DOI: https://doi.org/10.1007/s12551-022-00933-x

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