Construction of a forest plot prediction model based on Lasso regression for Epstein-Barr virus associated hemophagocytic lymphohistiocytosis in children

Construction of a forest plot prediction model based on Lasso regression for Epstein-Barr virus associated hemophagocytic lymphohistiocytosis in children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction of a forest plot prediction model based on Lasso regression for Epstein-Barr virus associated hemophagocytic lymphohistiocytosis in children Yuewen Su, Mengli Xu, Meng Cao, Yuqin Li, Shaoyan Hu, Wei-fang Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7536469/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Italian Journal of Pediatrics → Version 1 posted 5 You are reading this latest preprint version Abstract Objective To identify risk factors for the progression of Epstein-Barr virus(EBV) infection to Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) and guide clinical intervention by analyzing the clinical data and laboratory examination between infectious mononucleosis and EBV-HLH infection using a forest plot prediction model. Methods Clinical data and laboratory tests of children with "Epstein-Barr virus infection and hemophagocytic lymphohistiocytosis " who were hospitalized in Children's Hospital of Soochow University from January 2019 to December 2024 were collected. A total of 1358 children of infectious mononucleosis associated with EBV (IM group) and 86 children of hemophagocytic lymphohistiocytosis associated with EBV (EBV-HLH group) were included. The differences between the groups were retrospectively analyzed and regression analysis was performed. The proximity matching method was selected for 1:4 matching between the EBV-HLH group and the IM group. The forest plot prediction model was established based on Lasso regression to analyze the clinical differences between the IM group and the EBV-HLH group. Results Lasso regression model screening identified hemoglobin (HB), ferritin (FER), fibrinogen (FIB) and CD3 + CD4 + as hexhibiting good predictive value for EBV-HLH, with areas under the receiver operating characteristic (ROC) curve of 0.904, 0.973, 0.866 and 0.783, and specificities of 0.799, 0.965, 0.802 and 0.892, respectively. The prediction model constructed using HB, FER, FIB, and CD3 + CD4 + showed excellent predictive accuracy. With an optimal cut-off value of F = 56.95, the model achieved a sensitivity of 95.30% and a specificity of 99.70%. Conclusions The early diagnosis of EBV-HLH lacks specific indicators. In this study, a predictive model for EBV-HLH was established using LASSO regression, incorporating four key parameters (HB, FER, FIB, and CD3 + CD4 + T-cell subsets). This model may serve as a screening tool for the early diagnosis of EBV-HLH and provide a diagnostic basis for clinical practice.​ Epstein-Barr virus Infectious mononucleosis Hemophagocytic lymphohistiocytosis Children Figures Figure 1 Figure 2 Figure 3 Figure 4 Background EBV is a ubiquitous herpesvirus that can cause a range of diseases. Primary infection often remains asymptomatic, while some individuals may develop Infectious mononucleosis (IM), which is typically self-limiting [ 1 ] . However, viral reactivation in a small subset of infected individuals can trigger a cytokine storm, leading to the development of HLH. This condition is usually associated with impaired T cell and natural killer (NK) cell function, which prevents the necessary negative feedback regulation during immune activation. Consequently, this impairment results in uncontrolled activation of cytotoxic T lymphocytes (CTLs) and macrophages, initiating a "cytokine storm." [ 2 ] . Based on etiology, HLH can be classified into primary and secondary forms. Common secondary causes include infections, malignancies, and autoimmune disorders [ 3 ] . EBV-HLH is a frequent form of infection-induced HLH, characterized by rapid progression, poor prognosis, and high mortality. Therefore, early diagnosis and prompt treatment are crucial for improving survival rates. By analyzing the clinical differences between IM and EBV-HLH, this study aims to identify early diagnostic indicators to guide clinical intervention. Research object and method Ethics approval This study was approved by the Ethics Committee of Children's Hospital of Soochow University (Approval No.: 2023CS219). Written informed consent was obtained from the guardians of all participants. Research object Children with "Epstein-Barr virus infection and hemophagocytic syndrome" who were hospitalized in the Children's Hospital Affiliated to Soochow University from January 2019 to December 2024 were enrolled according to the inclusion and exclusion criteria formulated in this study. A total of 1358 children with infectious mononucleosis caused by Epstein-Barr virus infection (IM group) and 86 children with hemophagocytic syndrome caused by Epstein-Barr virus infection (EBV-HLH group) were finally included. Inclusion criteria and exclusion criteria Inclusion criteria: 1.The diagnostic criteria for IM refer to the Clinical Characteristics and Diagnostic Criteria of Epstein-Barr Virus Infectious Mononucleosis in Children [ 4 ] ; 2.The diagnostic criteria for EBV-HLH were based on the HLH-2004 diagnostic protocol [ 5 ] , and the EBV serological antibodies manifested as primary acute infection or positive EBV DNA. Exclusion criteria: 1. Do not meet the diagnostic criteria for IM and EBV-HLH; 2. Inpatient cases not first diagnosed in our hospital; 3. Patients with a large amount of missing clinical data; 4. In the HLH group, primary HLH, autoimmune-related HLH, and HLH caused by other pathogens (cytomegalovirus, mycoplasma, toxoplasma gondii, etc.) are excluded. Statistical Methods Data were statistically analyzed using SPSS 27.0 and R 4.3.3 software, with propensity score matching applied for calibration. Based on significant differences in gender and age, the nearest neighbor matching method was used to perform 1:4 matching between the HLH group and the IM group. Normally distributed measurement data were expressed as \(\:\stackrel{-}{x}\pm\:s\) , and comparisons between groups were made using the independent-samples t-test. Measurement data with non-normal distribution were expressed as M (P25, P75). Lasso regression analysis was used to screen for predictors influencing the progression of EBV infection to EBV-HLH. Multivariate logistic regression was employed to determine the final model, and the predictive performance of the model was evaluated using the ROC curve. A P-value < 0.05 was considered statistically significant. Results General Information A total of 1444 children with EBV infection were enrolled in this study. There were 769 males (56.63%) and 589 females (43.37%) in the IM group. There were 32 males (37.21%) and 54 females (62.79%) in the EBV-HLH group. There was a significant difference in gender composition between IM group and EBV-HLH group ( χ 2 value = 12.346, P < 0.001). The age of the patients in the IM group was 4.33 (2.92, 6.40) years, and that in the EBV-HLH group was 4.67 (2.75, 8.25) years. There was no significant difference in the age of onset between the EBV-HLH group and the IM group (Z value= -1.026, P = 0.305), as shown in Table 1 . Table 1 Comparison of general conditions between the two groups Gender(%) IM group(n = 1358) EBV-HLH group(n = 86) χ 2 /Zvalue P value male 769(56.63%) 32(37.21%) 12.346 < 0.001 female 589(43.37%) 54(62.79%) age 4.33(2.92, 6.40) 4.67(2.75, 8.25) -1.026 0.305 Comparison of clinical manifestations and signs between the two groups: Compared with IM group, EBV-HLH group had longer heat course, more rashes, bleeding spots, and hemophagocytosis in bone marrow smear. In the IM group, eyelid edema, nasal obstruction, snoring and tonsil enlargement were more common, and the above differences were statistically significant (P < 0.05), as shown in Table 2 . Table 2 Comparison of clinical manifestations and signs between the two groups [cases %] IM group(n = 1358) EBV-HLH group(n = 86) χ2 value P value fever 1239(91.24%) 85(98.84%) 6.131 0.013 Febrile days 1d ≤ Heating ≤ 7d 852(68.77) 20(23.53%) Fever > 7 days 387(31.23%) 65(76.47%) angina 310(22.83%) 2(2.31%) 20.071 < 0.001 otalgia 25(1.84%) 0(0%) 1.611 0.204 Stuffy nose + snoring 535(39.4%) 1(1.16%) 50.652 < 0.001 rash 119(8.76%) 20(23.26%) 19.528 < 0.001 Yellow stain 0(0%) 15(17.44%) 225.296 < 0.001 bleeder 7(0.52%) 24(27.91%) 299.888 < 0.001 Eyelid edema 738(54.34%) 3(3.49%) 83.730 < 0.001 Swollen neck lymph node 1333(98.2%) 62(72.09%) 179.421 < 0.001 Tonsil indexing 643.296 < 0.001 Ⅰ ° 255(19.57%) 8(9.30%) Ⅱ ° 920(70.61%) 5(5.81%) Ⅲ ° 128(9.82%) 0(0%) Tonsils white moss 968(71.28%) 1(1.16%) 180.141 < 0.001 hepatomegaly 528(38.88%) 59(68.60%) 263.358 < 0.001 splenomegaly 523(38.51%) 53(61.63%) 226.545 < 0.001 myelophagocytosis 3(0.22%) 53(61.63%) 801.375 < 0.001 Comparison of laboratory indicators between the two groups: There were differences in gender ( χ 2 = 12.346, P < 0.001) distribution between the IM group and the EBV-HLH group, and there were no significant differences in age (z=-1.026), but there was a large difference in sample size between the two groups. Therefore, 4:1 PSM was performed to balance gender and age differences between the two groups to ensure comparability between the two groups. After matching, there were 344 cases in the IM group (subsequently expressed as the IM* group) and 86 cases in the EBV-HLH group, and the normalized difference of variables after matching was less than 0.1, indicating good balance after matching. The difference analysis of laboratory test results between the IM and EBV-HLH groups is shown in Table 3 . The levels of white blood cell count (WBC), percentage of lymphocytes (LYP), neutrophil count (NE), lymphocyte count (LY), hemoglobin (HB), platelet count (PLT), fibrinogen (FIB), C3, immunoglobulin A (IgA), immunoglobulin G (IgG), immunoglobulin M (IgM), uric acid (UA), CD3+, CD3 + CD8+, and CD3-CD (16 + 56+) in the EBV-HLH group were lower than those in the IM group. percentage of neutrophil (N%), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), ferritin (FER), prothrombin time (PT), D-dimer, C4, aspartate aminotransferase (AST), alanine aminotransferase (ALT), glutamyl transpeptidase (GGT), lactate dehydrogenase (LDH), serumurea (UREA), triglyceride (TG), C-reactive protein (CRP), CD3 + CD4+, CD4+/CD8+, and CD3-CD19 + cell proportions and plasma EBV-DNA load were higher than those in the IM group. The difference was statistically significant ( P < 0.05). Table 3 differences in laboratory indexes between the two groups [ \(\:\stackrel{-}{x}\pm\:s\) or M (P25, P75)] IM* group(n = 344) EBV-HLH group(n = 86) Z value P value WBC(×10 9 /L) 13.98(10.43, 17.54) 2.60(1.33, 5.73) -12.639 < 0.001 NEP(%) 23.55(16.93, 30.35) 36.45(19.85, 53.60) -5.785 < 0.001 LYP(%) 66.85(59.60, 73.85) 50.00(37.58, 67.88) -6.504 < 0.001 NE(×10 9 /L) 3.23(2.16, 4.29) 0.90(0.39, 1.78) -10.075 < 0.001 LY(×10 9 /L) 9.14(6.51, 11.93) 1.15(0.55, 2.67) -12.972 < 0.001 HB(×g/L) 121.00(114.25, 127.00) 97.50(86.00, 109.00) -11.600 < 0.001 PLT(×10 9 /L) 197.00(165.00, 250.00) 61.00(33.00, 100.75) -11.730 < 0.001 NLR 0.35(0.23, 0.50) 0.72(0.29, 1.38) -6.006 < 0.001 PLR 22.48(15.00, 31.32) 54.82(27.933, 101.81) -7.824 < 0.001 FER(pol/L) 136.15(77.70, 238.38) 4971.75(1517.40, 15755.85) -13.584 < 0.001 PT(s) 13.90(13.40, 14.50) 14.90(13.00, 16.30) -3.615 < 0.001 APTT(s) 40.95(36.63, 42.25) 40.75(36.00, 53.98) -1.371 0.170 FIB(g/L) 3.05(2.62, 3.52) 1.73(1.29, 2.76) -10.514 < 0.001 D-dimer(ug/L) 830.00(532.50, 1233.00) 6710.00(3112.50, 13357.50) -12.749 < 0.001 AST(U/L) 60.00(41.55, 102.55) 213.90(77.13, 386.25) -8.332 < 0.001 ALT(U/L) 62.05(29.30, 136.83) 116.95(41.23, 246.93) -3.901 < 0.001 GGT(U/L) 22.95(12.13, 60.60) 101.65(31.28, 224.75) -7.167 < 0.001 LDH(U/L) 511.80(438.83, 611.70) 957.40(643.53, 1939.80) -9.718 < 0.001 UREA(mmol/L) 3.45(2.89, 4.06) 3.85(3.01, 5.31) -3.651 < 0.001 CREA(umol/L) 35.25(28.10, 41.78) 31.85(25.75, 40.35) -1.751 0.080 UA(umol/L) 317.15(263.38, 378.08 235.50(184.65, 285.18) -7.085 < 0.001 TG(mmol/L) 1.30(1.04, 1.66) 2.41(1.79, 3.38) -9.449 < 0.001 CRP(mg/L) 5.91(2.44, 14.79) 15.37(4.61, 33.74) -4.327 < 0.001 C3(g/L) 1.14 ± 0.21 1.01 ± 0.27 4.134 < 0.001 C4(g/L) 0.36(0.30, 0.43) 0.38(0.32, 0.46) -2.102 0.036 IgA(g/L) 1.61(1.02, 2.31) 1.04(0.63, 1.43) -5.634 < 0.001 IgG(g/L) 11.69(9.68, 13.94) 9.63(7.14, 12.28) -4.015 < 0.001 IgM(g/L) 1.70(1.31, 2.16) 0.89(0.44, 1.33) -8.762 < 0.001 CD3+% 84.30(79.00, 88.40) 79.24(66.74, 87.55) -3.367 < 0.001 CD3 + CD4+% 16.11(12.30, 21.59) 29.10(19.52, 42.18) -8.114 < 0.001 CD3 + CD8+% 61.00(49.83, 69.45) 35.71(28.57, 57.17) -7.782 < 0.001 CD4+/CD8+ 0.30(0.20, 0.40) 0.93(0.38, 1.35) -8.496 < 0.001 CD3-CD19+% 4.80(3.00, 8.10) 8.82(4.43, 16.40) -5.573 < 0.001 CD3-CD(16 + 56+)% 8.44(6.30, 12.40) 7.44(2.99, 11.80) -2.766 0.006 CD19 + 23+% 2.20(1.40, 3.80) 2.15(0.93, 3.36) -1.579 0.114 Whole blood EBV-DNA copies/ml 324000(91800, 1265000) 390500(9845, 4205000) -0.003 0.997 plasma EBV-DNA copies/ml 2180(505, 8160) 49700(1060, 362000) -6.559 < 0.001 Predictor of development of EBV-HLH from Epstein-Barr virus infection Combined with the above analysis results, with the occurrence of hemophagocytosis as the dependent variable and the laboratory indicators with significant differences between the two groups as the independent variables, they were included in the univariate logistic regression for preliminary screening, and 33 indicators with significant specificity were obtained, as shown in Table 4 . These 33 indicators can be considered as predictors of the progression of EB infection to EBV-HLH. Thirty-three predictors were incorporated into Lasso regression for characteristic selection (see Fig. 1 and Fig. 2 ). Using λ = lambda.lse (0.0443), 12 predictors were screened out, among which 4 predictors were non-zero coefficients in the Lasso cohort, namely HB, FER, FIB, and CD3 + CD4+. Lasso regression was used to screen out 4 variables, and then multi-factor logistics regression analysis was conducted on these 4 variables to obtain 4 independent risk factors: HB (OR = 1.116, 95%CI = 1.050–1.188), FER (OR = 0.996, 95%CI = 0.994–0.998), FIB (OR = 7.807, 95%CI = 2.452–24.853), and CD3 + CD4+ (OR = 0.805, 95%CI = 0.729–0.889), as shown in Table 5 . According to the plot, HB, FER, FIB, and CD3 + CD4+% were all statistically significant risk factors (all P < 0.05), as shown in Fig. 3 . Table 4 Univariate logistic regression analysis. index B Std wald P value OR 95%CI WBC(×10 9 /L) -0.523 0.055 90.406 <0.001 0.593 0.532–0.660 NEP(%) 0.067 0.009 51.135 <0.001 1.069 1.049–1.088 LYP(%) -0.067 0.009 50.743 <0.001 0.935 0.918–0.952 NE(×10 9 /L) -0.942 0.122 59.814 <0.001 0.390 0.307–0.495 LY(×10 9 /L) -0.840 0.091 85.381 <0.001 0.432 0.361–0.516 HB(×g/L) -0.163 0.018 81.825 <0.001 0.849 0.820–0.880 PLT(×10 9 /L) -0.029 0.003 96.153 <0.001 0.972 0.966–0.977 NLR 1.979 0.329 36.183 <0.001 7.236 3.797–13.791 PLR 0.038 0.005 50.589 <0.001 1.039 1.028–1.050 FER(pol/L) 0.004 0.001 47.451 <0.001 1.004 1.003–1.005 PT(s) 0.406 0.084 23.064 <0.001 1.500 1.271–1.771 Fib(×g/L) -2.242 0.248 82.035 <0.001 0.106 0.650 − 0.173 D-dimer(ug/L) 0.000 0.000 54.805 <0.001 1.000 1.000–1.000 AST(U/L) 0.007 0.001 45.326 <0.001 1.007 1.005–1.009 ALT(U/L) 0.003 0.001 14.287 <0.001 1.003 1.001–1.005 GGT(U/L) 0.009 0.001 43.194 <0.001 1.009 1.006–1.012 LDH(U/L) 0.005 0.001 50.207 <0.001 1.005 1.004–1.007 UREA(mmol/L) 0.484 0.111 19.070 <0.001 1.623 1.306–2.018 UA(umol/L) 0.006 0.001 14.608 <0.001 0.994 0.992–0.997 TG(mmol/L) 1.193 0.151 62.638 <0.001 3.297 2.454–4.431 CRP CRP(mg/L) 0.035 0.007 26.089 <0.001 1.036 1.022–1.050 C3(×g/L) -2.525 0.557 20.583 <0.001 0.080 0.027–0.238 C4(×g/L) 2.581 1.049 6.057 0.014 13.209 1.691-103.164 IgA(×g/L) -0.901 0.180 25.047 <0.001 0.406 0.286–0.578 IgG (×g/L) -0.115 0.036 10.044 0.002 0.892 0.831–0.957 IgM(×g/L) -1.728 0.241 51.238 <0.001 0.178 0.111–0.285 CD3+% -0.050 0.011 19.407 <0.001 0.951 0.930–0.973 CD3 + CD4+% 0.114 0.014 67.409 <0.001 1.121 1.091–1.152 CD3 + CD8+% -0.066 0.009 59.201 <0.001 0.937 0.921–0.952 CD4+/CD8+ 2.255 0.305 54.596 <0.001 9.539 5.244–17.351 CD3-CD19+% 0.100 0.017 33.797 <0.001 1.105 1.068–1.143 CD3-CD(16 + 56+)% -0.460 0.024 3.802 0.051 0.955 0.911-1.000 plasma EBV-DNA (copies/ml) 0.000 0.000 27.245 <0.001 1.000 1.000–1.000 Table 5 Multi-factor logistics regression analysis index B Std wald P value OR 95%CI HB(g/L) -0.110 0.320 12.222 0.001 0.896 0.842–0.953 FER(pol/L) 0.004 0.001 23.717 <0.001 1.004 1.002–1.006 FIB(g/L) -2.055 0.591 12.099 0.001 0.128 0.040–0.408 CD3 + CD4+% 0.217 0.050 18.455 <0.001 1.242 1.125–1.371 Construction of prediction model and evaluation The logistic regression model derived from the above analysis was used to evaluate the discriminative ability and accuracy of the model with the ROC curve. The results showed that HB, FER, FIB, and CD3 + CD4 + all had good predictive value, among which FER had the greatest predictive value (AUC = 0.973, P < 0.001, with 602.1 as the cut-off point, sensitivity 0.895, specificity 0.965), as shown in Fig. 4 and Table 6 . Based on multi-factor analysis, the prediction model equation of EBV-HLH infection development was F = 0.004×FER − 0.110×HB + 0.217×CD3 + CD4 + − 2.055×FIB + 8.320. The ROC curve was plotted for internal verification, and the AUC of the predicted model F was 0.997. At the optimal cut-off value of F = 56.95, the sensitivity of the model is 95.30%, and the specificity is 99.70%. According to the Hosmer-Lemeshow goodness-of-fit test, χ² = 0.696, P = 1.000, the model exhibiting good calibration ability. Table 6 ROC curve analysis of prediction model Factor AUC 95%CI P value Cut-off point sensitivity specificity HB(g/L) 0.904 0.863–0.945 <0.001 113.50 0.872 0.800 FER(pol/L) 0.973 0.953–0.993 <0.001 602.10 0.895 0.965 FIB(g/L) 0.866 0.813–0.920 <0.001 2.49 0.814 0.802 CD3 + CD4+% 0.783 0.720–0.846 <0.001 26.43 0.640 0.892 Discussion Based on acute EBV infection, the clinical manifestations and experimental indicators of EBV-HLH may be difficult to distinguish from IM in the early stage, and there is no clear predictor for early judgment of EBV-HLH. This study attempts to establish a model for predicting the development of EBV-HLH from EBV using routine clinical laboratory indicators to assist early clinical diagnosis. Using LASSO regression and multi-factor logistic regression analysis, HB, FER, FIB, and CD3 + CD4 + were finally confirmed as predictors. Based on the findings of this study, a combined prediction model was developed using the four indicators: F = 0.004×FER − 0.110×HB + 0.217×CD3 + CD4+% − 2.055×FIB + 8.320. The ROC curve was plotted for internal validation, and the AUC of prediction model was 0.997. When the optimal cut-off value is 56.95, the sensitivity of the model is 95.30%, and the specificity is 99.70%. According to the Hosmer-Lemeshow goodness-of-fit test, χ² = 0.696, P = 1.000, the model has good calibration ability. Furthermore, numerous researchers have integrated prediction model construction into early diagnostic studies for EBV-HLH. A relevant prediction model [ 6 ] (including phagocytosis, splenomegaly, cytopenia, elevated ferritin, and elevated triglycerides) was constructed by principal component analysis with a sensitivity of 95% and specificity of 94%. It has been externally verified (with a sensitivity of 98% and a specificity of 95%), and has a good value for the early prediction of EBV-HLH. Most primary EBV infections in children present as IM, which is a benign self-limiting disease with a generally good prognosis. IM is characterized by fever, angina, and lymph node enlargement [ 4 ] . In this study, the most common clinical manifestations in the IM group were cervical lymphadenopathy, tonsillar enlargement, fever, tonsillar exudate, and hepatomegaly, which is consistent with previous studies [ 7 ] . The clinical manifestations of EBV-HLH are complex and diverse. In this study, the clinical manifestations of EBV-HLH were fever, neck lymph enlargement, liver enlargement and spleen enlargement in sequence. Compared with children with IM, the fever lasted longer in children with EBV-HLH, usually more than 7 days, which is consistent with previous studies [ 8 ] . The alteration of peripheral blood parameters is a crucial factor in the diagnosis of IM and EBV-HLH. In the early stages of IM, white blood cell counts in peripheral blood may be normal or elevated, with a significantly higher proportion of lymphocytes compared to the EBV-HLH group, which is consistent with previous studies [ 9 ] . However, cytokines released during the excessive inflammatory response in EBV-HLH may suppress bone marrow hematopoiesis, leading to reduced white blood cell production [ 10 ] . Although the total white blood cell count is decreased, the percentage of neutrophils in EBV-HLH did not significantly decrease compared to IM. This may be attributed to the more pronounced impact of the cytokine storm on lymphocytes and monocytes, causing their relative or absolute numbers to decline more substantially, thereby resulting in a less significant change in the relative percentage of neutrophils [ 10 , 11 ] . In this study, EBV-HLH exhibited a notable pancytopenia among these parameters, platelet count showed the most significant decrease. This reduction is thought to be associated with decreased platelet numbers