Developing a nomogram for predicting chronic complications in pediatric acute suppurative arthritis

Developing a nomogram for predicting chronic complications in pediatric acute suppurative arthritis | 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 Developing a nomogram for predicting chronic complications in pediatric acute suppurative arthritis Yang Zhang, Federico Canavese, FuXing Xun, JingChun Li, HongWen Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797237/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pediatric acute suppurative arthritis (ASA) carries a risk of chronic complications, and predicting these complications is crucial for optimizing prognosis. We sought to develop a risk prediction model to identify chronic complications in children with ASA. Methods This retrospective observational study enrolled children (ages one month to 18 years) diagnosed with ASA who were hospitalized at a tertiary pediatric hospital between 2016 and 2023. We documented clinical management, complication status, and sequelae, and constructed a multivariate logistic regression model for predicting chronic complications. Results A total of 95 children were identified, 16.8% of whom experienced chronic complications from ASA over a 12-month follow-up period. Univariate logistic analysis identified the following factors associated with chronic complication development: white blood cell (WBC), serum amyloid A (SAA), hematocrit, hemoglobin, mean platelet volume, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) on admission; SAA, hematocrit (HCT), and hemoglobin (HGB) at discharge; bacteremia; Staphylococcus aureus detection; bone abscess; delayed source control; bone debridement; isolated arthritis; and arthritis combined with dislocation or subluxation (all p < 0.05). On further multivariate logistic regression analysis, we identified four independent predictors: WBC on admission (OR = 1.165, 95% CI: 1.038–1.308), ALT on admission (OR = 1.014, 95% CI: 1.004–1.025), SAA at discharge (OR = 1.153, 95% CI: 1.029–1.292), and arthritis combined with dislocation or subluxation (OR = 28.134, 95% CI: 3.691–214.431). The area under the receiver operating characteristic (ROC) curve was 0.882 (95% CI: 0.786–0.979). The logistic regression model formula was: Log(P) = -8.459 + 0.153×WBC on admission + 0.014×ALT on admission + 0.142×SAA at discharge + 3.337×arthritis combined with dislocation or subluxation. Conclusion The prediction model for chronic complications of pediatric ASA incorporates four key variables: WBC on admission, ALT on admission, SAA at discharge, and arthritis combined with dislocation or subluxation. This model has been shown to effectively predict chronic complication risk in children with ASA. Acute suppurative arthritis Children Risk prediction Chronic complications Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute suppurative arthritis (ASA) is an infection of the synovial joints, predominantly affecting young children. In developed countries, the incidence of pediatric ASA ranges from 1–5 per 100,000 children [1]. Notably, ASA is a potentially destructive condition, with a high rate of severe and long-term complications in growing children [2]. Chronic complications associated with ASA include growth arrest, pathological fractures, avascular necrosis, joint stiffness, chronic dislocation, and joint deformities. Furthermore, infection recurrence may occur in cases of inadequate treatment or poor treatment compliance [3]. However, the variable clinical courses observed in pediatric patients have hindered the development of standardized management guidelines. Furthermore, in Asia, osteoarticular pathogens like Staphylococcus aureus show different characteristics compared to similar pathogens in the United States and Europe. This highlights the need for custom prediction models [4, 5]. Most existing studies on ASA have primarily relied on retrospective analyses to identify factors associated with chronic complications and disease prognosis [2,3,6–8]. In 2023, Gouveia et al. became the first to successfully develop and validate a risk prediction model for acute and chronic complications in children with osteoarticular infections [9]. The primary objective of our study was to develop a nomogram to predict chronic complications in pediatric patients with ASA. The secondary aim was to compare the newly developed nomogram with the Gouveia system [9] previously reported. Methods We conducted a retrospective review of the medical records of children between one month and 18 years of age who were hospitalized at our institution between January 2016 and December 2023 with ASA and symptoms lasting no longer than 14 days before admission. The Ethics Committee of our Institution approved this study (No. [2025]242A01). The inclusion criteria for this study were: (1) patients aged 1 month to 18 years; (2) patients with confirmed diagnosis of ASA; (3) patients whose symptoms began within 14 days before admission; (4) patients with at least one year of follow-up after the first onset of symptoms. The exclusion criteria were: (1) patients with pre-existing disease, such as blood disease, category of impediment disease, or cardiovascular disorder; neoplasm malignant; or other chronic disease; (2) patients diagnosed with juvenile arthritis, synovitis, pulmonary tuberculosis, immunological disease, or arthritis reactive after admission; (3) patients with osteomyelitis of flat bones, irregular bones, or small bones (e.g., vertebral column, jawbone, clavicle, or metacarpal bone), septic arthritis of small joints, or secondary infection after open fracture or surgery; (4) exclusion criteria also included patients with follow-up duration less than 1 year. Diagnostic criteria The direct and indirect diagnostic criteria for Acute suppurative arthritis are as follows: - Direct diagnosis is established if pathogenic microorganisms are detected in blood, pus, or tissue cultures. - Indirect diagnosis can be made when cultures are negative but symptoms such as dysphoria, local swelling, pain, limited range of mobility, elevated C-reactive protein (CRP) levels, imaging signs, or positive Magnetic resonance imaging (MRI) are present. Body fluid or tissue cultures can be obtained through joint incision and drainage, Bone abscess drainage, or Biopsy bone. Cultures from joints or soft tissues near the site of bone infection can also be used. For disseminated infections, cultures from various sources such as mediastinal fluid or pleural fluid are used. Chronic complications and scoring Chronic complications include Growth arrest, Pathological fracture, Ischemic necrosis, joint ankylosis, limited range of joint mobility, Chronic dislocation, Joint deformity and Osteomyelitis chronic. Osteomyelitis chronic is defined as persistent or recurrent symptoms and signs related to epiphyseal, metaphyseal or skeletal changes visible on plain X-ray. It requires at least 12 weeks of Antibiotic therapy. In the Gouveia scoring system, the final model for estimating the probability of sequelae at 6 months is πSeq-6 = OddsSeq-6/1.01 + OddsSeq-6, where OddsSeq-6 = exp [-4.75 + 1.41· (age ≥ 4 years) + 1.52· (CRP ≥ 110 mg) + 2.22·disseminated disease + 1.70·Bone abscess] [9]. Statistical Analysis Data analyses were performed using SPSS version 27.0 (International Business Machines Corporation, Armonk, NY, USA). Normally distributed quantitative data were presented as mean (standard deviation), and non-normally distributed quantitative data as median (P25, P75). Categorical data were described by frequencies and proportions. For between-group comparisons, independent samples t-tests were used for normally distributed quantitative data, nonparametric tests for non-normally distributed data, and chi-square tests for categorical data. Missing values were handled via multiple imputation. Univariate analyses were conducted for all variables; statistically significant factors were included in a stepwise backward multiple logistic regression model. Using R software version 4.2.2 (R Core Team, Auckland, New Zealand), a nomogram for predicting childhood ASA chronic complications was developed based on identified independent predictors. The model was optimized by 1000 bootstrap iterations, and its predictive performance was internally validated via receiver operating characteristic (ROC) curves. The Hosmer-Lemeshow test evaluated the goodness-of-fit between predicted and observed outcomes, and calibration curves assessed calibration. Decision curve analysis quantified the nomogram's net benefit across threshold probabilities to validate its utility. A p-value < 0.05 was considered statistically significant. The Gouveia scoring system was applied to predict cohort data risks, enabling direct performance comparison with the present study's nomogram. Results This study enrolled 95 patients with ASA, including 51 males (53.7%) and 44 females (46.3%) (Fig. 1 ). Table 1 and Table 2 present the demographic and clinical details (Table 1 and Table 2 ). Among these cases, 71 (74.7%) were diagnosed with isolated septic arthritis, 16 (16.8%) with septic arthritis complicated by osteomyelitis, 7 (7.4%) with arthritis accompanied by dislocation or subluxation, and 1 (1.1%) with multifocal infection. Regarding laterality, ASA affected the left side in 54 patients (56.8%) and the right side in 41 patients (43.2%). The most commonly involved joints were the hip (n = 34, 35.8%) and knee (n = 31, 32.6%). Table 1 Relationship between predictive and outcome variables and chronic complications in children with ASA. Missing Data a Variables Total cases (n = 95) Non - chronic complication group (n = 79) Chronic complication group (n = 16) P b Age, years, median (IQR) 0.83(0.42,2.00) 0.92(0.50,3.00) 0.67(0.10,0.81) 0.092 Age ≥ 4 years, n (%) 16(16.8) 16(20.3) 0(0) 0.992 Gender, n (%) 0.746 Male 51(53.7) 43(54.4) 8(50) Female 44(46.3) 36(45.6) 8(50) Temperature on Admission, ℃, median (IQR) 36.80(36.50,37.70) 36.80(36.50,37.70) 36.75(36.50,37.80) 0.969 Symptom duration at admission, days, median (IQR) 7.00(5.00,10.00) 7.00(5.00,10.00) 7.00(4.25,11.00) 0.886 Duration of hospital stay, days, median (IQR) 15.00(11.00,21.00) 15.00(11.00,19.00) 19.00(12.00,22.75) 0.139 Antibiotic treatment before Admission, n (%) 54(56.8) 44(55.7) 10(62.5) 0.617 Laterality, Left, n (%) 54(56.8) 45(57) 9(56.3) 0.958 Site involved, n (%) Hip joint 34(35.8) 26(32.9) 8(50) 0.199 Knee joint 31(32.6) 26(32.9) 5(31.3) 0.897 Ankle joint 10(10.5) 10(12.7) 0(0) 0.990 Elbow joint 6(6.3) 6(7.6) 0(0) 0.992 Shoulder joint 12(12.6) 9(11.4) 3(18.8) 0.424 Wrist joint 1(1.1) 1(1.3) 0(0) 0.992 Small joint 1(1.1) 1(1.3) 0(0) 0.992 Classification of ASA, n (%) Isolated ASA 71(74.7) 63(79.7) 8(50) 0.017 Combined AHO 16(16.8) 13(16.5) 3(18.8) 0.823 Combined Dislocation or Subluxation 7(7.4) 2(2.5) 5(31.3) 0.001 Combined Multifocal infection 1(1.1) 1(1.3) 0(0) 0.992 Disseminated infection, n (%) 2(2.1) 2(2.5) 0(0) 0.993 Time to initiate antibiotic treatment after admission, days, median (IQR) 1.00(1.00,1.00) 1.00(1.00,1.00) 1.00(1.00,1.75) 0.377 CRP ≥ 110 mg/L on Admission, n (%) 23(24.2) 21(26.6) 2(12.5) 0.244 11 Duration of antibiotic treatment, days, median (IQR) 15.00(11.00,20.75) 14.50(10.75,18.50) 18.50(11.75,23.00) 0.099 CRP ≥ 100 mg/L after 2–4 d of antibiotics, n (%) 69(72.6) 55(69.6) 14(87.5) 0.115 Fever > 48 h of antibiotics, n (%) 27(28.4) 21(26.6) 6(37.5) 0.380 12 Change of antibiotics during intravenous drip, n (%) 45(54.2) 35(50.7) 10(71.4) 0.223 Bone abscess, n (%) 16(16.8) 9(11.4) 7(43.8) 0.003 1 Bacteremia, n (%) 47(50) 35(44.3) 12(80) 0.015 1 S. aureus detected, n (%) 26(27.7) 18(23.1) 8(50) 0.042 1 MRSA detected, n (%) 7(7.4) 4(5.1) 3(18.8) 0.120 Bone debridement, n (%) 21(22.1) 14(17.7) 7(43.8) 0.028 Source control, n (%) 79(83.2) 63(79.7) 16(100) 0.992 11 Delayed source control, n (%) 26(31) 25(35.7) 1(7.1) 0.032 Multiple surgeries during the