Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

Autoimmun Rev. 2023 May;22(5):103294. doi: 10.1016/j.autrev.2023.103294. Epub 2023 Feb 13.

Abstract

Objective: We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

Methods: A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

Results: We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

Conclusions: The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

Review registration: PROSPERO ID# CRD42021284881. (Amended to limit the scope).

Keywords: Big data; Machine learning; Prediction; Rheumatic autoimmune diseases; Systematic review.

Publication types

  • Systematic Review
  • Review

MeSH terms

  • Humans
  • Lupus Erythematosus, Systemic* / diagnosis
  • Machine Learning
  • Models, Statistical*
  • Prognosis