caused by activated macrophages alongside coagulation dysfunction and bleeding tendencies due to excessive phagocytosis [ 12 , 13 ] . Serum ferritin is an acute phase reaction protein that is widely distributed in liver cells, the spleen, and bone marrow. EBV-HLH is characterized by the phagocytosis of histiocytes. During this process, ferritin is released due to the destruction of blood cells, while the expression of ferritin receptors is limited by inflammation, which hinders the clearance of ferritin from serum. This leads to an increase in ferritin levels [ 14 , 15 ] . However, the degree of IM inflammation was mild, resulting in a less pronounced increase in ferritin levels. In this study, we found that ferritin demonstrates strong predictive ability for EBV-HLH resulting from Epstein-Barr virus infection, with an area under the curve (AUC) of 0.973 (Table 6 ). This finding aligns with previous studies indicating that ferritin possesses good specificity and sensitivity for distinguishing between these two conditions at early stages [ 16 ] . Nevertheless, elevated ferritin levels are not exclusive to EBV-HLH; they can also be observed in other inflammatory diseases, liver disorders, and rare hematological conditions [ 17 ] . Therefore, while ferritin serves as a useful indicator for predicting progression from EB infection to EBV-HLH development, it should be evaluated alongside other laboratory results and clinical manifestations. Diseases associated with EBV infection frequently present with liver damage, typically manifesting as mild to moderate abnormalities in liver function tests. Hepatic injury does not result directly from viral invasion of hepatocytes but rather from immune-mediated damage caused by lymphocyte infiltration and cytokine release following EBV infection—this represents the body's response to EBV-infected B cells [ 18 ] . In this study, liver function indices were slightly elevated in the IM group, whereas those with EBV-HLH exhibited significantly higher values. This suggests that the overactivation of immune cells and an uncontrollable cytokine storm lead to more severe organ inflammation and damage. When the body is in a state of high inflammation and excessive immune activation, it can trigger activation of the coagulation pathway and consumption of fibrinogen. Fibrinogen is a coagulation-related protein synthesized and secreted by the liver. It is decomposed by plasminogen activated by macrophages and inflammatory factors, thereby forming hypofibrinemia [ 19 ] . In this study, fibrinogen levels in the EBV-HLH group were significantly lower compared with those in the IM group, with a statistically significant difference. This is consistent with previous research showing that over half of children with HLH had hypofibrinemia [ 20 ] . Peripheral blood lymphocyte subsets can assess the immune function status of children. CD4 + T cells, also known as helper T cells, are commonly used to evaluate the immune function of the body and play an important role in assisting the analysis of the pathogenesis and clinical efficacy evaluation of Epstein-Barr virus infection [ 21 ] . CD8 + T cells include CTL and inhibitory T cells. They mainly recognize and kill infected or abnormal target cells specifically and can also secrete inhibitory factors. CD4+/CD8 + is an important indicator for judging whether the immune function of the body is normal. In this study, the proportion of CD8 + T cells in both groups of children increased significantly, and the ratio of CD4+/CD8 + was inverted. This was more obvious in the IM group. This is mainly related to the different target cells that Epstein-Barr virus infection acts on in different diseases. In IM, the virus mainly affects B cells and CD4 + T cells, resulting in a relative reduction in the number of CD4 + T cells, while the direct response of CD8 + T cells to the virus can lead to a significant increase in their quantity and activity. In EBV-HLH, the virus mainly infects CD8 + T cells or NK cells. CTL and NK cells lose the ability to clear virus-infected cells. Meanwhile, the abnormal proliferation and activation of lymphocytes lead to the massive release of cytokines, thereby inducing hemophagy [ 22 , 23 ] . CD3-CD (16 + 56+) cells, also known as NK cells, are an important part of the body's innate immune system and play a very important role in antiviral immunity, tumor immunity and so on. In this study, the proportion of NK cells is low in the two diseases after EB virus infection, which is more obvious in EBV-HLH. Abnormal activation of the immune system leads to abnormal activation of NK cells, thus increasing apoptosis and reducing the number and function of NK cells. Some scholars also used multivariate regression analysis to obtain relevant prediction models based on hemophagocytosis, polycytopenia, splenomegaly, immunoglobulin, fibrinogen, triglyceride, and body temperature [ 24 ] , with a sensitivity of 94.21% and a specificity of 83.02%, which also had good predictive value. Other studies have used logistic and LASSO regression to construct a nomogram prediction model based on the five indicators of ferritin, anti-EBV-NA-igg, IL-6, IL-10, and CD3-CD(16 + 56+), which has been proved to have excellent discrimination efficiency and calibration [ 25 ] . Compared with the above-mentioned models, this research model still has some deficiencies. The reasons for consideration are as follows: (1) Fewer included indicators; (2) There are significant differences between the two sets of data in some laboratory indicators (such as ferritin). The model may have overadapted to this difference, thus showing an extremely high degree of fit on the data. (3) Although the baseline confounding factors were controlled by the PSM method, the sample size difference between the two groups was still large after matching, which may affect the stability and external generalization of the model. (4) Although the internal validation demonstrated that the prediction model exhibited high accuracy, during external validation, the insufficient sample size of EBV-HLH patients and the substantial difference in ferritin concentration between the IM group and the EBV-HLH group resulted in an area under the ROC curve of 1. This suggests that the extreme ferritin levels observed in EBV-HLH cases during external validation influenced the results, rather than reflecting the model's true generalization ability. The model can be further improved by obtaining cohort data with prespecified matching ratios through multicenter cooperation. Conclusions In conclusion, EBV-HLH is characterized by an acute onset, rapid progression, high mortality, and lack of obvious specificity in early clinical manifestations and laboratory indicators. Therefore, it is essential to identify and diagnose EBV-HLH as early as possible, and standardized treatment can improve the prognosis. Decreased hemoglobin, decreased fibrinogen, elevated ferritin, and increased CD3 + CD4 + at admission have certain reference significance. However, this study has certain limitations: it is a single-center study with a single data source and a limited number of medical records. The clinical predictive value of the relevant model still needs to be further evaluated. In the future, multi-center and large-sample internal and external validations are still required to assess the feasibility and reliability of the model. Declarations Acknowledgements This is a clinical research project approved by Children’s Hospital of Soochow University. We greatly appreciate all participants in this study. Author contributions Meng Cao and Mengli Xu collected cases and experimental data, Yuewen Su wrote the main manuscript text, Yuqin Li analyzed and interpreted the experimental datas, Weifang Zhou and Shaoyan Hu designed the research and revised the manuscript. All authors have read and approved the final manuscript. Funding This study was supported by the Key Disease Project of Suzhou (LCZX202313) and Key Laboratory (SZS2023014). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of Children's Hospital of Soochow University (Ethics Approval No.: 2023CS219). We demonstrate that this study was conducted in accordance with the 1964 Declaration of Helsinki and subsequent amendments. Written informed consent was obtained from the guardians of all participants. Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References LUPO J, TRUFFOT A, ANDREANI J et al. Virological Markers in Epstein-Barr Virus-Associated Diseases[J]. Viruses, 2023, 15(3). FAJGENBAUM D C, JUNE C H. Cytokine Storm[J]. N Engl J Med. 2020;383(23):2255–73. MADKAIKAR M, SHABRISH S. Current Updates on Classification, Diagnosis and Treatment of Hemophagocytic Lymphohistiocytosis (HLH)[J]. Indian J Pediatr. 2016;83(5):434–43. LEUNG A K C, LAM J M BARANKINB. Infectious Mononucleosis: An Updated Review[J]. Curr Pediatr Rev. 2024;20(3):305–22. HENTER J I, HORNE A, ARICO M, et al. HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis[J]. Pediatr Blood Cancer. 2007;48(2):124–31. SMITS B M, VAN MONTFRANS J, MERRILL S A, et al. A Minimal Parameter Set Facilitating Early Decision-making in the Diagnosis of Hemophagocytic Lymphohistiocytosis[J]. J Clin Immunol. 2021;41(6):1219–28. WU Y, MA S, ZHANG L, et al. Clinical manifestations and laboratory results of 61 children with infectious mononucleosis[J]. J Int Med Res. 2020;48(10):300060520924550. INFECTIOUS DISEASES GROUP P B, CHINESE MEDICAL ASSOCIATION, CHILDREN N C G O E-B V I. I. Expert consensus on principles for diagnosis and treatment of diseases associated with Epstein-Barr virus infection in children[J]. Chin J Pediatr. 2021;59(11):905–11. WANG Z. [How to make the diagnosis of hemophagocytic lymphohistiocytosis][J]. Zhonghua Xue Ye Xue Za Zhi. 2016;37(7):550–3. WANG JS, WANG Y N WUL, et al. [Refractory/relapsed hemophagocytic lymphohistiocytosis treated with ruxolitinib: three cases report and literatures review][J]. Zhonghua Xue Ye Xue Za Zhi. 2019;40(1):73–5. JIN Z L, WANG Y N WANGZ. [Clinical analysis of patients with hemophagocytic lymphohistiocytosis complicated with gastrointestinal bleeding][J]. Zhonghua Xue Ye Xue Za Zhi. 2017;38(10):853–7. AL X W Z W Y, W E. Study on serum thrombopoietin levels in patients with hemophagocytic syndrome[J]. Leuk lymphoma. 2011;20(6):347–9. SARANGI R, PATHAK M, PADHI S. Ferritin in hemophagocytic lymphohistiocytosis (HLH): current concepts and controversies[J]. Clin Chim Acta. 2020;510:408–15. Chen L, Li Y, Ye X. Application of ferritin in hemophagocytic syndrome [J]. Int J Clin Invest, 2022, 6(5). CAI L, XING Y, XIA Y, et al. Comparative study of biomarkers for the early identification of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in infectious mononucleosis[J]. BMC Infect Dis. 2023;23(1):728. PHIRI K S, CALIS J C, SIYASIYA A, et al. New cut-off values for ferritin and soluble transferrin receptor for the assessment of iron deficiency in children in a high infection pressure area[J]. J Clin Pathol. 