treatment course, n (%) 10(10.5) 7(8.9) 3(18.8) 0.251 Oral medications prescribed for taking after discharge, n (%) 73(76.8) 61(77.2) 12(75) 0.848 a: The number of missing cases in the variables. b: The P-value was obtained from univariate logistic regression analysis of the data with missing items after multiple imputation. Table 2 Relationship between chronic complications in children with ASA and predictive and outcome variables (laboratory serum biomarkers). Missing Data a Variables Total cases (n = 95) Non - chronic complication group (n = 79) Chronic complication group (n = 16) P b 1 WBC on admission, ×10 9 /L, median (IQR) 14.45(10.80,18.50) 14.00(10.80,18.20) 17.50(14.00,19.80) 0.12 3 WBC at discharge, ×10 9 /L, median (IQR) 8.90(6.70,10.48) 8.80(6.65,10.60) 9.40(7.40,10.20) 0.75 1 CRP on admission, mg/L, median (IQR) 45.88(16.85,104.93) 46.06(17.00,119.28) 45.70(15.45,77.31) 0.78 4 CRP at discharge, mg/L, median (IQR) 3.90(0.66,10.20) 2.86(0.52,7.27) 10.50(1.64,20.08) 0.037 29 ESR on admission, mm/L, mean (SD) 47.65(29.78) 48.14(31.51) 44.90(18.12) 0.653 46 ESR at discharge, mm/L, mean (SD) 28.59(18.38) 28.59(18.86) 28.58(17.61) 0.999 51 PCT on admission, %, median (IQR) 0.17(0.10,0.19) 0.16(0.10,0.43) 0.18(0.12,0.56) 0.637 74 PCT at discharge, %, median (IQR) 0.10(0.10,0.19) 0.10(0.10,0.18) 0.29(0.05,0.52) 0.853 55 SAA on admission, mg/L, mean (SD) 163.29(103.05) 178.66(99.35) 101.80(100.28) 0.058 81 SAA at discharge, mg/L, median (IQR) 10.01(6.00,35.39) 6.64(6.00,13.69) 40.16(36.98,55.73) 0.022 1 EO# on admission, ×10 9 /L, median (IQR) 0.11(0.01,0.28) 0.09(0.01,0.28) 0.16(0.01,0.30) 0.868 3 EO# at discharge, ×10 9 /L, median (IQR) 0.27(0.16,0.39) 0.27(0.17,0.40) 0.19(0.15,0.33) 0.161 1 EO% on admission, %, median (IQR) 1.00(0.00,2.00) 1.00(0.00,2.00) 1.00(0.00,2.00) 0.696 3 EO% at discharge, %, median (IQR) 3.00(2.00,4.00) 3.00(2.00,4.00) 2.00(1.00,4.00) 0.131 1 NEUT# on admission, ×10 9 /L, median (IQR) 7.07(4.23,11.00) 6.76(4.21,11.22) 7.77(4.92,10.92) 0.613 3 NEUT# at discharge, ×10 9 /L, median (IQR) 2.74(1.80,3.98) 2.72(1.78,4.03) 2.75(2.29,3.52) 0.992 1 NEUT% on admission, %, mean (SD) 49.31(18.26) 49.77(18.27) 46.87(18.64) 0.575 3 NEUT% at discharge, %, median (IQR) 2.74(1.80,3.98) 31.00(22.00,43.50) 27.00(23.00,37.00) 0.615 1 LYMPH# on admission, ×10 9 /L, mean (SD) 5.74(2.91) 5.54(2.91) 6.83(2.75) 0.116 3 LYMPH# at discharge, ×10 9 /L, mean (SD) 5.24(2.22) 5.22(2.18) 5.38(2.50) 0.795 1 LYMPH% on admission, %, median (IQR) 38.50(26.00,52.00) 37.00(25.00,52.00) 40.00(30.00,52.00) 0.442 3 LYMPH% at discharge, %, median (IQR) 60.50(49.00,66.75) 60.00(47.00,66.50) 62.00(52.00,69.00) 0.492 1 MONO# on admission, ×10 9 /L, median (IQR) 1.23(0.91,1.73) 1.22(0.91,1.62) 1.33(0.80,1.92) 0.409 3 MONO# at discharge, ×10 9 /L, median (IQR) 0.58(0.40,0.76) 0.55(0.40,0.73) 0.67(0.49,0.93) 0.157 1 MONO% on admission, %, median (IQR) 9.00(7.00,11.00) 9.00(7.00,11.00) 10.00(7.00,12.00) 0.811 3 MONO% at discharge, %, median (IQR) 6.00(5.00,8.00) 6.00(5.00,8.00) 8.00(6.00,9.00) 0.044 1 HCT on admission, %, mean (SD) 32.15(4.68) 32.62(4.41) 29.65(5.39) 0.023 3 HCT at discharge, %, mean (SD) 33.14(4.58) 33.63(4.43) 30.64(4.64) 0.02 1 HGB on admission, g/L, mean (SD) 105.29(15.80) 106.94(15.31) 96.60(16.00) 0.019 3 HGB at discharge, g/L, mean (SD) 105.41(15.40) 107.53(14.85) 94.53(13.90) 0.002 2 MCH on admission, Pg, median (IQR) 26.40(24.75,27.75) 26.50(25.05,27.73) 25.00(23.20,28.60) 0.4 3 MCH at discharge, Pg, median (IQR) 25.90(23.93,27.28) 26.10(24.35,27.45) 24.80(22.60,25.90) 0.065 1 MCHC on admission, g/L, mean (SD) 327.19(11.41) 327.65(10.98) 324.80(13.66) 0.379 3 MCHC at discharge, g/L, mean (SD) 317.10(12.67) 318.69(11.86) 308.93(13.94) 0.006 1 MCV on admission, fL, median (IQR) 80.35(75.48,83.65) 81.00(76.60,83.50) 78.70(71.80,89.00) 0.445 3 MCV at discharge, fL, median (IQR) 81.00(77.70,84.48) 81.20(77.95,84.55) 78.30(77.00,83.00) 0.281 4 MPV on admission, fL, mean (SD) 9.53(0.85) 9.48(0.80) 9.81(1.07) 0.18 6 MPV at discharge, fL, mean (SD) 9.35(0.71) 9.34(0.70) 9.40(0.84) 0.78 1 PLT on admission, ×10 9 /L, mean (SD) 550.06(205.98) 535.24(199.37) 628.13(229.38) 0.11 3 PLT at discharge, ×10 9 /L, mean (SD) 527.58(181.75) 526.65(190.00) 532.33(136.75) 0.912 1 RBC on admission, ×10 12 /L, mean (SD) 4.07(0.66) 4.13(0.64) 3.75(0.70) 0.041 3 RBC at discharge, ×10 12 /L, mean (SD) 4.15(0.62) 4.21(0.61) 3.84(0.60) 0.032 3 A/G on admission, mean (SD) 1.31(0.30) 1.34(0.28) 1.13(0.31) 0.01 74 A/G at discharge, mean (SD) 1.45(0.35) 1.49(0.35) 1.31(0.37) 0.392 3 ALB on admission, g/L, mean (SD) 37.13(4.78) 37.79(4.48) 33.75(4.97) 0.002 74 ALB at discharge, g/L, mean (SD) 38.09(5.11) 38.57(5.40) 36.05(3.43) 0.389 3 GLO on admission, g/L, median (IQR) 29.25(25.20,33.68) 28.70(25.15,33.10) 32.30(28.10,35.30) 0.166 74 GLO at discharge, g/L, mean (SD) 27.42(6.26) 27.16(6.58) 28.53(5.29) 0.705 3 ALT on admission, U/L, median (IQR) 22.00(14.00,31.50) 21.00(13.50,38.00) 24.00(18.00,112.00) 0.081 74 ALT at discharge, U/L, median (IQR) 20.00(14.00,31.50) 20.00(14.00,25.50) 27.00(15.25,59.75) 0.56 3 AST on admission, U/L, median (IQR) 33.50(26.25,50.00) 34.00(26.00,48.00) 33.00(27.00,131.00) 0.45 74 AST at discharge, U/L, median (IQR) 35.00(27.00,42.00) 35.00(27.00,41.00) 37.50(28.25,72.25) 0.53 3 ALP on admission, U/L, median (IQR) 170.50(141.75,222.00) 170.00(140.00,221.00) 185.00(153.00,232.00) 0.286 74 ALP at discharge, U/L, mean (SD) 223.71(88.91) 227.71(97.14) 206.75(43.62) 0.683 3 UA on admission, µmol/L, median (IQR) 185.00(152.00,244.25) 185.00(151.00,246.00) 173.00(152.00,223.00) 0.326 74 UA at discharge, µmol/L, mean (SD) 226.14(66.24) 221.12(64.04) 247.50(81.41) 0.488 a: The number of missing cases in the variables. b: The P-value was obtained from univariate logistic regression analysis of the data with missing items after multiple imputation. WBC, White Blood Cell; CRP, C-Reactive Protein; ESR, Erythrocyte Sedimentation Rate; PCT, Procalcitonin; SAA, Serum Amyloid A; EO, Eosinophil; NEUT, Neutrophil; LYMPH, Lymphocyte; MONO, Monocyte; HCT, Hematocrit; HGB, Hemoglobin; MCH, Mean Corpuscular Hemoglobin; MCHC, Mean Corpuscular Hemoglobin Concentration; MCV, Mean Corpuscular Volume; MPV, Mean Platelet Volume; PLT, Platelet Count; RBC, Red Blood Cell Count; A/G, Albumin/Globulin ratio; ALB, Albumin; GLO, Globulin; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; ALP, Alkaline Phosphatase; UA, Uric Acid. #, absolute count; %, percentage. The median age at onset was 0.83 years. Prior to admission, 54 patients (56.8%) had received antibiotic treatment. On admission, 30 patients (31.6%) presented with fever; 27 patients (28.4%) remained febrile 48 hours after initiation of empirical intravenous antibiotics, and 12 patients (12.6%) had a CRP level ≥ 100 mg/dL within 2–4 days of treatment. The median symptom duration was 7 days, and the median total hospital stay was 15 days. Empirical antibiotic therapy was generally initiated on the day of admission, with definitive antibiotic treatment starting at a median of one day post-admission (range, 11 to 20.75 days). The median duration of antibiotic therapy was 15 days, and 45 patients (54.2%) required a switch in antibiotics during hospitalization. Microbiological cultures were performed for all patients. Blood culture yielded positive results in 19 of 94 cases (20.2%), while cultures of pus, synovial fluid, or tissue were positive in 42 of 76 cases (55.3%). Overall, 47 patients (50%) had positive microbiological cultures. Staphylococcus aureus was identified in 26 cases, including 15 strains (31.9%) of methicillin-sensitive Staphylococcus aureus and 7 strains (14.9%) of methicillin-resistant Staphylococcus aureus. There were also 6 cases (12.%) of Staphylococcus hominis, 9 cases (19.1%) of Salmonella [4 cases (8.5%) of S. Typhimurium, 2 cases (4.3%) of S. enteritidis, 1 case (2.1%) of S. Riphium, 1 case (2.1%) of S. Potsdam, 1 case (2.1%) of S. Chester], 2 cases (4.3%) of Streptococcus pneumoniae, 2 cases (4.3%) of Streptococcus Agalactiae (group B), 1 case (2.1%) of Acinetobacter baumannii, 1 case (2.1%) of Bacillus Cereus, 1 case (2.1%) of Proteus species. Osseous abscesses were detected in 16 patients (16.8%), 2 of whom (2.1%) had concurrent disseminated infection. Within 72 hours of admission, 79 patients (83.2%) received active intervention, including 21 patients (22.1%) who underwent bone debridement. Delayed intervention (> 72 hours) was performed in 26 patients (31%), and 10 patients (10.5%) required multiple surgical procedures during hospitalization. Chronic Complications Of the 95 patients, 16 (16.8%) experienced chronic complications. These included 8 cases of avascular necrosis, 6 cases of growth arrest, 4 cases of chronic dislocation, 2 cases of joint deformity, 3 cases of joint ankylosis, and 1 case of pathological fracture. Univariate Logistic Regression Analysis Several factors were identified to be associated with complications of chronic osteoarticular infections (OAIs), with statistical significance (p < 0.05) (Table 1 ). These factors included white blood cell count (WBC), serum amyloid A (SAA), hematocrit (HCT), hemoglobin (HGB), mean platelet volume (MPV), alanine transaminase (ALT), and aspartate transaminase (AST) on admission; SAA, HCT, and HGB at discharge; as well as bacteremia, detection of Staphylococcus aureus, bone abscess, delayed source control, bone debridement, isolated arthritis, and arthritis combined dislocation or subluxation. Multivariate Regression Analysis and Prediction Model Establishment Seventeen variables with statistical significance in the univariate analysis of ASA were included in the multivariate analysis. Backward stepwise Logistic regression was employed, and the results revealed that WBC on admission, ALT on admission, SAA at discharge, and arthritis combined dislocation or subluxation were independent predictors of chronic complications in ASA patients (p < 0.05) (Table 3 ). The resulting regression model was expressed as: Log(P) = -8.459 + 0.153×WBC on admission + 0.014×ALT on admission + 0.142×SAA at discharge + 3.337×arthritis combined with dislocation or subluxation. Based on this logistic regression equation, we developed a nomogram for predicting chronic complications (Fig. 2 ). Table 3 Binary logistic regression analysis backwards stepwise (conditional) for chronic complications following ASA. Variables B Standard Error χ2 P Odds Ratio OR Lower 95% OR Upper 95% WBC on admission 0.153 0.059 6.765 0.009 1.165 1.038 1.308 SAA at discharge 0.142 0.058 6.007 0.014 1.153 1.029 1.292 ALT on admission 0.014 0.005 7.442 0.006 1.014 1.004 1.025 ASA combined dislocation or subluxation 3.337 1.036 10.368 0.001 28.134 3.691 214.431 Intercept -8.459 2.115 15.992 < 0.001 < 0.001 < 0.001 0.013 Table 5 Comparative ROC curve analysis for chronic complication in pediatric patients with ASA. Variables AUC Cutoff value Sensitivity (%) Specificity (%) PPV (%) NPV (%) Accuracy (%) Nomogram 0.882(0.786–0.979) 0.427 66.7 96.1 76.9 93.7 91.3 Gouveia score 0.551(0.395–0.708) 0.041 43.8 81 31.8 87.7 74.7 Validation of the Prognostic Nomogram Internal validation of the nomogram for predicting chronic complications in pediatric ASA patients was performed using bootstrap resampling with 1000 iterations. The area under the curve (AUC) was 0.882 (95% confidence interval [CI]: 0.786–0.979), indicating high predictive accuracy (Fig. 3 ). The optimal cut-off value was determined to be 0.427, at which the sum of sensitivity and specificity reached the maximum: sensitivity was 66.7%, specificity was 96.1%, positive predictive value (PPV) was 76.9%, negative predictive value (NPV) was 93.7%, and overall accuracy was 91.3%. These results demonstrated good discriminative ability of the model (Table 4). The Hosmer-Lemeshow test yielded a p-value of 0.447, suggesting no significant deviation from perfect fit and confirming the model's satisfactory predictive accuracy. The calibration curve, which compared predicted outcomes with actual results, further verified the reliability of the model in the validation set (Fig. 4 ). Decision curve analysis (DCA) demonstrated the clinical utility of the model by showing its net benefit across the threshold probability range for predicting chronic complications in ASA patients (Fig. 5 ). A comparative study of the prediction model for chronic complications in pediatric ASA patients was conducted using the Gouveia et al. scoring system [9] (Table 4). When the Gouveia et al. scoring system was applied to predict the risk of chronic complications in the same patient cohort, the AUC was 0.551 (95% CI: 0.395–0.708). At the cut-off value of 0.041, the system achieved a sensitivity of 43.8%, specificity of 81%, PPV of 31.8%, NPV of 87.7%, and overall accuracy of 74.7%. Discussion This population-based study revealed that the following factors may be associated with chronic complications in the prognosis of pediatric ASA: WBC, SAA, HCT, HGB, MPV, ALT and AST on admission; SAA, HCT, HGB at discharge, bacteremia, staphylococcus aureus detection, bone abscess, delayed source control, bone debridement, isolated arthritis and arthritis combined dislocation or subluxation. Further multivariate analysis revealed that WBC on admission, ALT on admission, SAA at discharge, and arthritis combined with dislocation or subluxation were independent risk factors for chronic complications in these patients.