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Bacterial infection in patients with juvenile systemic lupus erythematosus and fever

Pediatric Rheumatology volume 23, Article number: 39 (2025) Cite this article

Abstract

Background

Juvenile Systemic Lupus Erythematosus (JSLE) is a chronic, systemic autoimmune disease characterized by an increased susceptibility to infections. Fever in these patients can result from infection, heightened lupus activity, or a combination of both. Various clinical factors and biomarkers have been proposed to differentiate between infection and disease activity, but the results remain inconclusive. The Systemic Lupus Erythematosus Disease Activity Index-2000 (SLEDAI-2 k) is used to assess lupus activity in the presence or absence of infection. This study aimed to identify factors associated with bacterial infections in JSLE patients presenting with fever.

Methods

A case–control study, approved by the institutional ethics committee, was conducted.

Results

Bacterial infection was identified in 17% of 116 patients. Factors evaluated included immunomodulator use, high-dose steroids, renal replacement therapy, erythrocyte sedimentation rate (ESR) > 20, C-reactive protein (CRP) > 60 and > 90 mg/L, ferritin > 500 ng/mL, neutrophil-to-lymphocyte ratio (NLR) > 6, platelet-to-lymphocyte ratio (PLR) > 133, procalcitonin (PCT) > 0.9 ng/mL, lymphocyte-to-C4 ratio (LC4R) > 66.7, and ESR/CRP ratio < 2. In the adjusted model, PCT > 0.9 ng/mL retained significance with p < 0.01. Nagelkerke’s R2 was 0.65, and the Hosmer–Lemeshow test indicated good internal validity.

Conclusions

Bacterial infection was detected in 17% of JSLE patients with fever. Procalcitonin > 0.9 ng/mL is a critical marker for identifying bacterial infection. NLR, PLR, ESR/CRP ratio, LC4R, and ferritin require further investigation to establish definitive cut-off values for differentiating bacterial infections from other infections or disease activity. Individual patient evaluation remains the recommended approach for diagnosis.

Background

Systemic lupus erythematosus (SLE) is a chronic, systemic autoimmune disease characterized by the presence of autoantibodies caused by immune system dysregulation. Its prevalence ranges from 3.2 to 300 cases per 100,000 people, with an incidence of 1.4 to 8.7 per 100,000 people [1]. SLE exhibits extensive phenotypic variability, with juvenile-onset SLE (JSLE) being the most common, accounting for 55.7% of cases. JSLE typically manifests between the ages of 7 and 13 [2] and is associated with a higher susceptibility to infections, increased disease activity, greater tissue and organ damage [3], and the need for more intensive immunosuppressive therapy [4]. The disease alternates between potentially life-threatening periods of immune activity and phases of remission [5,6,7].

In JSLE, infections contribute to morbidity and mortality in 44% of cases and increase healthcare costs by 30% due to prolonged hospitalizations and admissions to pediatric intensive care units. Infections can be bacterial, viral, or fungal in nature [8,9,10]. Approximately 50% of adults with SLE experience severe infectious episodes requiring extended hospitalization, and early identification of infected patients significantly improves outcomes [11]. Fever is a common symptom of both infection and heightened lupus activity, and these conditions often coexist. Fever is reported in 36–96% of adults with SLE, with 60% of cases attributed to disease activity, 23% to infection, and 12% to both conditions [12, 13].

Several clinical features and biomarkers have been investigated to assist clinicians in differentiating between disease activity and infection in SLE patients with fever. Evidence regarding the utility of biomarkers such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), neutrophil-to-lymphocyte ratio (NLR), ferritin, and procalcitonin (PCT) remains inconclusive [14,15,16,17,18,19,20]. Ju-Yang et al. (2019) identified serositis, hematologic involvement, and high-dose glucocorticoids (> 7.5 mg/day of prednisolone) as factors associated with severe infections, such as those requiring Intensive Care Unit (ICU) hospitalization or intravenous antibiotic use [21].