2009;62(12):1103–6. LIU R H, LI J, QU N Y, et al. [Clinical features of children with Epstein-Barr virus-related acute liver failure: an analysis of four cases][J]. Zhongguo Dang Dai Er Ke Za Zhi. 2018;20(12):1030–3. Jing Kang S, Wang F, Li. Progress in the pathogenesis, clinical features and treatment of coagulation dysfunction in hemophagocytic syndrome [J]. J experimental Hematol. 2022;30(3):959–64. NANDHAKUMAR D, LOGANATHA A, SIVASANKARAN M, et al. Hemophagocytic Lymphohistiocytosis in Children[J]. Indian J Pediatr. 2020;87(7):526–31. AL-HUSSAINI A, FAQEIH E, EL-HATTAB A W, et al. Clinical and molecular characteristics of mitochondrial DNA depletion syndrome associated with neonatal cholestasis and liver failure[J]. J Pediatr. 2014;164(3):553–e559551. SOHN D H, SOHN H J, LEE H J, et al. Measurement of CD8 + and CD4 + T Cell Frequencies Specific for EBV LMP1 and LMP2a Using mRNA-Transfected DCs[J]. PLoS ONE. 2015;10(5):e0127899. Huangfu C, Fu H. Clinical features and changes of peripheral blood lymphocyte subsets in children with EBV-associated hemophagocytic syndrome[J]. Chin Pediatr Emerg Med. 2010;17(4):330–2. SMITS B M, VAN MONTFRANS J, MERRILL S A, et al. A Minimal Parameter Set Facilitating Early Decision-making in the Diagnosis of Hemophagocytic Lymphohistiocytosis[J]. J Clin Immunol. 2021;41(6):1219–28. Lai Guo Y, Wang J, Ba, et al. Analysis of clinical characteristics of hemophagocytic syndrome and construction of diagnostic prediction model [J]. J Experimental Hematol. 2024;32(5):1594–600. HUANG R, WU D, WANG L, et al. A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis[J]. Front Immunol. 2024;15:1503118. Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Italian Journal of Pediatrics → Version 1 posted Editorial decision: Major revision 24 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 07 Sep, 2025 First submitted to journal 04 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":69964,"visible":true,"origin":"","legend":"\u003cp\u003eCross-validation diagram\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7536469/v1/2227df170f4e6157e5565934.png"},{"id":91959478,"identity":"b99c3e9a-7bd7-4681-8244-6a573924956d","added_by":"auto","created_at":"2025-09-23 07:39:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57374,"visible":true,"origin":"","legend":"\u003cp\u003eForest map with multivariate Logistic regression\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7536469/v1/46f8cb3f6bbe9a50f12760e7.png"},{"id":91963338,"identity":"50806e44-3bc7-44a8-9514-87b6cf7815fa","added_by":"auto","created_at":"2025-09-23 08:03:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42091,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of prediction model\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7536469/v1/d41654d6eaffc6abd0fb2d78.png"},{"id":100070945,"identity":"a5fe9083-1292-4309-81a7-697c02264770","added_by":"auto","created_at":"2026-01-12 16:18:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1295861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7536469/v1/45caca77-0d3d-4861-8471-778e9bb05a19.pdf"}],"financialInterests":"","formattedTitle":"Construction of a forest plot prediction model based on Lasso regression for Epstein-Barr virus associated hemophagocytic lymphohistiocytosis in children","fulltext":[{"header":"Background","content":"\u003cp\u003eEBV is a ubiquitous herpesvirus that can cause a range of diseases. Primary infection often remains asymptomatic, while some individuals may develop Infectious mononucleosis (IM), which is typically self-limiting \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, viral reactivation in a small subset of infected individuals can trigger a cytokine storm, leading to the development of HLH. This condition is usually associated with impaired T cell and natural killer (NK) cell function, which prevents the necessary negative feedback regulation during immune activation. Consequently, this impairment results in uncontrolled activation of cytotoxic T lymphocytes (CTLs) and macrophages, initiating a \"cytokine storm.\" \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Based on etiology, HLH can be classified into primary and secondary forms. Common secondary causes include infections, malignancies, and autoimmune disorders\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. EBV-HLH is a frequent form of infection-induced HLH, characterized by rapid progression, poor prognosis, and high mortality. Therefore, early diagnosis and prompt treatment are crucial for improving survival rates. By analyzing the clinical differences between IM and EBV-HLH, this study aims to identify early diagnostic indicators to guide clinical intervention.\u003c/p\u003e"},{"header":"Research object and method","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of Children's Hospital of Soochow University (Approval No.: 2023CS219). Written informed consent was obtained from the guardians of all participants.\u003c/p\u003e\u003ch3\u003eResearch object\u003c/h3\u003e\u003cp\u003eChildren with \"Epstein-Barr virus infection and hemophagocytic syndrome\" who were hospitalized in the Children's Hospital Affiliated to Soochow University from January 2019 to December 2024 were enrolled according to the inclusion and exclusion criteria formulated in this study. A total of 1358 children with infectious mononucleosis caused by Epstein-Barr virus infection (IM group) and 86 children with hemophagocytic syndrome caused by Epstein-Barr virus infection (EBV-HLH group) were finally included.\u003c/p\u003e\u003ch3\u003eInclusion criteria and exclusion criteria\u003c/h3\u003e\u003cp\u003eInclusion criteria: 1.The diagnostic criteria for IM refer to the Clinical Characteristics and Diagnostic Criteria of Epstein-Barr Virus Infectious Mononucleosis in Children\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e; 2.The diagnostic criteria for EBV-HLH were based on the HLH-2004 diagnostic protocol\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, and the EBV serological antibodies manifested as primary acute infection or positive EBV DNA.\u003c/p\u003e\u003cp\u003eExclusion criteria: 1. Do not meet the diagnostic criteria for IM and EBV-HLH; 2. Inpatient cases not first diagnosed in our hospital; 3. Patients with a large amount of missing clinical data; 4. In the HLH group, primary HLH, autoimmune-related HLH, and HLH caused by other pathogens (cytomegalovirus, mycoplasma, toxoplasma gondii, etc.) are excluded.\u003c/p\u003e\u003ch3\u003eStatistical Methods\u003c/h3\u003e\u003cp\u003eData were statistically analyzed using SPSS 27.0 and R 4.3.3 software, with propensity score matching applied for calibration. Based on significant differences in gender and age, the nearest neighbor matching method was used to perform 1:4 matching between the HLH group and the IM group. Normally distributed measurement data were expressed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\pm\\:s\\)\u003c/span\u003e\u003c/span\u003e, and comparisons between groups were made using the independent-samples t-test. Measurement data with non-normal distribution were expressed as M (P25, P75). Lasso regression analysis was used to screen for predictors influencing the progression of EBV infection to EBV-HLH. Multivariate logistic regression was employed to determine the final model, and the predictive performance of the model was evaluated using the ROC curve. A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGeneral Information\u003c/h2\u003e\u003cp\u003eA total of 1444 children with EBV infection were enrolled in this study. There were 769 males (56.63%) and 589 females (43.37%) in the IM group. There were 32 males (37.21%) and 54 females (62.79%) in the EBV-HLH group. There was a significant difference in gender composition between IM group and EBV-HLH group (\u003cb\u003eχ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e value\u0026thinsp;=\u0026thinsp;12.346, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The age of the patients in the IM group was 4.33 (2.92, 6.40) years, and that in the EBV-HLH group was 4.67 (2.75, 8.25) years. There was no significant difference in the age of onset between the EBV-HLH group and the IM group (Z value= -1.026, P\u0026thinsp;=\u0026thinsp;0.305), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of general conditions between the two groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIM group(n\u0026thinsp;=\u0026thinsp;1358)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEBV-HLH group(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/Zvalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e769(56.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32(37.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e12.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e589(43.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54(62.79%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.33(2.92, 6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.67(2.75, 8.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eComparison of clinical manifestations and signs between the two groups: Compared with IM group, EBV-HLH group had longer heat course, more rashes, bleeding spots, and hemophagocytosis in bone marrow smear. In the IM group, eyelid edema, nasal obstruction, snoring and tonsil enlargement were more common, and the above differences were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of clinical manifestations and signs between the two groups [cases %]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIM group(n\u0026thinsp;=\u0026thinsp;1358)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEBV-HLH group(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2 value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1239(91.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85(98.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFebrile days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1d\u0026thinsp;\u0026le;\u0026thinsp;Heating\u0026thinsp;\u0026le;\u0026thinsp;7d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e852(68.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(23.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFever\u0026thinsp;\u0026gt;\u0026thinsp;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e387(31.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(76.