​ Elevated WBC count is a hallmark of suppurative arthritis, reflecting bacterial load and inflammatory intensity [10,11]. Persistent leukocyte infiltration activates the complement system via enzymatic hydrolysis, cell death, and cytokine release; complement then amplifies inflammation through leukocyte chemotaxis and activation [12–14], ultimately leading to inflammatory escalation and tissue damage. During infection, the liver participates in the acute-phase response by synthesizing acute-phase proteins (e.g., SAA, CRP) to regulate systemic inflammation [15]. Concurrently, hepatocytes may be injured by inflammatory mediators (e.g., tumor necrosis factor-α, interleukin-6) [16–18], forming an "inflammation-hepatic damage" vicious cycle that causes ALT release into the bloodstream. Elevated admission ALT may thus reflect both the magnitude of the host inflammatory response and liver involvement in inflammation. Notably, direct studies exploring the association between admission ALT and ASA prognostic risk remain limited, warranting confirmation in future prospective investigations.​ Serum SAA levels rise precipitously following infection: during the acute-phase response to tissue injury, approximately 2.5-3% of hepatic protein synthesis is dedicated to SAA production, enabling plasma concentrations to increase from 1–5 µg/mL to 1000 µg/mL within 24 hours [19,20]. This elevation persists for 2–3 days (circulating SAA half-life ≈ 90 minutes) and normalizes within 7–10 days if tissue injury resolves [21,22]. A higher SAA level at discharge may therefore indicate unresolved inflammation and ongoing tissue damage.​ Previous studies have shown that acetabular dysplasia often persists during follow-up in children with ASA complicated by dislocation or subluxation, suggesting severe joint damage [23]. For instance, dislocation of the hip joint, which is a ball-and-socket joint characterized by an extensive range of motion and a thick articular capsule, can readily compress periarticular blood vessels, thereby leading to femoral head avascular necrosis [24,25].​ Calculating chronic complication risk scores during hospitalization aids in determining optimal management strategies. Over 90% of patients who develop chronic complications do so within one year of admission, supporting the rationale for one-year follow-up in high-risk patients [3,26,27]. When elevated risk is predicted, orthopedic surgeons should recommend extended outpatient follow-up to closely monitor complication development and provide families with guidance on prevention strategies.​ In our ASA cohort, arthritis with dislocation emerged as a strong independent risk factor (B = 3.337, χ²=10.368, P = 0.001), with an odds ratio (OR) of 28.134 (95% CI: 3.691-214.431), indicating a 28.134-fold higher risk of chronic complications compared to patients without dislocation. Three laboratory markers also showed significant positive associations: each 1-unit increase in admission WBC count was associated with a 16.5% increased risk (OR = 1.165, 95% CI: 1.038–1.308, P = 0.009); each 1-unit increase in admission ALT with a 1.4% increased risk (OR = 1.014, 95% CI: 1.004–1.025, P = 0.006); and each 1-unit increase in discharge SAA with a 15.3% increased risk (OR = 1.153, 95% CI: 1.029–1.292, P = 0.014). These variables demonstrated good discriminative ability for predicting 1-year chronic complications.​ Given the accessibility of these risk variables, we compared our nomogram to the Gouveia scoring system. While the Gouveia system [9] has demonstrated satisfactory predictive performance within the local cohorts, our findings indicate that the nomogram exhibits superior discriminatory ability, particularly among Chinese patients. The nomogram's AUC of 0.882 (95% CI: 0.786-0.979) significantly surpasses the moderate prediction level threshold of 0.7, indicating its strong capacity to predict the target event. In contrast, the Gouveia system had an AUC of only 0.551 (95% CI: 0.395-0.708), approaching random prediction and lacking reliability. At a cut-off value of 0.427, the nomogram achieved 66.7% sensitivity, 96.1% specificity, 76.9% PPV, 93.7% NPV, and 91.3% accuracy—demonstrating a "high specificity priority" clinical advantage. Conversely, the Gouveia system had only 43.8% sensitivity and 31.8% PPV, with high risks of missed and incorrect diagnoses and 74.7% accuracy. The nomograms’ strong discriminative ability (AUC>0.8), balanced sensitivity, and high specificity make it more robust than traditional scoring systems, supporting its potential clinical utility for identifying 1-year chronic complications in pediatric ASA. We encountered several limitations when analyzing our results. First, the data came from a single-center retrospective analysis of Chinese patients with ASA, which may introduce information bias and limit generalizability. Second, the sample size was small, and there was a lack of external datasets for validation. Future research should use multi-center, large-sample clinical data to refine this prediction model. Conclusions In conclusion, the current prediction model shows promise in predicting the risk of chronic complications in children with ASA. It could guide clinical decisions, but more research is needed. Abbreviations ASA Acute suppurative arthritis CRP C-reactive protein MRI Magnetic resonance imaging ROC Receiver operating characteristic OAIs Osteoarticular Infections WBC White blood cell SAA Serum amyloid A HCT Hematocrit HGB Hemoglobin MPV Mean platelet volume ALT Alanine transaminase AST Aspartate transaminase AUC Area under the curve PPV Positive predictive value NPV Negative predictive value DCA Decision curve analysis Declarations Ethics approval and consent for participate All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This was a retrospective study, and IRB approval was obtained (approval No. [2025] 242A01). Consent for publication Not applicable. Availability of data and materials The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding Guangzhou Science and Technology Basic and Applied Basic Research Program (202201011108). National Natural Science Foundation of China (82402837). Guangzhou Medical University 2024 Research Capability Enhancement Plan Project. Doctoral Research Initiation Project of Guangzhou Women and Children Medical Center (No. 2023BS015). Authors' contributions YZ, XZ, FC and HX: study design YZ, XZ, FC, FX: data analysis YZ, FC: manuscript preparation FC, FX, HX: critical review of the manuscript HX, FC: supervision All authors: data collection and approval of the final version to be published Acknowledgements The authors gratefully acknowledge all individuals who participated in this study. References Erkilinc M, Gilmore A, Weber M, Mistovich RJ. Current Concepts in Pediatric Septic Arthritis. The Journal of the American Academy of Orthopaedic Surgeons 2021, 29, 196–206. https://doi.org/10.5435/JAAOS-D-20-00835. Jeyanthi JC, Yi KM, Allen JC Jr, Gera SK, Mahadev A. Epidemiology and Outcome of Septic Arthritis in Childhood: A 16-Year Experience and Review of Literature. Singapore medical journal 2022, 63, 256–262. https://doi.org/10.11622/smedj.2020140. Howard-Jones AR, Isaacs D, Gibbons PJ. Twelve-Month Outcome Following Septic Arthritis in Children. 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Clinical Practice Guideline by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America: 2021 Guideline on Diagnosis and Management of Acute Hematogenous Osteomyelitis in Pediatrics. Journal of the Pediatric Infectious Diseases Society 2021, 10, 801–844. https://doi.org/10.1093/jpids/piab027. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7797237","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533116208,"identity":"cc13a906-acaf-4762-95b3-e22b815b3607","order_by":0,"name":"Yang Zhang","email":"","orcid":"","institution":"Department of pediatric orthopaedics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University，Guangdong Provincial Clinical Research Center for Child 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13:56:35","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7225,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/fec3a0c30e929f0c73023328.png"},{"id":94398062,"identity":"b6f862af-d993-46a7-bb5d-f3f4de06ae74","added_by":"auto","created_at":"2025-10-27 13:56:57","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7814,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/edbb0aa6cf1d9cd605ea699a.png"},{"id":94398273,"identity":"a48ac09a-9130-48b8-b050-06bbaa28688c","added_by":"auto","created_at":"2025-10-27 13:57:02","extension":"xml","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114838,"visible":true,"origin":"","legend":"","description":"","filename":"f2e83d17a8df4df496de57d3c7f536141structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/89e0776dbfeb68bcab1b51f0.xml"},{"id":94397797,"identity":"e6255bdf-14c8-413b-8310-f5c306874223","added_by":"auto","created_at":"2025-10-27 13:56:48","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118919,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/4c6a47bbc06bb5100b2e1501.html"},{"id":94398869,"identity":"38f646a7-4b66-4f99-9c77-63423dc36bab","added_by":"auto","created_at":"2025-10-27 13:57:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2847086,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient inclusion.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/ff7bcba3c7417179a7983e42.png"},{"id":94399001,"identity":"a8172284-def3-40f1-a13b-38f9697d84e8","added_by":"auto","created_at":"2025-10-27 13:57:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250881,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomograms for predicting the risk of chronic complications in pediatric patients with ASA.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/95dadd2a2297a8b1d954cad1.png"},{"id":94397841,"identity":"87c375bf-f9d4-4d13-8477-b205c4ab8e2c","added_by":"auto","created_at":"2025-10-27 13:56:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89946,"visible":true,"origin":"","legend":"\u003cp\u003eROC for the risk of chronic complications in ASA patients. ROC, receiver operating characteristic; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/435b75470d9dd2736e4bc3a1.png"},{"id":94396812,"identity":"fbac7a0f-9427-4f85-a17f-d85bb0a007c3","added_by":"auto","created_at":"2025-10-27 13:56:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145204,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the nomogram prediction. The x-axis represents the nomogram prediction, and the y-axis represents the actual situation. The diagonal line indicates an exact match between the forecast and the actual situation. The closer the solid line is to the diagonal line, the more accurate the prediction.