Zhai et al. (2021) reported a scoring system for identifying bacterial infections in adults with SLE, achieving an area under the curve (AUC) of 0.842 and a 95% confidence interval (CI) of 0.794–0.891. Variables such as white blood cell count, neutrophil count, ESR, CRP, PCT, interleukin-6, interleukin-10, interferon-gamma, and tumor necrosis factor-alpha were significantly elevated in patients with bacterial infections [22].

In pediatrics, Luo et al. reported that the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI), fever, CRP, PCT, lymphocyte percentage, NLR, hemoglobin, and the SLE Disease Activity Index 2000 (SLEDAI-2 K) are predictive of infection, with an area under the curve (AUC) of 0.7886, sensitivity of 63.5%, and specificity of 89.2%. The authors recommend individualized clinical analysis for decision-making [23].

Sari et al. identified urinary tract infections (41%), skin and soft tissue infections (20.5%), and pneumonia (20.5%) as the most common infections, with methylprednisolone pulse therapy being a predictor of infection [24].

Disease activity indices, such as SLEDAI, are designed to classify disease activity regardless of the triggering cause [25,26,27]. Biomarkers like NLR, ESR/CRP ratio, platelet-to-lymphocyte ratio (PLR), and lymphocyte/C4 index (L/C4) have been studied in adult Colombian populations to differentiate infection from disease activity. The optimal cutoff values for infection detection were NLR > 6, ESR/CRP ratio < 2, PLR > 132, and L/C4 index > 66.7 [28].

The objective of this study is to identify factors associated with bacterial infections in JSLE patients with fever.

Ethical considerations

The study protocol was approved by the Pediatric Research Group (GRINPED) under record 072 on August 12, 2023; the Ethics and Bioethics Research Committee of Fundación Clínica Infantil Club Noel under registration 268 on October 20, 2023; and the Ethics Committee of Universidad Libre Seccional Cali, Colombia (Resolution CEB-10–2024, dated March 28, 2024), in accordance with the Declaration of Helsinki of the World Medical Association.

Methods

An observational, analytical case–control study with retrospective data collection was conducted at a pediatric referral institution in Cali, Colombia (Fundación Clínica Infantil Club Noel). The study was self-funded.

Patient selection

The study included 116 febrile patients with juvenile systemic lupus erythematosus (JSLE) who were admitted between January 2015 and December 2023.

Cases were defined as JSLE patients diagnosed according to the American College of Rheumatology 2017 criteria, the SLICC 2012 criteria, or diagnostic confirmation by a pediatric rheumatology expert, who experienced at least one febrile episode exceeding 38 °C of non-hospital onset, with bacterial infection confirmed by culture isolation or detection using staining, antigenic tests, serological, or molecular methods.

Controls included febrile JSLE patients, as defined above, in whom bacterial pathogens could not be detected or isolated.

Patients were excluded if they:

  • Had more than 20% of their data missing.

  • Received initial stabilization care and were referred to another institution.

  • Presented with severe trauma, burns, or underwent major surgery.

  • Had malignant neoplasms, coexisting inflammatory bowel disease, chronic liver disease, or pre-existing and known chronic infections (e.g., osteomyelitis, endocarditis, active HIV, or hepatitis B).

    • Experienced macrophage activation syndrome.

The calculated sample size (Epi Info 7.10) of 18 cases and 78 controls (Fleiss method) was achieved. Figure 1 shows the patient selection flowchart.

Fig. 1
figure 1

Flowchart of patient selection for the study

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Measures

The data were sourced from medical records. Disease activity was classified using the SLEDAI-2 K scale [25,26,27]. Sociodemographic variables included age, sex, place of residence, and social security coverage. Clinical variables analyzed included duration of fever (in days), temperature at admission, disease onset, nutritional status, use of immunosuppressants, use of high-dose steroids (> 7.5 mg/day of prednisolone), renal replacement therapy, and the SLEDAI-2 K score.