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eangina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310(22.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(2.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eotalgia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(1.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStuffy nose\u0026thinsp;+\u0026thinsp;snoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e535(39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erash\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119(8.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(23.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYellow stain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(17.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e225.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebleeder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(0.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(27.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e299.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEyelid edema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e738(54.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(3.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwollen neck lymph node\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1333(98.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62(72.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e179.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTonsil indexing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e643.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅠ \u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255(19.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(9.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅡ \u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920(70.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(5.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅢ \u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128(9.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTonsils white moss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e968(71.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehepatomegaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e528(38.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59(68.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e263.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esplenomegaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e523(38.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(61.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e226.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emyelophagocytosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(0.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(61.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e801.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eComparison of laboratory indicators between the two groups: There were differences in gender ( \u003cb\u003eχ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;12.346, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) distribution between the IM group and the EBV-HLH group, and there were no significant differences in age (z=-1.026), but there was a large difference in sample size between the two groups. Therefore, 4:1 PSM was performed to balance gender and age differences between the two groups to ensure comparability between the two groups. After matching, there were 344 cases in the IM group (subsequently expressed as the IM* group) and 86 cases in the EBV-HLH group, and the normalized difference of variables after matching was less than 0.1, indicating good balance after matching. The difference analysis of laboratory test results between the IM and EBV-HLH groups is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The levels of white blood cell count (WBC), percentage of lymphocytes (LYP), neutrophil count (NE), lymphocyte count (LY), hemoglobin (HB), platelet count (PLT), fibrinogen (FIB), C3, immunoglobulin A (IgA), immunoglobulin G (IgG), immunoglobulin M (IgM), uric acid (UA), CD3+, CD3\u0026thinsp;+\u0026thinsp;CD8+, and CD3-CD (16\u0026thinsp;+\u0026thinsp;56+) in the EBV-HLH group were lower than those in the IM group. percentage of neutrophil (N%), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), ferritin (FER), prothrombin time (PT), D-dimer, C4, aspartate aminotransferase (AST), alanine aminotransferase (ALT), glutamyl transpeptidase (GGT), lactate dehydrogenase (LDH), serumurea (UREA), triglyceride (TG), C-reactive protein (CRP), CD3\u0026thinsp;+\u0026thinsp;CD4+, CD4+/CD8+, and CD3-CD19\u0026thinsp;+\u0026thinsp;cell proportions and plasma EBV-DNA load were higher than those in the IM group. The difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003edifferences in laboratory indexes between the two groups [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\pm\\:s\\)\u003c/span\u003e\u003c/span\u003e or M (P25, P75)]\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIM* group(n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEBV-HLH group(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.98(10.43, 17.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.60(1.33, 5.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-12.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEP(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.55(16.93, 30.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.45(19.85, 53.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYP(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.85(59.60, 73.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.00(37.58, 67.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNE(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.23(2.16, 4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90(0.39, 1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLY(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.14(6.51, 11.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15(0.55, 2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-12.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.00(114.25, 127.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.50(86.00, 109.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-11.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197.00(165.00, 250.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.00(33.00, 100.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-11.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35(0.23, 0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72(0.29, 1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.48(15.00, 31.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.82(27.933, 101.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFER(pol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136.15(77.70, 238.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4971.75(1517.40, 15755.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-13.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.90(13.40, 14.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.90(13.00, 16.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.95(36.63, 42.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.75(36.00, 53.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.05(2.62, 3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73(1.29, 2.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer(ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e830.00(532.50, 1233.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6710.00(3112.50, 13357.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-12.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.00(41.55, 102.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213.90(77.13, 386.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.05(29.30, 136.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.95(41.23, 246.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.95(12.13, 60.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.65(31.28, 224.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e511.80(438.83, 611.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e957.40(643.53, 1939.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUREA(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.45(2.89, 4.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.85(3.01, 5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCREA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.25(28.10, 41.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.85(25.75, 40.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e317.15(263.38, 378.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235.50(184.65, 285.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.30(1.04, 1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.41(1.79, 3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-9.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.91(2.44, 14.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.37(4.61, 33.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC4(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36(0.30, 0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38(0.32, 0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgA(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.61(1.02, 2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04(0.63, 1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.69(9.68, 13.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.63(7.14, 12.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgM(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.70(1.31, 2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89(0.44, 1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.30(79.00, 88.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.24(66.74, 87.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD4+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.11(12.30, 21.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.10(19.52, 42.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD8+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.00(49.83, 69.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.71(28.57, 57.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4+/CD8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30(0.20, 0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93(0.38, 1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-8.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3-CD19+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.80(3.00, 8.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.82(4.43, 16.