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/c76fc01ce027a6cba3bd40ee.png"},{"id":94397831,"identity":"27fe917c-456f-44f0-9638-51b81bea3f63","added_by":"auto","created_at":"2025-10-27 13:56:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131481,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for the predictive model. Net benefit was produced against the high-risk threshold. The red line represents the predictive model. Using this predictive model would result in a greater net benefit than the treat-all or treat-none strategies.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/2c28c9ad140cca25971be10a.png"},{"id":100796080,"identity":"2e363212-b825-4c8d-b82d-8c385a208c95","added_by":"auto","created_at":"2026-01-21 13:38:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5059820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7797237/v1/17f3cded-1b80-4985-b910-94e0c86f88b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing a nomogram for predicting chronic complications in pediatric acute suppurative arthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute suppurative arthritis (ASA) is an infection of the synovial joints, predominantly affecting young children. In developed countries, the incidence of pediatric ASA ranges from 1\u0026ndash;5 per 100,000 children [1]. Notably, ASA is a potentially destructive condition, with a high rate of severe and long-term complications in growing children [2]. Chronic complications associated with ASA include growth arrest, pathological fractures, avascular necrosis, joint stiffness, chronic dislocation, and joint deformities. Furthermore, infection recurrence may occur in cases of inadequate treatment or poor treatment compliance [3]. However, the variable clinical courses observed in pediatric patients have hindered the development of standardized management guidelines. Furthermore, in Asia, osteoarticular pathogens like Staphylococcus aureus show different characteristics compared to similar pathogens in the United States and Europe. This highlights the need for custom prediction models [4, 5].\u003c/p\u003e\u003cp\u003eMost existing studies on ASA have primarily relied on retrospective analyses to identify factors associated with chronic complications and disease prognosis [2,3,6\u0026ndash;8]. In 2023, Gouveia et al. became the first to successfully develop and validate a risk prediction model for acute and chronic complications in children with osteoarticular infections [9].\u003c/p\u003e\u003cp\u003eThe primary objective of our study was to develop a nomogram to predict chronic complications in pediatric patients with ASA. The secondary aim was to compare the newly developed nomogram with the Gouveia system [9] previously reported.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a retrospective review of the medical records of children between one month and 18 years of age who were hospitalized at our institution between January 2016 and December 2023 with ASA and symptoms lasting no longer than 14 days before admission. The Ethics Committee of our Institution approved this study (No. [2025]242A01).\u003c/p\u003e\u003cp\u003eThe inclusion criteria for this study were: (1) patients aged 1 month to 18 years; (2) patients with confirmed diagnosis of ASA; (3) patients whose symptoms began within 14 days before admission; (4) patients with at least one year of follow-up after the first onset of symptoms.\u003c/p\u003e\u003cp\u003eThe exclusion criteria were: (1) patients with pre-existing disease, such as blood disease, category of impediment disease, or cardiovascular disorder; neoplasm malignant; or other chronic disease; (2) patients diagnosed with juvenile arthritis, synovitis, pulmonary tuberculosis, immunological disease, or arthritis reactive after admission; (3) patients with osteomyelitis of flat bones, irregular bones, or small bones (e.g., vertebral column, jawbone, clavicle, or metacarpal bone), septic arthritis of small joints, or secondary infection after open fracture or surgery; (4) exclusion criteria also included patients with follow-up duration less than 1 year.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDiagnostic criteria\u003c/h2\u003e\u003cp\u003eThe direct and indirect diagnostic criteria for Acute suppurative arthritis are as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e- Direct diagnosis is established if pathogenic microorganisms are detected in blood, pus, or tissue cultures.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e- Indirect diagnosis can be made when cultures are negative but symptoms such as dysphoria, local swelling, pain, limited range of mobility, elevated C-reactive protein (CRP) levels, imaging signs, or positive Magnetic resonance imaging (MRI) are present. Body fluid or tissue cultures can be obtained through joint incision and drainage, Bone abscess drainage, or Biopsy bone. Cultures from joints or soft tissues near the site of bone infection can also be used. For disseminated infections, cultures from various sources such as mediastinal fluid or pleural fluid are used.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChronic complications and scoring\u003c/h3\u003e\n\u003cp\u003eChronic complications include Growth arrest, Pathological fracture, Ischemic necrosis, joint ankylosis, limited range of joint mobility, Chronic dislocation, Joint deformity and Osteomyelitis chronic. Osteomyelitis chronic is defined as persistent or recurrent symptoms and signs related to epiphyseal, metaphyseal or skeletal changes visible on plain X-ray. It requires at least 12 weeks of Antibiotic therapy.\u003c/p\u003e\u003cp\u003eIn the Gouveia scoring system, the final model for estimating the probability of sequelae at 6 months is πSeq-6\u0026thinsp;=\u0026thinsp;OddsSeq-6/1.01\u0026thinsp;+\u0026thinsp;OddsSeq-6, where OddsSeq-6\u0026thinsp;=\u0026thinsp;exp [-4.75\u0026thinsp;+\u0026thinsp;1.41\u0026middot; (age\u0026thinsp;\u0026ge;\u0026thinsp;4 years)\u0026thinsp;+\u0026thinsp;1.52\u0026middot; (CRP\u0026thinsp;\u0026ge;\u0026thinsp;110 mg)\u0026thinsp;+\u0026thinsp;2.22\u0026middot;disseminated disease\u0026thinsp;+\u0026thinsp;1.70\u0026middot;Bone abscess] [9].\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData analyses were performed using SPSS version 27.0 (International Business Machines Corporation, Armonk, NY, USA). Normally distributed quantitative data were presented as mean (standard deviation), and non-normally distributed quantitative data as median (P25, P75). Categorical data were described by frequencies and proportions. For between-group comparisons, independent samples t-tests were used for normally distributed quantitative data, nonparametric tests for non-normally distributed data, and chi-square tests for categorical data. Missing values were handled via multiple imputation. Univariate analyses were conducted for all variables; statistically significant factors were included in a stepwise backward multiple logistic regression model. Using R software version 4.2.2 (R Core Team, Auckland, New Zealand), a nomogram for predicting childhood ASA chronic complications was developed based on identified independent predictors. The model was optimized by 1000 bootstrap iterations, and its predictive performance was internally validated via receiver operating characteristic (ROC) curves. The Hosmer-Lemeshow test evaluated the goodness-of-fit between predicted and observed outcomes, and calibration curves assessed calibration. Decision curve analysis quantified the nomogram's net benefit across threshold probabilities to validate its utility. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The Gouveia scoring system was applied to predict cohort data risks, enabling direct performance comparison with the present study's nomogram.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study enrolled 95 patients with ASA, including 51 males (53.7%) and 44 females (46.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the demographic and clinical details (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these cases, 71 (74.7%) were diagnosed with isolated septic arthritis, 16 (16.8%) with septic arthritis complicated by osteomyelitis, 7 (7.4%) with arthritis accompanied by dislocation or subluxation, and 1 (1.1%) with multifocal infection. Regarding laterality, ASA affected the left side in 54 patients (56.8%) and the right side in 41 patients (43.2%). The most commonly involved joints were the hip (n\u0026thinsp;=\u0026thinsp;34, 35.8%) and knee (n\u0026thinsp;=\u0026thinsp;31, 32.6%).\u003c/p\u003e\u003cp\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\u003eRelationship between predictive and outcome variables and chronic complications in children with ASA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing Data\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal cases (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon - chronic complication group (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChronic complication group (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83(0.42,2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92(0.50,3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67(0.10,0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;4 years, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\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\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51(53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43(54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44(46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36(45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemperature on Admission, ℃, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.80(36.50,37.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.80(36.50,37.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.75(36.50,37.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSymptom duration at admission, days, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00(5.00,10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.00(5.00,10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00(4.25,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of hospital stay, days, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00(11.00,21.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00(11.00,19.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.00(12.00,22.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntibiotic treatment before Admission, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54(56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44(55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaterality, Left, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54(56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45(57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(56.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSite involved, n (%)\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHip joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKnee joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnkle joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10(12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElbow joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShoulder joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12(12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9(11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWrist joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall joint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClassification of ASA, n (%)\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIsolated ASA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71(74.