Laboratory tests analyzed were those performed upon patient admission and included hematological parameters and biomarkers such as C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), ferritin (FERRI), complement C3 (C3), and complement C4 (C4). Ratios such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), ESR/CRP ratio, and lymphocyte/C4 ratio were also calculated. Immunological tests included rheumatoid factor (RF), antinuclear antibodies (ANA), anti-dsDNA antibodies, anti-RNP antibodies, anti-Sm antibodies, anti-SS-A/Ro antibodies, and perinuclear anti-neutrophil cytoplasmic antibodies (p-ANCA), as well as C3 and C4 levels.

The dependent variable was bacterial infection, defined as isolation by culture or detection through molecular tests, staining, or antigen tests. Clinical bacterial infections without microbiological confirmation were not classified as cases to minimize investigator bias.

Data collection

Data were collected in Microsoft Office Excel® and processed using IBM SPSS Statistics version 29.0.2.0 (2020) © International Business Machines Corporation.

Statistical analysis

The distribution of quantitative variables was determined using the Kolmogorov–Smirnov test, and in the case of non-normality, they were summarized by median and interquartile range. Qualitative variables were summarized using frequencies and percentages. Associations were established through bivariate and multivariate analyses, assessing the goodness of fit of the resulting model. To assess how well the regression model explains the observed data, Nagelkerke's R-squared and the Hosmer–Lemeshow test were used.

Results

Sociodemographic characteristics

A total of 116 febrile JSLE patients were included in the study, of which 20 patients with confirmed bacterial detection or isolation formed the case group, and 96 patients without bacterial detection or isolation comprised the control group, yielding a case-to-control ratio of 1:4. The cohort consisted of 16 males and 100 females, with 70 patients residing in Cali and 46 from other municipalities.

Regarding social security coverage, 37 patients were under the contributory scheme, 78 under the subsidized scheme, and one under a special scheme.

Diagnosis and clinical characteristics

Twenty-eight patients were unaware of their disease status, and 19 were undergoing renal replacement therapy (RRT). The proportion of confirmed bacterial infection was 17%.

All 116 study patients had a positive SLEDAI-2 K score indicating disease activity, with 22 patients classified as having mild activity and 94 as having moderate to severe activity.

Table 1 provides a detailed description of the sociodemographic and clinical characteristics of the study population, stratified by case and control groups.

Table 1 Sociodemographic and clinical characteristics of the study population, stratified by case and control groups
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Table 2 presents the quantitative clinical and laboratory characteristics of interest. The distribution of all quantitative variables, except for the SLEDAI-2 K score, was non-parametric as determined by the Kolmogorov–Smirnov test. All variables, including the SLEDAI-2 K score, are summarized using the median and interquartile range (IQR).

Table 2 Clinical and laboratory characteristics at admission of febrile JSLE patients, stratified by confirmed bacterial infection
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Laboratory studies

Regarding immunological tests for juvenile systemic lupus erythematosus (JSLE), the most commonly observed antinuclear antibody (ANA) pattern was homogeneous, found in 21 patients, followed by a speckled pattern in 17 patients and a fine granular pattern in 7 of the 59 patients tested.

The most frequently reported ANA titers were 1:1280, observed in 22 cases, followed by 1:640 in 14 patients and 1:2560 in 8, out of a total of 58 patients tested.

Anti-dsDNA antibodies were tested in 64 patients and were positive in 16. Anti-RNP was positive in 4 of 48 patients, anti-Sm in 4 of 47, anti-SS-A/Ro in 10 of 47, and rheumatoid factor in 12 of 36 patients tested.

Table 3 presents the studies conducted on the 116 patients to identify the etiology of the infection, while Table 4 provides details of patients with bacterial isolations or detections and their associated non-bacterial co-infections.

Table 3 Studies performed on JSLE patients with fever to identify the etiology of the infection (n = 116)
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Table 4 Bacterial isolations or detections in JSLE patients with fever and confirmed bacterial infection
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Bivariate analysis

Bivariate analysis in Tables 5, 6 and 7 describes the relationships between sociodemographic and clinical characteristics, biomarkers and indicators, and immunological tests with confirmed bacterial infection in patients with Juvenile Systemic Lupus Erythematosus (SLEJ) and fever.