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-5.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3-CD(16\u0026thinsp;+\u0026thinsp;56+)%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.44(6.30, 12.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.44(2.99, 11.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD19\u0026thinsp;+\u0026thinsp;23+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20(1.40, 3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.15(0.93, 3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole blood EBV-DNA copies/ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e324000(91800, 1265000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390500(9845, 4205000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eplasma EBV-DNA copies/ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2180(505, 8160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49700(1060, 362000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePredictor of development of EBV-HLH from Epstein-Barr virus infection\u003c/h3\u003e\n\u003cp\u003eCombined with the above analysis results, with the occurrence of hemophagocytosis as the dependent variable and the laboratory indicators with significant differences between the two groups as the independent variables, they were included in the univariate logistic regression for preliminary screening, and 33 indicators with significant specificity were obtained, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These 33 indicators can be considered as predictors of the progression of EB infection to EBV-HLH. Thirty-three predictors were incorporated into Lasso regression for characteristic selection (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Using λ\u0026thinsp;=\u0026thinsp;lambda.lse (0.0443), 12 predictors were screened out, among which 4 predictors were non-zero coefficients in the Lasso cohort, namely HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4+. Lasso regression was used to screen out 4 variables, and then multi-factor logistics regression analysis was conducted on these 4 variables to obtain 4 independent risk factors: HB (OR\u0026thinsp;=\u0026thinsp;1.116, 95%CI\u0026thinsp;=\u0026thinsp;1.050\u0026ndash;1.188), FER (OR\u0026thinsp;=\u0026thinsp;0.996, 95%CI\u0026thinsp;=\u0026thinsp;0.994\u0026ndash;0.998), FIB (OR\u0026thinsp;=\u0026thinsp;7.807, 95%CI\u0026thinsp;=\u0026thinsp;2.452\u0026ndash;24.853), and CD3\u0026thinsp;+\u0026thinsp;CD4+ (OR\u0026thinsp;=\u0026thinsp;0.805, 95%CI\u0026thinsp;=\u0026thinsp;0.729\u0026ndash;0.889), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. According to the plot, HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4+% were all statistically significant risk factors (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate logistic regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ewald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.532\u0026ndash;0.660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEP(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.049\u0026ndash;1.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYP(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.918\u0026ndash;0.952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNE(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.307\u0026ndash;0.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLY(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.361\u0026ndash;0.516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.820\u0026ndash;0.880\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.966\u0026ndash;0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.797\u0026ndash;13.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.028\u0026ndash;1.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFER(pol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.003\u0026ndash;1.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.271\u0026ndash;1.771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFib(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.650\u0026thinsp;\u0026minus;\u0026thinsp;0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer(ug/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u0026ndash;1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.005\u0026ndash;1.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.001\u0026ndash;1.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.006\u0026ndash;1.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH(U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.004\u0026ndash;1.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUREA(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.306\u0026ndash;2.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA(umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.992\u0026ndash;0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.454\u0026ndash;4.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP CRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.022\u0026ndash;1.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.027\u0026ndash;0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC4(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.691-103.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgA(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.286\u0026ndash;0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgG (\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.831\u0026ndash;0.957\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIgM(\u0026times;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.111\u0026ndash;0.285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.930\u0026ndash;0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD4+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.091\u0026ndash;1.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD8+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.921\u0026ndash;0.952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4+/CD8+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.244\u0026ndash;17.351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3-CD19+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.068\u0026ndash;1.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3-CD(16\u0026thinsp;+\u0026thinsp;56+)%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.911-1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eplasma EBV-DNA (copies/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u0026ndash;1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulti-factor logistics regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ewald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.842\u0026ndash;0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFER(pol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.002\u0026ndash;1.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.040\u0026ndash;0.408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD4+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.125\u0026ndash;1.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eConstruction of prediction model and evaluation\u003c/h3\u003e\n\u003cp\u003eThe logistic regression model derived from the above analysis was used to evaluate the discriminative ability and accuracy of the model with the ROC curve. The results showed that HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;all had good predictive value, among which FER had the greatest predictive value (AUC\u0026thinsp;=\u0026thinsp;0.973, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with 602.1 as the cut-off point, sensitivity 0.895, specificity 0.965), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Based on multi-factor analysis, the prediction model equation of EBV-HLH infection development was F\u0026thinsp;=\u0026thinsp;0.004\u0026times;FER \u0026minus;\u0026thinsp;0.110\u0026times;HB\u0026thinsp;+\u0026thinsp;0.217\u0026times;CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;\u0026minus;\u0026thinsp;2.055\u0026times;FIB\u0026thinsp;+\u0026thinsp;8.320. The ROC curve was plotted for internal verification, and the AUC of the predicted model F was 0.997. At the optimal cut-off value of F\u0026thinsp;=\u0026thinsp;56.95, the sensitivity of the model is 95.30%, and the specificity is 99.70%. According to the Hosmer-Lemeshow goodness-of-fit test, χ\u0026sup2; = 0.696, P\u0026thinsp;=\u0026thinsp;1.000, the model exhibiting good calibration ability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC curve analysis of prediction model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCut-off point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003esensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003especificity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.863\u0026ndash;0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e113.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFER(pol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.953\u0026ndash;0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e602.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.813\u0026ndash;0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD3\u0026thinsp;+\u0026thinsp;CD4+%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.720\u0026ndash;0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on acute EBV infection, the clinical manifestations and experimental indicators of EBV-HLH may be difficult to distinguish from IM in the early stage, and there is no clear predictor for early judgment of EBV-HLH. This study attempts to establish a model for predicting the development of EBV-HLH from EBV using routine clinical laboratory indicators to assist early clinical diagnosis. Using LASSO regression and multi-factor logistic regression analysis, HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;were finally confirmed as predictors. Based on the findings of this study, a combined prediction model was developed using the four indicators: F\u0026thinsp;=\u0026thinsp;0.004\u0026times;FER \u0026minus;\u0026thinsp;0.110\u0026times;HB\u0026thinsp;+\u0026thinsp;0.217\u0026times;CD3\u0026thinsp;+\u0026thinsp;CD4+% \u0026minus;\u0026thinsp;2.055\u0026times;FIB\u0026thinsp;+\u0026thinsp;8.320. The ROC curve was plotted for internal validation, and the AUC of prediction model was 0.997. When the optimal cut-off value is 56.95, the sensitivity of the model is 95.30%, and the specificity is 99.70%. According to the Hosmer-Lemeshow goodness-of-fit test, χ\u0026sup2; = 0.696, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000, the model has good calibration ability. Furthermore, numerous researchers have integrated prediction model construction into early diagnostic studies for EBV-HLH. A relevant prediction model\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e (including phagocytosis, splenomegaly, cytopenia, elevated ferritin, and elevated triglycerides) was constructed by principal component analysis with a sensitivity of 95% and specificity of 94%. It has been externally verified (with a sensitivity of 98% and a specificity of 95%), and has a good value for the early prediction of EBV-HLH.