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63(79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined AHO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13(16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined Dislocation or Subluxation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(31.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombined Multifocal infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisseminated infection, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime to initiate antibiotic treatment after admission, days, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00(1.00,1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00(1.00,1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;110 mg/L on Admission, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDuration of antibiotic treatment, days, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00(11.00,20.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.50(10.75,18.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.50(11.75,23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L after 2\u0026ndash;4 d of antibiotics, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69(72.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55(69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFever\u0026thinsp;\u0026gt;\u0026thinsp;48 h of antibiotics, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(26.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChange of antibiotics during intravenous drip, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45(54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35(50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBone abscess, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9(11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBacteremia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35(44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS. aureus detected, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18(23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMRSA detected, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBone debridement, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7(43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource control, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79(83.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63(79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16(100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDelayed source control, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25(35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiple surgeries during the treatment course, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7(8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOral medications prescribed for taking after discharge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73(76.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61(77.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003ea: The number of missing cases in the variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eb: The P-value was obtained from univariate logistic regression analysis of the data with missing items after multiple imputation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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\u003eRelationship between chronic complications in children with ASA and predictive and outcome variables (laboratory serum biomarkers).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing Data\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal cases (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon - chronic complication group (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChronic complication group (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWBC on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.45(10.80,18.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.00(10.80,18.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.50(14.00,19.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWBC at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.90(6.70,10.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.80(6.65,10.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.40(7.40,10.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRP on admission, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.88(16.85,104.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.06(17.00,119.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.70(15.45,77.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRP at discharge, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.90(0.66,10.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.86(0.52,7.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.50(1.64,20.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESR on admission, mm/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47.65(29.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.14(31.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.90(18.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESR at discharge, mm/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.59(18.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.59(18.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.58(17.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCT on admission, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17(0.10,0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.16(0.10,0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18(0.12,0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCT at discharge, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10(0.10,0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10(0.10,0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29(0.05,0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAA on admission, mg/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163.29(103.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e178.66(99.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e101.80(100.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSAA at discharge, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.01(6.00,35.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.64(6.00,13.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40.16(36.98,55.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEO# on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11(0.01,0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09(0.01,0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16(0.01,0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEO# at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27(0.16,0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27(0.17,0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19(0.15,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEO% on admission, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00(0.00,2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.00,2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00(0.00,2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEO% at discharge, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00(2.00,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00(2.00,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.00(1.00,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNEUT# on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.07(4.23,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.76(4.21,11.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.77(4.92,10.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNEUT# at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74(1.80,3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.72(1.78,4.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.75(2.29,3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNEUT% on admission, %, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.31(18.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.77(18.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.87(18.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNEUT% at discharge, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74(1.80,3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.00(22.00,43.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.00(23.00,37.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYMPH# on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.74(2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.54(2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.83(2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYMPH# at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.24(2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.22(2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.38(2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYMPH% on admission, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.50(26.00,52.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.00(25.00,52.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40.00(30.00,52.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLYMPH% at discharge, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.50(49.00,66.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.00(47.00,66.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.00(52.00,69.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMONO# on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23(0.91,1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22(0.91,1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.33(0.80,1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMONO# at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58(0.40,0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55(0.40,0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67(0.49,0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMONO% on admission, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.00(7.00,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.00(7.00,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.00(7.00,12.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMONO% at discharge, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00(5.00,8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.00(5.00,8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.00(6.00,9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHCT on admission, %, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.15(4.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.62(4.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.65(5.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHCT at discharge, %, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.14(4.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.63(4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.64(4.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHGB on admission, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105.29(15.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106.94(15.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96.60(16.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHGB at discharge, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105.41(15.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107.53(14.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e94.53(13.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCH on admission, Pg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.40(24.75,27.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.50(25.05,27.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.00(23.20,28.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCH at discharge, Pg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.90(23.93,27.