NLR outperforms PLR in SLE diagnosis and prognosis: an AI-enhanced meta-analysis of 12 850 patients with ethnicity-specific cut-offs
Abstract
Objectives: To evaluate the diagnostic and prognostic performance of the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) in SLE and to integrate these biomarkers into an interpretable artificial intelligence (AI) model for clinical decision support.
Design: We conducted a two-phase mixed-methods study: (1) a meta-analysis of 50 studies (n=12 850 patients with SLE), compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, and (2) the development and validation of an XGBoost machine learning model, guided by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-AI, with SHapley Additive exPlanations (SHAP) explainability.
Setting: Our analysis used multicentre data from global SLE registries, including cohorts from Asia, Europe, North America and Africa.
Participants: The study included adults (≥18 years) who met the 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for SLE, with NLR and PLR measured via standardised complete blood count. Comparator groups consisted of healthy controls and patients with non-SLE autoimmune diseases.
Interventions: NLR and PLR were assessed as biomarkers for SLE activity and complications. Our AI model integrated these ratios with standard clinical biomarkers and multi-omics data.
Primary and secondary outcome measures: The primary outcome was diagnostic accuracy (measured by area under the curve…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 12 participants or records.
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