Table 5 Bivariate analysis of sociodemographic and clinical factors and their relationship with bacterial infection in patients with SLEJ and fever (n = 116, Ca = 20, Co = 96)
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Table 6 Bivariate analysis of biomarkers and their relationship with bacterial infection in patients with SLEJ and fever (n = 116, Ca = 20, Co = 96)
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Table 7 Bivariate analysis of immunological disease activity markers and their relationship with bacterial infection in patients with SLEJ and fever (n = 116, Ca = 20, Co = 96)
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Multivariate analysis

For the multivariate analysis, variables with a significant crude odds ratio (OR) and those with a crude OR < 0.25 were included in the explanatory model for bacterial infection. These variables included the use of immunomodulators, high-dose steroids, renal replacement therapy, erythrocyte sedimentation rate (ESR) > 20, C-reactive protein (CRP) > 60, CRP > 90, ferritin > 500, interleukin (IL) > 6, platelets > 133, procalcitonin (PCT) > 0.9, and, based on researcher interest, the indices ILC4 > 66.7 and ESR/CRP < 2. The variable PCT > 0.9 retained significance with a p-value < 0.01. The other variables were not included in the final model (Table 8). The Nagelkerke R-squared was 0.65. The Hosmer–Lemeshow test demonstrated adequate internal validity, with a chi-squared value of 0.000, indicating perfect concordance between the observed and expected frequencies.

Table 8 Binary logistic regression of factors associated with bacterial infection in patients with SLE and fever
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Discussion

In the studied population of patients with SLEJ and fever, the confirmed bacterial infection rate was 17%, which is lower than the 35% reported in Colombia for adult SLE patients by Beltrán et al. and the 32% found in Medellín, Colombia, as reported by Santamaría-Alza et al. in adult populations [13, 28]. The most common infection identified was urinary tract infection, consistent with the findings of Sari et al., who reported a rate of 41% [24].

The clinical presentation of patients with bacterial infections did not significantly differ from those without bacterial infections. In both groups, the primary reason for consultation was systemic or musculoskeletal symptoms, observed in 40% and 51% of cases, respectively. Neither the duration of fever nor the onset of SLEJ were associated with the presence of bacterial infection. The use of immunomodulators in SLEJ initially suggested a higher likelihood of bacterial infection, as reported by Ju-Yang et al. in adult SLE patients. However, this factor did not remain significant in the final model. Other factors, such as hematological involvement and high-dose steroid use—factors previously suggested in adult populations—did not show significant differences between the groups in this study of SLEJ patients [21].

Direct biomarkers such as procalcitonin, CRP, and ESR did not show significant differences between the groups when using the cut-off values suggested for healthy individuals. A recent study in an adult population by Abdel-Magied et al. used a procalcitonin cut-off value of 0.9 ng/mL, which is higher than the 0.25–0.50 ng/mL range typically suggested for healthy subjects.This study confirmed a significant association between elevated procalcitonin levels and bacterial infection [29]. Similarly, the present study found that a procalcitonin level of 0.9 ng/mL was the only variable retained in the final explanatory model for bacterial infection in SLEJ patients with fever. Other variables, such as CRP > 60 mg/L, CRP > 90 mg/L, ESR > 20 mm/h, and ferritin > 500 ng/mL, although significant in the bivariate analysis, were excluded from the final model.

Regarding biomarker indicators, Santamaría-Alza's study in Medellín, Colombia, used a neutrophil-to-lymphocyte ratio (NLR) cut-off of 6.3 in an adult population, significantly higher than the 2.0 cut-off proposed by Abdel-Magied. For other indices, Santamaría-Alza set the platelet-to-lymphocyte ratio (PLR) cut-off at 132.9, suggested a value of < 2 for the ESR/CRP ratio as indicative of infection, and used a lymphocyte-C4 ratio of 66.7. In that study, the lymphocyte-C4 ratio emerged as the best-performing index.