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.10(24.35,27.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.80(22.60,25.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCHC on admission, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e327.19(11.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e327.65(10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e324.80(13.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.379\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCHC at discharge, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e317.10(12.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e318.69(11.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e308.93(13.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCV on admission, fL, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.35(75.48,83.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.00(76.60,83.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.70(71.80,89.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCV at discharge, fL, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.00(77.70,84.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.20(77.95,84.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.30(77.00,83.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMPV on admission, fL, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.53(0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.48(0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.81(1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMPV at discharge, fL, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.35(0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.34(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.40(0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLT on admission, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e550.06(205.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e535.24(199.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e628.13(229.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLT at discharge, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e527.58(181.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e526.65(190.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e532.33(136.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRBC on admission, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.07(0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.13(0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.75(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRBC at discharge, \u0026times;10\u003csup\u003e12\u003c/sup\u003e/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.15(0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.21(0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.84(0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/G on admission, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.31(0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34(0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13(0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/G at discharge, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.45(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.31(0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALB on admission, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.13(4.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.79(4.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.75(4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALB at discharge, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.09(5.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.57(5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.05(3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLO on admission, g/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.25(25.20,33.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.70(25.15,33.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.30(28.10,35.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLO at discharge, g/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.42(6.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.16(6.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.53(5.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT on admission, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.00(14.00,31.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.00(13.50,38.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.00(18.00,112.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT at discharge, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.00(14.00,31.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.00(14.00,25.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.00(15.25,59.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAST on admission, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.50(26.25,50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.00(26.00,48.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.00(27.00,131.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAST at discharge, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.00(27.00,42.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.00(27.00,41.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.50(28.25,72.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALP on admission, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170.50(141.75,222.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e170.00(140.00,221.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e185.00(153.00,232.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALP at discharge, U/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223.71(88.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e227.71(97.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e206.75(43.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.683\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA on admission, \u0026micro;mol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185.00(152.00,244.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e185.00(151.00,246.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173.00(152.00,223.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUA at discharge, \u0026micro;mol/L, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e226.14(66.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e221.12(64.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e247.50(81.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003ea: The number of missing cases in the variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eb: The P-value was obtained from univariate logistic regression analysis of the data with missing items after multiple imputation.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eWBC, White Blood Cell; CRP, C-Reactive Protein; ESR, Erythrocyte Sedimentation Rate; PCT, Procalcitonin; SAA, Serum Amyloid A; EO, Eosinophil; NEUT, Neutrophil; LYMPH, Lymphocyte; MONO, Monocyte; HCT, Hematocrit; HGB, Hemoglobin; MCH, Mean Corpuscular Hemoglobin; MCHC, Mean Corpuscular Hemoglobin Concentration; MCV, Mean Corpuscular Volume; MPV, Mean Platelet Volume; PLT, Platelet Count; RBC, Red Blood Cell Count; A/G, Albumin/Globulin ratio; ALB, Albumin; GLO, Globulin; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; ALP, Alkaline Phosphatase; UA, Uric Acid.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e#, absolute count; %, percentage.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe median age at onset was 0.83 years. Prior to admission, 54 patients (56.8%) had received antibiotic treatment. On admission, 30 patients (31.6%) presented with fever; 27 patients (28.4%) remained febrile 48 hours after initiation of empirical intravenous antibiotics, and 12 patients (12.6%) had a CRP level\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL within 2\u0026ndash;4 days of treatment. The median symptom duration was 7 days, and the median total hospital stay was 15 days.\u003c/p\u003e\u003cp\u003eEmpirical antibiotic therapy was generally initiated on the day of admission, with definitive antibiotic treatment starting at a median of one day post-admission (range, 11 to 20.75 days). The median duration of antibiotic therapy was 15 days, and 45 patients (54.2%) required a switch in antibiotics during hospitalization.\u003c/p\u003e\u003cp\u003eMicrobiological cultures were performed for all patients. Blood culture yielded positive results in 19 of 94 cases (20.2%), while cultures of pus, synovial fluid, or tissue were positive in 42 of 76 cases (55.3%). Overall, 47 patients (50%) had positive microbiological cultures. Staphylococcus aureus was identified in 26 cases, including 15 strains (31.9%) of methicillin-sensitive Staphylococcus aureus and 7 strains (14.9%) of methicillin-resistant Staphylococcus aureus.\u003c/p\u003e\u003cp\u003eThere were also 6 cases (12.%) of Staphylococcus hominis, 9 cases (19.1%) of Salmonella [4 cases (8.5%) of S. Typhimurium, 2 cases (4.3%) of S. enteritidis, 1 case (2.1%) of S. Riphium, 1 case (2.1%) of S. Potsdam, 1 case (2.1%) of S. Chester], 2 cases (4.3%) of Streptococcus pneumoniae, 2 cases (4.3%) of Streptococcus Agalactiae (group B), 1 case (2.1%) of Acinetobacter baumannii, 1 case (2.1%) of Bacillus Cereus, 1 case (2.1%) of Proteus species. Osseous abscesses were detected in 16 patients (16.8%), 2 of whom (2.1%) had concurrent disseminated infection. Within 72 hours of admission, 79 patients (83.2%) received active intervention, including 21 patients (22.1%) who underwent bone debridement. Delayed intervention (\u0026gt;\u0026thinsp;72 hours) was performed in 26 patients (31%), and 10 patients (10.5%) required multiple surgical procedures during hospitalization.\u003c/p\u003e\n\u003ch3\u003eChronic Complications\u003c/h3\u003e\n\u003cp\u003eOf the 95 patients, 16 (16.8%) experienced chronic complications. These included 8 cases of avascular necrosis, 6 cases of growth arrest, 4 cases of chronic dislocation, 2 cases of joint deformity, 3 cases of joint ankylosis, and 1 case of pathological fracture.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate Logistic Regression Analysis\u003c/h2\u003e\u003cp\u003eSeveral factors were identified to be associated with complications of chronic osteoarticular infections (OAIs), with statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These factors included white blood cell count (WBC), serum amyloid A (SAA), hematocrit (HCT), hemoglobin (HGB), mean platelet volume (MPV), alanine transaminase (ALT), and aspartate transaminase (AST) on admission; SAA, HCT, and HGB at discharge; as well as bacteremia, detection of Staphylococcus aureus, bone abscess, delayed source control, bone debridement, isolated arthritis, and arthritis combined dislocation or subluxation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate Regression Analysis and Prediction Model Establishment\u003c/h3\u003e\n\u003cp\u003eSeventeen variables with statistical significance in the univariate analysis of ASA were included in the multivariate analysis. Backward stepwise Logistic regression was employed, and the results revealed that WBC on admission, ALT on admission, SAA at discharge, and arthritis combined dislocation or subluxation were independent predictors of chronic complications in ASA patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The resulting regression model was expressed as: Log(P) = -8.459\u0026thinsp;+\u0026thinsp;0.153\u0026times;WBC on admission\u0026thinsp;+\u0026thinsp;0.014\u0026times;ALT on admission\u0026thinsp;+\u0026thinsp;0.142\u0026times;SAA at discharge\u0026thinsp;+\u0026thinsp;3.337\u0026times;arthritis combined with dislocation or subluxation. Based on this logistic regression equation, we developed a nomogram for predicting chronic complications (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eBinary logistic regression analysis backwards stepwise (conditional) for chronic complications following ASA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\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\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR Lower 95%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR Upper 95%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC on admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAA at discharge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.007\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\u003e1.