\u003c/p\u003e\u003cp\u003eMost primary EBV infections in children present as IM, which is a benign self-limiting disease with a generally good prognosis. IM is characterized by fever, angina, and lymph node enlargement \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In this study, the most common clinical manifestations in the IM group were cervical lymphadenopathy, tonsillar enlargement, fever, tonsillar exudate, and hepatomegaly, which is consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The clinical manifestations of EBV-HLH are complex and diverse. In this study, the clinical manifestations of EBV-HLH were fever, neck lymph enlargement, liver enlargement and spleen enlargement in sequence. Compared with children with IM, the fever lasted longer in children with EBV-HLH, usually more than 7 days, which is consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe alteration of peripheral blood parameters is a crucial factor in the diagnosis of IM and EBV-HLH. In the early stages of IM, white blood cell counts in peripheral blood may be normal or elevated, with a significantly higher proportion of lymphocytes compared to the EBV-HLH group, which is consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, cytokines released during the excessive inflammatory response in EBV-HLH may suppress bone marrow hematopoiesis, leading to reduced white blood cell production\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Although the total white blood cell count is decreased, the percentage of neutrophils in EBV-HLH did not significantly decrease compared to IM. This may be attributed to the more pronounced impact of the cytokine storm on lymphocytes and monocytes, causing their relative or absolute numbers to decline more substantially, thereby resulting in a less significant change in the relative percentage of neutrophils\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In this study, EBV-HLH exhibited a notable pancytopenia among these parameters, platelet count showed the most significant decrease. This reduction is thought to be associated with decreased platelet numbers caused by activated macrophages alongside coagulation dysfunction and bleeding tendencies due to excessive phagocytosis\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSerum ferritin is an acute phase reaction protein that is widely distributed in liver cells, the spleen, and bone marrow. EBV-HLH is characterized by the phagocytosis of histiocytes. During this process, ferritin is released due to the destruction of blood cells, while the expression of ferritin receptors is limited by inflammation, which hinders the clearance of ferritin from serum. This leads to an increase in ferritin levels\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. However, the degree of IM inflammation was mild, resulting in a less pronounced increase in ferritin levels. In this study, we found that ferritin demonstrates strong predictive ability for EBV-HLH resulting from Epstein-Barr virus infection, with an area under the curve (AUC) of 0.973 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This finding aligns with previous studies indicating that ferritin possesses good specificity and sensitivity for distinguishing between these two conditions at early stages\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, elevated ferritin levels are not exclusive to EBV-HLH; they can also be observed in other inflammatory diseases, liver disorders, and rare hematological conditions\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Therefore, while ferritin serves as a useful indicator for predicting progression from EB infection to EBV-HLH development, it should be evaluated alongside other laboratory results and clinical manifestations.\u003c/p\u003e\u003cp\u003eDiseases associated with EBV infection frequently present with liver damage, typically manifesting as mild to moderate abnormalities in liver function tests. Hepatic injury does not result directly from viral invasion of hepatocytes but rather from immune-mediated damage caused by lymphocyte infiltration and cytokine release following EBV infection\u0026mdash;this represents the body's response to EBV-infected B cells\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In this study, liver function indices were slightly elevated in the IM group, whereas those with EBV-HLH exhibited significantly higher values. This suggests that the overactivation of immune cells and an uncontrollable cytokine storm lead to more severe organ inflammation and damage.\u003c/p\u003e\u003cp\u003eWhen the body is in a state of high inflammation and excessive immune activation, it can trigger activation of the coagulation pathway and consumption of fibrinogen. Fibrinogen is a coagulation-related protein synthesized and secreted by the liver. It is decomposed by plasminogen activated by macrophages and inflammatory factors, thereby forming hypofibrinemia\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In this study, fibrinogen levels in the EBV-HLH group were significantly lower compared with those in the IM group, with a statistically significant difference. This is consistent with previous research showing that over half of children with HLH had hypofibrinemia\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePeripheral blood lymphocyte subsets can assess the immune function status of children. CD4\u0026thinsp;+\u0026thinsp;T cells, also known as helper T cells, are commonly used to evaluate the immune function of the body and play an important role in assisting the analysis of the pathogenesis and clinical efficacy evaluation of Epstein-Barr virus infection\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. CD8\u0026thinsp;+\u0026thinsp;T cells include CTL and inhibitory T cells. They mainly recognize and kill infected or abnormal target cells specifically and can also secrete inhibitory factors. CD4+/CD8\u0026thinsp;+\u0026thinsp;is an important indicator for judging whether the immune function of the body is normal. In this study, the proportion of CD8\u0026thinsp;+\u0026thinsp;T cells in both groups of children increased significantly, and the ratio of CD4+/CD8\u0026thinsp;+\u0026thinsp;was inverted. This was more obvious in the IM group. This is mainly related to the different target cells that Epstein-Barr virus infection acts on in different diseases. In IM, the virus mainly affects B cells and CD4\u0026thinsp;+\u0026thinsp;T cells, resulting in a relative reduction in the number of CD4\u0026thinsp;+\u0026thinsp;T cells, while the direct response of CD8\u0026thinsp;+\u0026thinsp;T cells to the virus can lead to a significant increase in their quantity and activity. In EBV-HLH, the virus mainly infects CD8\u0026thinsp;+\u0026thinsp;T cells or NK cells. CTL and NK cells lose the ability to clear virus-infected cells. Meanwhile, the abnormal proliferation and activation of lymphocytes lead to the massive release of cytokines, thereby inducing hemophagy\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. CD3-CD (16\u0026thinsp;+\u0026thinsp;56+) cells, also known as NK cells, are an important part of the body's innate immune system and play a very important role in antiviral immunity, tumor immunity and so on. In this study, the proportion of NK cells is low in the two diseases after EB virus infection, which is more obvious in EBV-HLH. Abnormal activation of the immune system leads to abnormal activation of NK cells, thus increasing apoptosis and reducing the number and function of NK cells.\u003c/p\u003e\u003cp\u003eSome scholars also used multivariate regression analysis to obtain relevant prediction models based on hemophagocytosis, polycytopenia, splenomegaly, immunoglobulin, fibrinogen, triglyceride, and body temperature\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, with a sensitivity of 94.21% and a specificity of 83.02%, which also had good predictive value. Other studies have used logistic and LASSO regression to construct a nomogram prediction model based on the five indicators of ferritin, anti-EBV-NA-igg, IL-6, IL-10, and CD3-CD(16\u0026thinsp;+\u0026thinsp;56+), which has been proved to have excellent discrimination efficiency and calibration\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Compared with the above-mentioned models, this research model still has some deficiencies. The reasons for consideration are as follows: (1) Fewer included indicators; (2) There are significant differences between the two sets of data in some laboratory indicators (such as ferritin). The model may have overadapted to this difference, thus showing an extremely high degree of fit on the data. (3) Although the baseline confounding factors were controlled by the PSM method, the sample size difference between the two groups was still large after matching, which may affect the stability and external generalization of the model. (4) Although the internal validation demonstrated that the prediction model exhibited high accuracy, during external validation, the insufficient sample size of EBV-HLH patients and the substantial difference in ferritin concentration between the IM group and the EBV-HLH group resulted in an area under the ROC curve of 1. This suggests that the extreme ferritin levels observed in EBV-HLH cases during external validation influenced the results, rather than reflecting the model's true generalization ability. The model can be further improved by obtaining cohort data with prespecified matching ratios through multicenter cooperation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, EBV-HLH is characterized by an acute onset, rapid progression, high mortality, and lack of obvious specificity in early clinical manifestations and laboratory indicators. Therefore, it is essential to identify and diagnose EBV-HLH as early as possible, and standardized treatment can improve the prognosis. Decreased hemoglobin, decreased fibrinogen, elevated ferritin, and increased CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;at admission have certain reference significance. However, this study has certain limitations: it is a single-center study with a single data source and a limited number of medical