Work

Machine-learning model for distinguishing cytomegalovirus infection status in hospitalised patients with systemic lupus erythematosus: a retrospective cohort study with temporal validation

Year: 2026

Type: article

Abstract: Objectives To develop and temporally evaluate a machine-learning model to distinguish among no active cytomegalovirus (CMV) infection, CMV viraemia and CMV disease in hospitalised patients with systemic lupus erythematosus. Design Single-centre, retrospective, observational study with temporal validation. Setting A tertiary referral hospital in Beijing, China. The derivation cohort comprised patients admitted between March 2012 and October 2023, and the temporal validation cohort comprised patients admitted between November 2023 and January 2025. Participants A total of 1749 hospitalised patients with systemic lupus erythematosus (SLE) were included: 1519 in the derivation cohort and 230 in the temporal validation cohort. Main outcome measures CMV infection status was categorised as no active infection (Class 0), CMV viraemia (Class 1) or CMV disease (Class 2). Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with 95% CIs and decision curve analysis. Results A total of 1749 hospitalised patients were included, comprising 1519 patients in the derivation cohort and 230 in the temporally separated validation cohort. A random forest model with six variables (CD4+ T cell count, SLEDAI-2K, haemoglobin, C-reactive protein, neutrophil count and serum ferritin) achieved the best overall performance. In the temporal validation cohort (n=230), the model achieved area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI 0.80 to 0.98) for distinguishing CMV disease from no active CMV infection, 0.72 (95% CI 0.46 to 0.95) for distinguishing CMV disease from CMV viraemia and 0.65 (95% CI 0.57 to 0.74) for distinguishing CMV viraemia from no active CMV infection. In exploratory analyses restricted to the training cohort, CD4+T cell count cut-offs selected using the Youden index were 308 cells/µL for distinguishing Class 0 from Class 1 and 171 cells/µL for comparisons involving CMV disease. Conclusions The model discriminated CMV disease from no active CMV infection more reliably than it discriminated CMV viraemia. Given the retrospective single-centre design and the small number of CMV disease events in the temporal validation cohort, the model could be considered exploratory and requires prospective multicentre validation before clinical use.

Source: BMJ Open

DOI: 10.1136/bmjopen-2026-121354