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT on admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.014\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\u003e7.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASA combined dislocation or subluxation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.368\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\u003e28.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e214.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-8.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.992\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\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.013\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\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative ROC curve analysis for chronic complication in pediatric patients with ASA.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\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\u003eCutoff value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNPV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAccuracy (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNomogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.882(0.786\u0026ndash;0.979)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e93.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGouveia score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.551(0.395\u0026ndash;0.708)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e87.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e74.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eValidation of the Prognostic Nomogram\u003c/h3\u003e\n\u003cp\u003eInternal validation of the nomogram for predicting chronic complications in pediatric ASA patients was performed using bootstrap resampling with 1000 iterations. The area under the curve (AUC) was 0.882 (95% confidence interval [CI]: 0.786\u0026ndash;0.979), indicating high predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The optimal cut-off value was determined to be 0.427, at which the sum of sensitivity and specificity reached the maximum: sensitivity was 66.7%, specificity was 96.1%, positive predictive value (PPV) was 76.9%, negative predictive value (NPV) was 93.7%, and overall accuracy was 91.3%. These results demonstrated good discriminative ability of the model (Table\u0026nbsp;4). The Hosmer-Lemeshow test yielded a p-value of 0.447, suggesting no significant deviation from perfect fit and confirming the model's satisfactory predictive accuracy. The calibration curve, which compared predicted outcomes with actual results, further verified the reliability of the model in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Decision curve analysis (DCA) demonstrated the clinical utility of the model by showing its net benefit across the threshold probability range for predicting chronic complications in ASA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA comparative study of the prediction model for chronic complications in pediatric ASA patients was conducted using the Gouveia et al. scoring system [9] (Table\u0026nbsp;4). When the Gouveia et al. scoring system was applied to predict the risk of chronic complications in the same patient cohort, the AUC was 0.551 (95% CI: 0.395\u0026ndash;0.708). At the cut-off value of 0.041, the system achieved a sensitivity of 43.8%, specificity of 81%, PPV of 31.8%, NPV of 87.7%, and overall accuracy of 74.7%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis population-based study revealed that the following factors may be associated with chronic complications in the prognosis of pediatric ASA: WBC, SAA, HCT, HGB, MPV, ALT and AST on admission; SAA, HCT, HGB at discharge, bacteremia, staphylococcus aureus detection, bone abscess, delayed source control, bone debridement, isolated arthritis and arthritis combined dislocation or subluxation. Further multivariate analysis revealed that WBC on admission, ALT on admission, SAA at discharge, and arthritis combined with dislocation or subluxation were independent risk factors for chronic complications in these patients.​\u003c/p\u003e\u003cp\u003eElevated WBC count is a hallmark of suppurative arthritis, reflecting bacterial load and inflammatory intensity [10,11]. Persistent leukocyte infiltration activates the complement system via enzymatic hydrolysis, cell death, and cytokine release; complement then amplifies inflammation through leukocyte chemotaxis and activation [12\u0026ndash;14], ultimately leading to inflammatory escalation and tissue damage.\u003c/p\u003e\u003cp\u003eDuring infection, the liver participates in the acute-phase response by synthesizing acute-phase proteins (e.g., SAA, CRP) to regulate systemic inflammation [15]. Concurrently, hepatocytes may be injured by inflammatory mediators (e.g., tumor necrosis factor-α, interleukin-6) [16\u0026ndash;18], forming an \"inflammation-hepatic damage\" vicious cycle that causes ALT release into the bloodstream. Elevated admission ALT may thus reflect both the magnitude of the host inflammatory response and liver involvement in inflammation. Notably, direct studies exploring the association between admission ALT and ASA prognostic risk remain limited, warranting confirmation in future prospective investigations.​\u003c/p\u003e\u003cp\u003eSerum SAA levels rise precipitously following infection: during the acute-phase response to tissue injury, approximately 2.5-3% of hepatic protein synthesis is dedicated to SAA production, enabling plasma concentrations to increase from 1\u0026ndash;5 \u0026micro;g/mL to 1000 \u0026micro;g/mL within 24 hours [19,20]. This elevation persists for 2\u0026ndash;3 days (circulating SAA half-life\u0026thinsp;\u0026asymp;\u0026thinsp;90 minutes) and normalizes within 7\u0026ndash;10 days if tissue injury resolves [21,22]. A higher SAA level at discharge may therefore indicate unresolved inflammation and ongoing tissue damage.​\u003c/p\u003e\u003cp\u003ePrevious studies have shown that acetabular dysplasia often persists during follow-up in children with ASA complicated by dislocation or subluxation, suggesting severe joint damage [23]. For instance, dislocation of the hip joint, which is a ball-and-socket joint characterized by an extensive range of motion and a thick articular capsule, can readily compress periarticular blood vessels, thereby leading to femoral head avascular necrosis [24,25].​\u003c/p\u003e\u003cp\u003eCalculating chronic complication risk scores during hospitalization aids in determining optimal management strategies. Over 90% of patients who develop chronic complications do so within one year of admission, supporting the rationale for one-year follow-up in high-risk patients [3,26,27]. When elevated risk is predicted, orthopedic surgeons should recommend extended outpatient follow-up to closely monitor complication development and provide families with guidance on prevention strategies.​\u003c/p\u003e\u003cp\u003eIn our ASA cohort, arthritis with dislocation emerged as a strong independent risk factor (B\u0026thinsp;=\u0026thinsp;3.337, χ\u0026sup2;=10.368, P\u0026thinsp;=\u0026thinsp;0.001), with an odds ratio (OR) of 28.134 (95% CI: 3.691-214.431), indicating a 28.134-fold higher risk of chronic complications compared to patients without dislocation. Three laboratory markers also showed significant positive associations: each 1-unit increase in admission WBC count was associated with a 16.5% increased risk (OR\u0026thinsp;=\u0026thinsp;1.165, 95% CI: 1.038\u0026ndash;1.308, P\u0026thinsp;=\u0026thinsp;0.009); each 1-unit increase in admission ALT with a 1.4% increased risk (OR\u0026thinsp;=\u0026thinsp;1.014, 95% CI: 1.004\u0026ndash;1.025, P\u0026thinsp;=\u0026thinsp;0.006); and each 1-unit increase in discharge SAA with a 15.3% increased risk (OR\u0026thinsp;=\u0026thinsp;1.153, 95% CI: 1.029\u0026ndash;1.292, P\u0026thinsp;=\u0026thinsp;0.014). These variables demonstrated good discriminative ability for predicting 1-year chronic complications.​\u003c/p\u003e\u003cp\u003eGiven the accessibility of these risk variables, we compared our nomogram to the Gouveia scoring system. While the Gouveia system [9] has demonstrated satisfactory predictive performance within the local cohorts, our findings indicate that the nomogram exhibits superior discriminatory ability, particularly among Chinese patients. The nomogram\u0026apos;s AUC of 0.882 (95% CI: 0.786-0.979) significantly surpasses the moderate prediction level threshold of 0.7, indicating its strong capacity to predict the target event. In contrast, the Gouveia system had an AUC of only 0.551 (95% CI: 0.395-0.708), approaching random prediction and lacking reliability. At a cut-off value of 0.427, the nomogram achieved 66.7% sensitivity, 96.1% specificity, 76.9% PPV, 93.7% NPV, and 91.3% accuracy\u0026mdash;demonstrating a \u0026quot;high specificity priority\u0026quot; clinical advantage. Conversely, the Gouveia system had only 43.8% sensitivity and 31.8% PPV, with high risks of missed and incorrect diagnoses and 74.7% accuracy. The nomograms\u0026rsquo; strong discriminative ability (AUC\u0026gt;0.8), balanced sensitivity, and high specificity make it more robust than traditional scoring systems, supporting its potential clinical utility for identifying 1-year chronic complications in pediatric ASA.\u003c/p\u003e\n\u003cp\u003eWe encountered several limitations when analyzing our results. First, the data came from a single-center retrospective analysis of Chinese patients with ASA, which may introduce information bias and limit generalizability. Second, the sample size was small, and there was a lack of external datasets for validation. Future research should use multi-center, large-sample clinical data to refine this prediction model.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the current prediction model shows promise in predicting the risk of chronic complications in children with ASA. It could guide clinical decisions, but more research is needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute suppurative arthritis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMagnetic resonance imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOAIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOsteoarticular Infections\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite blood cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSerum amyloid A\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHematocrit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHGB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean platelet volume\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlanine transaminase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAspartate transaminase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePositive predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNegative predictive value\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparticipate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This was a retrospective study, and IRB approval was obtained (approval No. [2025] 242A01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangzhou Science and Technology Basic and Applied Basic Research Program (202201011108).\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (82402837).