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Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases

  • Observational Research
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Abstract

There is an ongoing debate about the importance and the extent to which psychological and psychopathological factors, adverse childhood experiences, and socio-demographic characteristics are associated with the development of certain types of rheumatic disease. With the aim of contributing to knowledge on the subject, the present study uses machine learning modeling to determine the importance of 20 psychological and social variables in predicting two classes of rheumatic disease: inflammatory rheumatic and musculoskeletal diseases (RMD) (rheumatoid arthritis = RA, spondyloarthritis = SA, and Sjögren’s syndrome = SS) versus non-inflammatory RMD, namely fibromyalgia = FM). A total of 165 French women with FM, RA, SA, and SS completed an inventory of personality traits, a psychopathology diagnosis questionnaire, and a fatigue/pain questionnaire. They also answered questions about adverse childhood experiences and socio-demographic characteristics. Random forest and logistic regression machine learning algorithms were used for data analysis. The main findings suggest that mistreatment during childhood ((MDA = 10.22), the agreeableness personality trait (MDA = 3.39), and somatic disorder (MDA = 3.25) are the main psychological and social predictors of the type of rheumatic disease diagnosed. The first two predictors (OR = 18.92 and OR = 6.11) are also more strongly associated with FM than with RA-SA-SS. Overall, adverse childhood experiences seem relatively more important than personality traits, psychopathological or demographic variables. The results of this study suggest that traumatic childhood experiences may lead to psychopathological disorders in adulthood, which in turn might underlie, at least in part, the development of FM. Since there are no imaging or biological markers of FM, the present findings contribute to the scientific literature offering information to help patients with FM understand their pathology. They may also provide physicians with more diagnostic information.

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Availability of data and material

Following the Ethic Committee recommendation, the data used in this study cannot be shared through public open repositories. However, researchers willing to access the data can make the request to the corresponding author.

Code availability

Researchers willing to access the data analysis R code can make the request to the corresponding author.

Notes

  1. We included menopause in socio-demographic predictors because we did not have a more appropriate category in this study for this biological and psychosocial status.

  2. The inflammatory RMD group includes patients with RA, SA or SS, whose diagnosis was based on clinician expertise and were included in the study when they met the classification criteria. The diagnosis of FM was also primarily based on clinician expertise, implying that inflammatory RMD had been ruled out. It must be noted that some patients from in the RA-SA-SS group may present with FM features, as reported in the literature as secondary FM.

Abbreviations

RMD:

Rheumatic and musculoskeletal diseases

RA:

Rheumatoid arthritis

SA:

Spondyloarthritis

SS:

Sjögren’s syndrome

FM:

Fibromyalgia

ACR:

American College of Rheumatology

FIRST:

Fibromyalgia rapid screening tool

ASAS:

Assessment of SpondyloArthritis international Society

BFI:

Big five inventory

MDA:

Mean decrease accuracy

OR:

Odds ratio

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Acknowledgements

We are grateful to all the rheumatic patients who took part in this study, the team who worked in this research, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, receptionists and nurses. Particularly, we would like to thank Dr. Odile Plaisant, Professor Pierre Bourgeois and Professor Bruno Fautrel (Assistance Publique – Hôpitaux de Paris, University Hospital of Pitié-Salpêtrière), Dr. Stephanie Rist and Dr. Carine Salliot (Regional Hospital of Orleans) for their involvement in selecting the patients and the quality of data collection. In addition, the authors thank Agnès Caille and Bruno Giraudeau (CIC Inserm 1415, University Hospital of Tours) for providing their advice.

Funding

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Author information

Authors and Affiliations

  1. QualiPsy, EE 1901, University of Tours, 37000, Tours, France

    Germano Vera Cruz, Emilie Bucourt, Christian Réveillère & Robert Courtois

  2. Centre Val de Loire Resource Center for Professionals Working With Sex Offenders, Tours University Hospital, 37000, Tours, France

    Germano Vera Cruz & Robert Courtois

  3. Regional Hospital of Orléans, Rheumatology department, 45000, Orléans, France

    Virginie Martaillé

  4. University of Tours, Pain consultation, 37000, Tours, France

    Isabelle Joncker-Vannier

  5. University of Tours, Rheumatology department, 37000, Tours, France

    Philippe Goupille & Denis Mulleman

  6. CIC 1415, University Hospital of Tours, 37000, Tours, France

    Philippe Goupille & Denis Mulleman

  7. MSHS-Poitiers, 5 rue Théodore Lefebvre, 86073, Poitiers, France

    Germano Vera Cruz

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Contributions

GVC carried out data/statistical analysis and drafted the manuscript. EB collected the data. EB, RC, and CR conceived the study and initiated the scientific collaboration between the groups. VM, IJV, PG, and DM recruited the participants, performed the rheumatology diagnosis, and provided patient information. EB, DM, and RC edited the manuscript, with EB and DM providing meticulous revisions. All authors approved the final manuscript.

Corresponding author

Correspondence to Germano Vera Cruz.

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Conflict of interest

All authors are full-time employees at their indicated affiliation institutions, which are public universities and hospitals. None of the authors received fees, bonuses or other benefits for the work described in the manuscript.

Ethical approval

Ethical clearance was obtained from the ethical committee of the University of Tours. All participants provided written informed consent for data collection and publication prior to data collection. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

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Vera Cruz, G., Bucourt, E., Réveillère, C. et al. Machine learning reveals the most important psychological and social variables predicting the differential diagnosis of rheumatic and musculoskeletal diseases. Rheumatol Int 42, 1053–1062 (2022). https://doi.org/10.1007/s00296-021-04916-1

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