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. 2024 Dec 5:15:1503118.
doi: 10.3389/fimmu.2024.1503118. eCollection 2024.

A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis

Rui Huang  1   2 , Dan Wu  1   2 , Ling Wang  1   2 , Ping Liu  1   2 , Xiaoru Zhu  1   2 , Leqiu Huang  1   2 , Mengmeng Chen  1   2 , Xin Lv  1   2
Affiliations

A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis

Rui Huang et al. Front Immunol. .

Abstract

Background: Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) is a severe hyperinflammatory disorder induced by overactivation of macrophages and T cells. This study aims to identify the risk factors for the progression from infectious mononucleosis (EBV-IM) to EBV-HLH, by analyzing the laboratory parameters of patients with EBV-IM and EBV-HLH and constructing a clinical prediction model. The outcome of this study carries important clinical value for early diagnosis and treatment of EBV-HLH.

Methods: A retrospective analysis was conducted on 60 patients diagnosed with EBV-HLH and 221 patients diagnosed with EBV-IM at our hospital between November 2018 and January 2024. Participants were randomly assigned to derivation and internal validation cohorts in a 7:3 ratio. LASSO regression and logistic regression analyses were employed to identify risk factors and construct the nomogram.

Results: Ferritin (OR, 213.139; 95% CI, 8.604-5279.703; P=0.001), CD3-CD16+CD56+% (OR, 0.011; 95% CI, 0-0.467; P=0.011), anti-EBV-NA-IgG (OR, 57.370; 95%CI, 2.976-1106.049; P=0.007), IL-6 (OR, 71.505; 95%CI, 2.118-2414.288; P=0.017), IL-10 (OR, 213.139; 95% CI, 8.604-5279.703; P=0.001) were identified as independent predictors of EBV-HLH. The prediction model demonstrated excellent discriminatory capability evidenced by an AUC of 0.997 (95% CI,0.993-1.000). When visualized using a nomogram, the ROC curves for the derivation and validation cohorts exhibited AUCs of 0.997 and 0.993, respectively. These results suggested that the model was highly stable and accurate. Furthermore, calibration curves and clinical decision curves indicated that the model possessed good calibration and offered significant clinical benefits.

Conclusions: The nomogram, which was based on these five predictors, exhibited robust predictive value and stability, thereby can be used to aid clinicians in the early detection of EBV-HLH.

Keywords: Epstein-Barr virus infections; hemophagocytic; infectious mononucleosis; lymphohistiocytosis; nomograms; pediatrics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Screening of 21 variables based on Lasso regression. (A) The variation characteristics of the coefficient of variables; (B) the selection process of the optimum value of the parameter log(λ) in the Lasso regression model and 12 variables were selected for further logistic regression analysis.
Figure 2
Figure 2
The nomogram for prediction of EBV-HLH.
Figure 3
Figure 3
ROC curves show excellent discrimination ability for predicting EBV-HLH in the derivation (A) and internal validation cohorts (B). The cutoff values were 0.141 (AUC:0.997, sensitivity: 0.981, specificity: 1.000), and 0.420 (AUC:0.993, sensitivity: 0.983, specificity: 0.947).
Figure 4
Figure 4
Calibration curves of the derivation cohort (A) and internal validation cohort (B).
Figure 5
Figure 5
Decision curve of the derivation and validation cohorts.
See this image and copyright information in PMC

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