\u003c/p\u003e\n\u003cp\u003eGuangzhou Medical University 2024 Research Capability Enhancement Plan Project.\u003c/p\u003e\n\u003cp\u003eDoctoral Research Initiation Project of Guangzhou Women and Children Medical Center (No. 2023BS015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ, XZ, FC and HX: study design\u003c/p\u003e\n\u003cp\u003eYZ, XZ, FC, FX: data analysis\u003c/p\u003e\n\u003cp\u003eYZ, FC: manuscript preparation\u003c/p\u003e\n\u003cp\u003eFC, FX, HX: critical review of the manuscript\u003c/p\u003e\n\u003cp\u003eHX, FC: supervision\u003c/p\u003e\n\u003cp\u003eAll authors: data collection and approval of the final version to be published\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all individuals who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErkilinc M, Gilmore A, Weber M, Mistovich RJ. Current Concepts in Pediatric Septic Arthritis. The Journal of the American Academy of Orthopaedic Surgeons 2021, 29, 196\u0026ndash;206. https://doi.org/10.5435/JAAOS-D-20-00835. \u003c/li\u003e\n\u003cli\u003eJeyanthi JC, Yi KM, Allen JC Jr, Gera SK, Mahadev A. Epidemiology and Outcome of Septic Arthritis in Childhood: A 16-Year Experience and Review of Literature. Singapore medical journal 2022, 63, 256\u0026ndash;262. https://doi.org/10.11622/smedj.2020140. \u003c/li\u003e\n\u003cli\u003eHoward-Jones AR, Isaacs D, Gibbons PJ. Twelve-Month Outcome Following Septic Arthritis in Children. Journal of Pediatric Orthopaedics B 2013, 22, 486\u0026ndash;490. https://doi.org/10.1097/BPB.0b013e32836027ca. \u003c/li\u003e\n\u003cli\u003ePimentel de Araujo F, Monaco M, Del Grosso M, Pirolo M, Visca P, Pantosti A. Staphylococcus Aureus Clones Causing Osteomyelitis: A Literature Review (2000-2020). Journal of global antimicrobial resistance 2021, 26, 29\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eJunie LM, Jeican II, Matros L, Pandrea SL. Molecular Epidemiology of the Community-Associated Methicillin-Resistant Staphylococcus Aureus Clones: A Synthetic Review. Clujul medical (1957) 2018, 91, 7\u0026ndash;11. https://doi.org/10.15386/cjmed-807. \u003c/li\u003e\n\u003cli\u003eForlin E, Milani C. Sequelae of Septic Arthritis of the Hip in Children: A New Classification and a Review of 41 Hips. Journal of pediatric orthopedics 2008, 28, 524\u0026ndash;528. https://doi.org/10.1097/BPO.0b013e31817bb079. \u003c/li\u003e\n\u003cli\u003eSamora JB, Klingele K. Septic Arthritis of the Neonatal Hip: Acute Management and Late Reconstruction. The Journal of the American Academy of Orthopaedic Surgeons 2013, 21, 632\u0026ndash;641. https://doi.org/10.5435/JAAOS-21-10-632. \u003c/li\u003e\n\u003cli\u003eSaad L, Hupin M, Buteau C, Nault ML. Late sequelae of osteoarticular infections in pediatric patients: A single-center study. Medicine 2021, 100, e23765. https://doi.org/10.1097/MD.0000000000023765. \u003c/li\u003e\n\u003cli\u003eGouveia C, Subtil A, Aguiar P, Canh\u0026atilde;o H, Norte S, Arcangelo J, Varandas L, Tavares D. Osteoarticular Infections: Younger Children With Septic Arthritis and Low Inflammatory Patterns Have a Better Prognosis in a European Cohort. Pediatric Infectious Disease Journal 2023, 42, 969\u0026ndash;974. https://doi.org/10.1097/INF.0000000000004074. \u003c/li\u003e\n\u003cli\u003eRuzbarsky JJ, Gladnick BP, Dodwell E. Diagnosing Septic Arthritis in the Synovial White Cell Count \u0026ldquo;Gray Zone\u0026rdquo;. HSS journal: the musculoskeletal journal of Hospital for Special Surgery 2016, 12, 190\u0026ndash;192. https://doi.org/10.1007/s11420-015-9480-6. \u003c/li\u003e\n\u003cli\u003eCosta GG, Grassi A, Lo Presti M, Cialdella S, Zamparini E, Viale P, Filardo G, Zaffagnini S. White Blood Cell Count Is the Most Reliable Test for the Diagnosis of Septic Arthritis After Anterior Cruciate Ligament Reconstruction: An Observational Study of 38 Patients. Arthroscopy 2021, 37, 1522-1530.e2. https://doi.org/10.1016/j.arthro.2020.11.047.\u003c/li\u003e\n\u003cli\u003eGuo RF, Ward PA. Role of C5a in Inflammatory Responses. Annual review of immunology 2005, 23, 821\u0026ndash;852. https://doi.org/10.1146/annurev.immunol.23.021704.115835.\u003c/li\u003e\n\u003cli\u003eCedzynski M, Thielens NM, Mollnes TE, Vorup-Jensen T. Editorial: The Role of Complement in Health and Disease. Frontiers in immunology 2019, 10, 1869. https://doi.org/10.3389/fimmu.2019.01869. \u003c/li\u003e\n\u003cli\u003eVandendriessche S, Cambier S, Proost P, Marques PE. Complement Receptors and Their Role in Leukocyte Recruitment and Phagocytosis. Frontiers in cell and developmental biology 2021, 9, 624025. https://doi.org/10.3389/fcell.2021.624025. \u003c/li\u003e\n\u003cli\u003eLi L, Cui L, Lin P, Liu Z, Bao S, Ma X, Nan H, Zhu W, Cen J, Mao Y, Ma X, Jiang L, Nie Y, Ginhoux F, Li Y, Li H, Hui L. Kupffer-Cell-Derived IL-6 Is Repurposed for Hepatocyte Dedifferentiation via Activating Progenitor Genes from Injury-Specific Enhancers. Cell stem cell 2023, 30, 283-299.e9. https://doi.org/10.1016/j.stem.2023.01.009.\u003c/li\u003e\n\u003cli\u003eOsawa Y, Kojika E, Hayashi Y, Kimura M, Nishikawa K, Yoshio S, Doi H, Kanto T, Kimura K. Tumor Necrosis Factor-\u0026alpha;-Mediated Hepatocyte Apoptosis Stimulates Fibrosis in the Steatotic Liver in Mice. Hepatology communications 2018, 2, 407\u0026ndash;420. https://doi.org/10.1002/hep4.1158. \u003c/li\u003e\n\u003cli\u003ePerry BC, Soltys D, Toledo AH, Toledo-Pereyra LH. Tumor Necrosis Factor-\u0026alpha; in Liver Ischemia/Reperfusion Injury. Journal of investigative surgery : the official journal of the Academy of Surgical Research 2011, 24, 178\u0026ndash;188. https://doi.org/10.3109/08941939.2011.568594. \u003c/li\u003e\n\u003cli\u003eBlack D, Bird MA, Hayden M, Schrum LW, Lange P, Samson C, Hatano E, Rippe RA, Brenner DA, Behrns KE. TNF Alpha-Induced Hepatocyte Apoptosis Is Associated with Alterations of the Cell Cycle and Decreased Stem Loop Binding Protein. Surgery 2004, 135, 619\u0026ndash;628. https://doi.org/10.1016/j.surg.2003.11.004.\u003c/li\u003e\n\u003cli\u003eLowell CA, Stearman RS, Morrow JF. Transcriptional Regulation of Serum Amyloid A Gene Expression. The Journal of biological chemistry 1986, 261, 8453\u0026ndash;8461. \u003c/li\u003e\n\u003cli\u003eLindhorst E, et al. Acute Inflammation, Acute Phase Serum Amyloid A and Cholesterol Metabolism in the Mouse. Biochimica et biophysica acta 1997, 1339, 143\u0026ndash;154. https://doi.org/10.1016/S0167-4838(96)00227-0.\u003c/li\u003e\n\u003cli\u003eTape C, Kisilevsky R. Apolipoprotein A-I and Apolipoprotein SAA Half-Lives during Acute Inflammation and Amyloidogenesis. Biochimica et biophysica acta 1990, 1043, 295\u0026ndash;300. https://doi.org/10.1016/0005-2760(90)90030-2.\u003c/li\u003e\n\u003cli\u003eHoffman JS, Benditt EP. Plasma Clearance Kinetics of the Amyloid-Related High Density Lipoprotein Apoprotein, Serum Amyloid Protein (apoSAA), in the Mouse. Evidence for Rapid apoSAA Clearance. The Journal of clinical investigation 1983, 71, 926\u0026ndash;934. https://doi.org/10.1172/jci110847.\u003c/li\u003e\n\u003cli\u003eAgarwal A, Rastogi P. Outcome of Acute Septic Dislocation of Hip in Children Reduced at Arthrotomy. Journal of clinical orthopaedics and trauma 2021, 13, 95\u0026ndash;98. https://doi.org/10.1016/j.jcot.2020.12.007. \u003c/li\u003e\n\u003cli\u003eBinnet MS, Chakirgil GS, Adiyaman S, Ates Y. The Relationship between the Treatment of Congenital Dislocation of the Hip and Avascular Necrosis. Orthopedics 1992, 15, 73\u0026ndash;81. https://doi.org/10.3928/0147-7447-19920101-14.\u003c/li\u003e\n\u003cli\u003eMerckaert S, Zambelli PY. Treatment Perspective after Failed Open Reduction of Congenital Hip Dislocation. A Systematic Review. Frontiers in pediatrics 2023, 11, 1146332. https://doi.org/10.3389/fped.2023.1146332. \u003c/li\u003e\n\u003cli\u003eP\u0026auml;\u0026auml;kk\u0026ouml;nen M. Septic Arthritis in Children: Diagnosis and Treatment. Pediatric health, medicine and therapeutics 2017, 8, 65\u0026ndash;68. https://doi.org/10.2147/PHMT.S115429. \u003c/li\u003e\n\u003cli\u003eWoods CR, Bradley JS, Chatterjee A, et al. Clinical Practice Guideline by the Pediatric Infectious Diseases Society and the Infectious Diseases Society of America: 2021 Guideline on Diagnosis and Management of Acute Hematogenous Osteomyelitis in Pediatrics. Journal of the Pediatric Infectious Diseases Society 2021, 10, 801\u0026ndash;844. https://doi.org/10.1093/jpids/piab027.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute suppurative arthritis, Children, Risk prediction, Chronic complications, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7797237/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797237/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePediatric acute suppurative arthritis (ASA) carries a risk of chronic complications, and predicting these complications is crucial for optimizing prognosis. We sought to develop a risk prediction model to identify chronic complications in children with ASA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective observational study enrolled children (ages one month to 18 years) diagnosed with ASA who were hospitalized at a tertiary pediatric hospital between 2016 and 2023. We documented clinical management, complication status, and sequelae, and constructed a multivariate logistic regression model for predicting chronic complications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 95 children were identified, 16.8% of whom experienced chronic complications from ASA over a 12-month follow-up period. Univariate logistic analysis identified the following factors associated with chronic complication development: white blood cell (WBC), serum amyloid A (SAA), hematocrit, hemoglobin, mean platelet volume, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) on admission; SAA, hematocrit (HCT), and hemoglobin (HGB) at discharge; bacteremia; Staphylococcus aureus detection; bone abscess; delayed source control; bone debridement; isolated arthritis; and arthritis combined with dislocation or subluxation (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On further multivariate logistic regression analysis, we identified four independent predictors: WBC on admission (OR\u0026thinsp;=\u0026thinsp;1.165, 95% CI: 1.038\u0026ndash;1.308), ALT on admission (OR\u0026thinsp;=\u0026thinsp;1.014, 95% CI: 1.004\u0026ndash;1.025), SAA at discharge (OR\u0026thinsp;=\u0026thinsp;1.153, 95% CI: 1.029\u0026ndash;1.292), and arthritis combined with dislocation or subluxation (OR\u0026thinsp;=\u0026thinsp;28.134, 95% CI: 3.691\u0026ndash;214.431). The area under the receiver operating characteristic (ROC) curve was 0.882 (95% CI: 0.786\u0026ndash;0.979). The logistic regression model formula was: Log(P) = -8.459\u0026thinsp;+\u0026thinsp;0.153\u0026times;WBC on admission\u0026thinsp;+\u0026thinsp;0.014\u0026times;ALT on admission\u0026thinsp;+\u0026thinsp;0.142\u0026times;SAA at discharge\u0026thinsp;+\u0026thinsp;3.337\u0026times;arthritis combined with dislocation or subluxation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe prediction model for chronic complications of pediatric ASA incorporates four key variables: WBC on admission, ALT on admission, SAA at discharge, and arthritis combined with dislocation or subluxation. This model has been shown to effectively predict chronic complication risk in children with ASA.\u003c/p\u003e","manuscriptTitle":"Developing a nomogram for predicting chronic complications in pediatric acute suppurative arthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 01:00:52","doi":"10.21203/rs.3.rs-7797237/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c916613a-67c4-43e8-8e0c-7b1df2412cd3","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T09:24:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 01:00:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7797237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797237","identity":"rs-7797237","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}