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. 2023 Mar;29(2):292-299.
doi: 10.1111/jep.13765. Epub 2022 Sep 13.

Social determinants of health in prognostic machine learning models for orthopaedic outcomes: A systematic review

Amanda Lans  1   2 , Laura N Kanbier  1 , David N Bernstein  1 , Olivier Q Groot  1   2 , Paul T Ogink  2 , Daniel G Tobert  1 , Jorrit-Jan Verlaan  2 , Joseph H Schwab  1
Affiliations

Social determinants of health in prognostic machine learning models for orthopaedic outcomes: A systematic review

Amanda Lans et al. J Eval Clin Pract. 2023 Mar.

Abstract

Rational: Social determinants of health (SDOH) are being considered more frequently when providing orthopaedic care due to their impact on treatment outcomes. Simultaneously, prognostic machine learning (ML) models that facilitate clinical decision making have become popular tools in the field of orthopaedic surgery. When ML-driven tools are developed, it is important that the perpetuation of potential disparities is minimized. One approach is to consider SDOH during model development. To date, it remains unclear whether and how existing prognostic ML models for orthopaedic outcomes consider SDOH variables.

Objective: To investigate whether prognostic ML models for orthopaedic surgery outcomes account for SDOH, and to what extent SDOH variables are included in the final models.

Methods: A systematic search was conducted in PubMed, Embase and Cochrane for studies published up to 17 November 2020. Two reviewers independently extracted SDOH features using the PROGRESS+ framework (place of residence, race/ethnicity, Occupation, gender/sex, religion, education, social capital, socioeconomic status, 'Plus+' age, disability, and sexual orientation).

Results: The search yielded 7138 studies, of which 59 met the inclusion criteria. Across all studies, 96% (57/59) considered at least one PROGRESS+ factor during development. The most common factors were age (95%; 56/59) and gender/sex (96%; 57/59). Differential effect analyses, such as subgroup analysis, covariate adjustment, and baseline comparison, were rarely reported (10%; 6/59). The majority of models included age (92%; 54/59) and gender/sex (69%; 41/59) as final input variables. However, factors such as insurance status (7%; 4/59), marital status (7%; 4/59) and income (3%; 2/59) were seldom included.

Conclusion: The current level of reporting and consideration of SDOH during the development of prognostic ML models for orthopaedic outcomes is limited. Healthcare providers should be critical of the models they consider using and knowledgeable regarding the quality of model development, such as adherence to recognized methodological standards. Future efforts should aim to avoid bias and disparities when developing ML-driven applications for orthopaedics.

Keywords: algorithmic equity; artificial intelligence; health equity; machine learning; orthopaedic surgery; social determinants of health.

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References

REFERENCES

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