Development and temporal validation of a clinical prediction model to identify bacterial infection in hospitalised patients with systemic lupus erythematosus.

OBJECTIVE

To develop and temporally validate a clinical prediction model for identifying bacterial infection (BI) among hospitalised patients with SLE.

METHODS

We retrospectively included hospitalised patients with SLE admitted for BI or active disease between January 2023 and January 2026. Admissions from January 2023 to January 2025 formed the training cohort, and admissions from February 2025 to January 2026 formed the temporal validation cohort. BI included infection with or without concomitant active SLE; non-infected active (NA) SLE was the comparator. Candidate predictors were prespecified according to clinical relevance, prior literature, routine availability and missingness. LASSO (Least Absolute Shrinkage and Selection Operator) regression was applied across imputed datasets, and final coefficients were estimated using multivariable logistic regression and pooled using Rubin's rules. Performance was assessed by discrimination, calibration, full-workflow bootstrap internal validation, same-centre temporal validation and decision curve analysis.

RESULTS

The training cohort included 395 admissions (170 BI, 225 NA), and the validation cohort included 200 admissions (75 BI, 125 NA). The final model included age, disease duration, previous hospitalisation for BI, fever, white cell count, C reactive protein (CRP), procalcitonin, C4 hypocomplementemia and ordinal SLE-DAS (Systemic Lupus Erythematosus Disease Activity Score) category. Training area under the curve (AUC) was 0.869 (95% CI 0.833 to 0.905), exceeding CRP (0.771), procalcitonin (0.682) and the erythrocyte sedimentation rate/C reactive protein (ESR/CRP) ratio (0.736). Full-workflow bootstrap validation yielded an optimism-corrected AUC of 0.837 and corrected Brier score of 0.162. Same-centre temporal validation showed stable discrimination (AUC 0.852, 95% CI 0.794 to 0.910), acceptable calibration and preserved net benefit.

CONCLUSIONS

A routinely available, interpretable model identified BI among hospitalised patients with SLE using NA SLE as the comparator. C4 hypocomplementemia and higher SLE-DAS categories should be interpreted as markers of disease activity predominance rather than protective factors against infection. Multicentre external validation and clinical impact studies are required.