records. The clinical predictive value of the relevant model still needs to be further evaluated. In the future, multi-center and large-sample internal and external validations are still required to assess the feasibility and reliability of the model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a clinical research project approved by Children\u0026rsquo;s Hospital of Soochow University. \u0026nbsp; We greatly appreciate all participants in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeng Cao and Mengli Xu collected cases and experimental data, Yuewen Su wrote the main manuscript text, Yuqin Li analyzed and interpreted the experimental datas, Weifang Zhou and Shaoyan Hu designed the research and revised the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Disease Project of Suzhou (LCZX202313) and Key Laboratory (SZS2023014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Children\u0026apos;s Hospital of Soochow University (Ethics Approval No.: 2023CS219). We demonstrate that this study was conducted in accordance with the 1964 Declaration of Helsinki and subsequent amendments. Written informed consent was obtained from the guardians of all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLUPO J, TRUFFOT A, ANDREANI J et al. Virological Markers in Epstein-Barr Virus-Associated Diseases[J]. Viruses, 2023, 15(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFAJGENBAUM D C, JUNE C H. Cytokine Storm[J]. N Engl J Med. 2020;383(23):2255\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMADKAIKAR M, SHABRISH S. Current Updates on Classification, Diagnosis and Treatment of Hemophagocytic Lymphohistiocytosis (HLH)[J]. Indian J Pediatr. 2016;83(5):434\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLEUNG A K C, LAM J M BARANKINB. Infectious Mononucleosis: An Updated Review[J]. Curr Pediatr Rev. 2024;20(3):305\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHENTER J I, HORNE A, ARICO M, et al. HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis[J]. Pediatr Blood Cancer. 2007;48(2):124\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSMITS B M, VAN MONTFRANS J, MERRILL S A, et al. A Minimal Parameter Set Facilitating Early Decision-making in the Diagnosis of Hemophagocytic Lymphohistiocytosis[J]. J Clin Immunol. 2021;41(6):1219\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWU Y, MA S, ZHANG L, et al. Clinical manifestations and laboratory results of 61 children with infectious mononucleosis[J]. J Int Med Res. 2020;48(10):300060520924550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eINFECTIOUS DISEASES GROUP P B, CHINESE MEDICAL ASSOCIATION, CHILDREN N C G O E-B V I. I. Expert consensus on principles for diagnosis and treatment of diseases associated with Epstein-Barr virus infection in children[J]. Chin J Pediatr. 2021;59(11):905\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWANG Z. [How to make the diagnosis of hemophagocytic lymphohistiocytosis][J]. Zhonghua Xue Ye Xue Za Zhi. 2016;37(7):550\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWANG JS, WANG Y N WUL, et al. [Refractory/relapsed hemophagocytic lymphohistiocytosis treated with ruxolitinib: three cases report and literatures review][J]. Zhonghua Xue Ye Xue Za Zhi. 2019;40(1):73\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJIN Z L, WANG Y N WANGZ. [Clinical analysis of patients with hemophagocytic lymphohistiocytosis complicated with gastrointestinal bleeding][J]. Zhonghua Xue Ye Xue Za Zhi. 2017;38(10):853\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAL X W Z W Y, W E. Study on serum thrombopoietin levels in patients with hemophagocytic syndrome[J]. Leuk lymphoma. 2011;20(6):347\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSARANGI R, PATHAK M, PADHI S. Ferritin in hemophagocytic lymphohistiocytosis (HLH): current concepts and controversies[J]. Clin Chim Acta. 2020;510:408\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Li Y, Ye X. Application of ferritin in hemophagocytic syndrome [J]. Int J Clin Invest, 2022, 6(5).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCAI L, XING Y, XIA Y, et al. Comparative study of biomarkers for the early identification of Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in infectious mononucleosis[J]. BMC Infect Dis. 2023;23(1):728.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePHIRI K S, CALIS J C, SIYASIYA A, et al. New cut-off values for ferritin and soluble transferrin receptor for the assessment of iron deficiency in children in a high infection pressure area[J]. J Clin Pathol. 2009;62(12):1103\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLIU R H, LI J, QU N Y, et al. [Clinical features of children with Epstein-Barr virus-related acute liver failure: an analysis of four cases][J]. Zhongguo Dang Dai Er Ke Za Zhi. 2018;20(12):1030\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJing Kang S, Wang F, Li. Progress in the pathogenesis, clinical features and treatment of coagulation dysfunction in hemophagocytic syndrome [J]. J experimental Hematol. 2022;30(3):959\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNANDHAKUMAR D, LOGANATHA A, SIVASANKARAN M, et al. Hemophagocytic Lymphohistiocytosis in Children[J]. Indian J Pediatr. 2020;87(7):526\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAL-HUSSAINI A, FAQEIH E, EL-HATTAB A W, et al. Clinical and molecular characteristics of mitochondrial DNA depletion syndrome associated with neonatal cholestasis and liver failure[J]. J Pediatr. 2014;164(3):553\u0026ndash;e559551.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSOHN D H, SOHN H J, LEE H J, et al. Measurement of CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T Cell Frequencies Specific for EBV LMP1 and LMP2a Using mRNA-Transfected DCs[J]. PLoS ONE. 2015;10(5):e0127899.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuangfu C, Fu H. Clinical features and changes of peripheral blood lymphocyte subsets in children with EBV-associated hemophagocytic syndrome[J]. Chin Pediatr Emerg Med. 2010;17(4):330\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSMITS B M, VAN MONTFRANS J, MERRILL S A, et al. A Minimal Parameter Set Facilitating Early Decision-making in the Diagnosis of Hemophagocytic Lymphohistiocytosis[J]. J Clin Immunol. 2021;41(6):1219\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLai Guo Y, Wang J, Ba, et al. Analysis of clinical characteristics of hemophagocytic syndrome and construction of diagnostic prediction model [J]. J Experimental Hematol. 2024;32(5):1594\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHUANG R, WU D, WANG L, et al. A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis[J]. Front Immunol. 2024;15:1503118.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epstein-Barr virus, Infectious mononucleosis, Hemophagocytic lymphohistiocytosis, Children","lastPublishedDoi":"10.21203/rs.3.rs-7536469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7536469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify risk factors for the progression of Epstein-Barr virus(EBV) infection to Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis (EBV-HLH) and guide clinical intervention by analyzing the clinical data and laboratory examination between infectious mononucleosis and EBV-HLH infection using a forest plot prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eClinical data and laboratory tests of children with \"Epstein-Barr virus infection and hemophagocytic lymphohistiocytosis \" who were hospitalized in Children's Hospital of Soochow University from January 2019 to December 2024 were collected. A total of 1358 children of infectious mononucleosis associated with EBV (IM group) and 86 children of hemophagocytic lymphohistiocytosis associated with EBV (EBV-HLH group) were included. The differences between the groups were retrospectively analyzed and regression analysis was performed. The proximity matching method was selected for 1:4 matching between the EBV-HLH group and the IM group. The forest plot prediction model was established based on Lasso regression to analyze the clinical differences between the IM group and the EBV-HLH group.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eLasso regression model screening identified hemoglobin (HB), ferritin (FER), fibrinogen (FIB) and CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;as hexhibiting good predictive value for EBV-HLH, with areas under the receiver operating characteristic (ROC) curve of 0.904, 0.973, 0.866 and 0.783, and specificities of 0.799, 0.965, 0.802 and 0.892, respectively. The prediction model constructed using HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;showed excellent predictive accuracy. With an optimal cut-off value of F\u0026thinsp;=\u0026thinsp;56.95, the model achieved a sensitivity of 95.30% and a specificity of 99.70%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe early diagnosis of EBV-HLH lacks specific indicators. In this study, a predictive model for EBV-HLH was established using LASSO regression, incorporating four key parameters (HB, FER, FIB, and CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;T-cell subsets). This model may serve as a screening tool for the early diagnosis of EBV-HLH and provide a diagnostic basis for clinical practice.​\u003c/p\u003e","manuscriptTitle":"Construction of a forest plot prediction model based on Lasso regression for Epstein-Barr virus associated hemophagocytic lymphohistiocytosis in children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:39:12","doi":"10.21203/rs.3.rs-7536469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-10-25T03:25:14+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-24T12:53:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-14T22:37:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-08T02:42:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Italian Journal of Pediatrics","date":"2025-09-04T09:08:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f2f2a6d-7b39-4b2c-b29c-b6521861b8a3","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:16:37+00:00","versionOfRecord":{"articleIdentity":"rs-7536469","link":"https://doi.org/10.1186/s13052-025-02191-5","journal":{"identity":"italian-journal-of-pediatrics","isVorOnly":false,"title":"Italian Journal of Pediatrics"},"publishedOn":"2026-01-05 15:57:07","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-09-23 07:39:12","video":"","vorDoi":"10.1186/s13052-025-02191-5","vorDoiUrl":"https://doi.org/10.1186/s13052-025-02191-5","workflowStages":[]},"version":"v1","identity":"rs-7536469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7536469","identity":"rs-7536469","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}