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Prediction model for developing neuropsychiatric systemic lupus erythematosus in lupus patients

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Clinical Rheumatology Aims and scope Submit manuscript

Abstract

Objective

This study aimed to construct a predictive model for assessing the risk of development of neuropsychiatric systemic lupus erythematosus (NPSLE) among patients with SLE based on clinical, laboratory, and meteorological data.

Methods

A total of 2232 SLE patients were included and were randomly assigned into training and validation sets. Variables such as clinical and laboratory data and local meteorological data were screened by univariate and least absolute shrinkage and selection operator (LASSO) logistic regression modelling. After 10-fold cross-validation, the predictive model was built by multivariate logistic regression, and a nomogram was constructed to visualize the risk of NPSLE. The efficacy and accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curve analysis. Net clinical benefit was assessed by decision curve analysis.

Results

Variables that were included in the predictive model were anti-dsDNA, anti-SSA, lymphocyte count, hematocrit, erythrocyte sedimentation rate, pre-albumin, retinol binding protein, creatine kinase isoenzyme MB, Nterminal brain natriuretic peptide precursor, creatinine, indirect bilirubin, fibrinogen, hypersensitive C-reactive protein, CO, and mild contamination. The nomogram showed a broad prediction spectrum; the area under the curve (AUC) was 0.895 (0.858–0.931) for the training set and 0.849 (0.783–0.916) for the validation set.

Conclusion

The model exhibits good predictive performance and will confer clinical benefit in NPSLE risk calculation.

Key Points

• Clinical, laboratory, and meteorological data were incorporated into a predictive model for neuropsychiatric systemic lupus erythematosus (NPSLE) in SLE patients.

• Anti-dsDNA, anti-SSA, LYM, HCT, ESR, hsCRP, IBIL, PA, RBP, CO, Fib, NT-proBNP, Crea, CO, and mild contamination are predictors of the development of NPSLE and may have potential for research.

• The nomogram has good predictive performance and clinical value and can be used to guide clinical diagnosis and treatment.

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Data availability

Datasets are available from the corresponding author on reasonable request.

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Funding

This work was supported by grants from the National Natural Science Foundation of China (81701606).

Author information

Author notes

  1. Si-Yu Feng and Lin-Chong Su contributed equally.

Authors and Affiliations

  1. Department of Evidence-Based Medicine, School of Public Health, Southwest Medical University, Luzhou, Sichuan, China

    Si-Yu Feng, Xiao-Yan Liu & Wang-Dong Xu

  2. Hubei Provincial Key Laboratory of Occurrence and Intervention of Rheumatic diseases, Affiliated Minda Hospital of Hubei Institute for Nationalities, Enshi, Hubei, China

    Lin-Chong Su

  3. Department of Rheumatology and Immunology, Affiliated Minda Hospital of Hubei Institute for Nationalities, Enshi, Hubei, China

    Lin-Chong Su

  4. Department of Rheumatology and Immunology, the Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China

    Zhen Qin & An-Fang Huang

  5. Laboratory Animal Center, Southwest Medical University, Luzhou, Sichuan, China

    Lu Fu

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Contributions

Study conception and design: SYF, LCS, WDX. Acquisition of data: LF, XYL, ZQ, AFH. Analysis and interpretation of data: LF, XYL, ZQ, AFH. Drafting the article: SYF, LCS, WDX. Final approval of the version of the article to be published: all authors, and that all authors agree to be accountable for all aspects of the work.

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Correspondence to Wang-Dong Xu.

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Feng, SY., Su, LC., Liu, XY. et al. Prediction model for developing neuropsychiatric systemic lupus erythematosus in lupus patients. Clin Rheumatol 43, 1881–1896 (2024). https://doi.org/10.1007/s10067-024-06970-z

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  • DOI: https://doi.org/10.1007/s10067-024-06970-z

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