In the present study, bivariate analysis suggested potential associations with IL > 6, PLR > 133, and, for research purposes, we included ILC4 > 66.7 and ESR/CRP < 2 in the multivariate analysis. However, these indices were not retained in the final binary logistic regression model for bacterial infection.

Immunological tests are widely used in adults as markers of disease activity and are incorporated into the SLEDAI-2 K scale. However, their usage in the study population was low, ranging from 17 to 55% depending on the specific marker. This may be attributed to the clinicians' primary focus on identifying the source of fever, leading to an initial treatment for infection. It's important to note that disease activity may increase in the presence of infection. The present study specifically focuses on patients with juvenile systemic lupus erythematosus, also known as childhood-onset systemic lupus erythematosus (cSLE) [30].

Limitations and biases

Our results must be interpreted in light of several limitations inherent to the chosen study style.

A strategy was implemented to minimize selection bias by clearly defining the study population (patients with SLE and fever) and selecting all eligible patients. The controls were drawn from the database of febrile SLE patients, which is a strength of the study. This design allowed for a focused investigation into bacterial infection in patients with SLEJ and fever, rather than in healthy individuals or those with SLEJ who were not febrile.

To further control for bias, patients with a clinical diagnosis of bacterial infection, but without microbiological confirmation, were included as controls, regardless of whether they were receiving antimicrobial treatment. This helped minimize the possibility of biases that could arise from treatment-induced differences.

Information bias was mitigated by establishing operational definitions and using quantitatively measured exposures, which helped ensure the validity of the recorded data. The only reconstructed variables in the study were nutritional status and the SLEDAI-2 K score. Furthermore, the multivariate analysis was adjusted for potential confounding variables, strengthening the model's reliability.

Conclusions

The detection of bacterial infection in patients with SLEJ and fever continues to be a challenging clinical issue. In our study, the bacterial infection rate was 17%. Procalcitonin, with a cut-off of 0.9 ng/mL, proved to be a valuable decision-making tool, although it is not definitive on its own. Other biomarkers such as interleukin (IL), platelet-to-lymphocyte ratio (PLR), ESR/CRP ratio, lymphocyte-C4 ratio (ILC4), and ferritin levels warrant further investigation. The optimal cut-off values for these biomarkers in distinguishing bacterial infection from other types of infections or disease activity in febrile SLEJ patients are yet to be established. Ultimately, individualized patient assessment remains the cornerstone of clinical practice.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

GRINPED:

Pediatric Research Group

SLE:

Systemic Lupus Erythematosus also known as Childhood-onset systemic lupus erythematosus (cSLE) (30)

JSLE:

Juvenile Systemic Lupus Erythematosus

SLEDAI-2k:

Systemic Lupus Erythematosus Disease Activity Index-2000

ESR:

Erythrocyte sedimentation rate

CRP:

C-reactive protein

NLR:

Neutrophil-to-lymphocyte ratio

PLR:

Platelet-to-lymphocyte ratio

PCT:

Procalcitonin

LC4R:

Lymphocyte-to-C4 ratio

ESR/CRP ratio:

Erythrocyte sedimentation rate - C-reactive protein ratio

SLE:

Systemic lupus erythematosus

ICU:

Intensive Care Unit

CI:

Confidence interval

AUC:

Area under the curve

SDI:

Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index

SLICC:

Systemic Lupus Collaborating Clinics

HIV:

Human Immunodeficiency Virus

RF:

Rheumatoid factor

ANA:

Antinuclear antibodies

Anti-dsDNA:

Anti-double stranded DNA antibodies

Anti-RNP:

Anti- ribonucleoprotein antibodies

Anti-Sm:

Anti-Smith antibodies

anti-SS-A/Ro:

Anti-Sjögren’s syndrome related antigen A antibodies

p-ANCA:

Perinuclear anti-neutrophil cytoplasmic antibodies

IQR:

Interquartile range

References

  1. Fatoye F, Gebrye T, Mbada C. Global and regional prevalence and incidence of systemic lupus erythematosus in low-and-middle-income countries: a systematic review and meta-analysis. Rheumatol Int. 2022;42:2097–107. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00296-022-05183-4.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ambrose N, Morgan TA, Galloway J, Ionnoau Y, Beresford MW, Isenberg DA. Differences in disease phenotype and severity in SLE across age groups. Lupus. 2016;25(14):1542–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/0961203316644333.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Massias JS, Smith EMD, Al-Abadi E, Armon K, Bailey K, Ciurtin C, et al. Clinical and laboratory phenotypes in juvenile-onset systemic lupus erythematosus across ethnicities in the UK. Lupus. 2021;30(4):597–607.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tucker LB, Uribe AG, Fernández M, Vilá LM, McGwin G, Apte M, et al. Adolescent onset of lupus results in more aggressive disease and worse outcomes: Results of a nested matched case-control study within LUMINA, a multiethnic US cohort (LUMINA LVII). Lupus. 2008;17(4):314–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Massias JS, Smith EMD, Al-Abadi E, Armon K, Bailey K, Ciurtin C, et al. Clinical and laboratory characteristics in juvenile-onset systemic lupus erythematosus across age groups. Lupus. 2020;29(5):474–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dung NTN, Loan HT, Nielsen S, Zak M, Petersen FK. Juvenile systemic lupus erythematosus onset patterns in Vietnamese children: a descriptive study of 45 children. Pediatr Rheumatol. 2012;10:38–45. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1546-0096-10-38.

    Article  Google Scholar 

  7. Battaglia M, Garrett-Sinha LA. Bacterial infections in lupus: roles in promoting immune activation and in pathogenesis of the disease. J Transl Autoimmun. 2021;4: 100084.

    Article  Google Scholar 

  8. Merino R, Remesal A, Loza SM. Lupus eritematoso sistémico. An Pediatr Contin. 2013;11(2):89–97.

    Google Scholar 

  9. Caggiani M. Lupus eritematoso sistémico en niños y adolescentes. Arch Pediatr Urug. 2015;86(4):273–83.

    Google Scholar 

  10. Enberg G, Espoz M, Enberg MG, Kahn ChM, Goity CF, Villalón V, Zamorano JR, et al. Infecciones en pacientes con Lupus Eritematoso Sistémico. Rev Méd Chil. 2009;137:1367–74.

    Google Scholar 

  11. Muñoz-Grajales C, Pinto L, Velásquez CJ, Márquez JD, Restrepo M. Complicaciones infecciosas en lupus eritematoso sistémico. Rev Colomb Reumatol. 2013;20(3):141–7.

    Google Scholar 

  12. Doria A, Canova M, Tonon M, Zen M, Rampudda E, Bassi N, et al. Infections as triggers and complications of systemic lupus erythematosus. Autoimmun Rev. 2008;8(1):24–8.

    Article  CAS  PubMed  Google Scholar 

  13. Beltrán A, Mora C, Bastidas AR, Aragón Guzmán DM. Characterisation of patients with lupus and fever: activity, infection or both. Rev Colomb Reumatol. 2020;27(2):95–102.

    Google Scholar 

  14. Wang J, Niu R, Jiang L, Wang Y, Shao X, Wu M, et al. The diagnostic values of C-reactive protein and procalcitonin in identifying systemic lupus erythematosus infection and disease activity. Medicine (Baltimore). 2019;98(33): e16882.

    Article  Google Scholar 

  15. Bador KM, Intan S, Hussin S, Gafor AHA. Serum procalcitonin has negative predictive value for bacterial infection in active systemic lupus erythematosus. Lupus. 2012;21(11):1172–7.

    Article  CAS  PubMed  Google Scholar 

  16. Liu LN, Wang P, Guan SY, Li XM, Li BZ, Leng RX, et al. Comparison of plasma/serum levels of procalcitonin between infection and febrile disease flare in patients with systemic lupus erythematosus: a meta-analysis. Rheumatol Int. 2017;37(12):1991–8.

    Article  CAS  PubMed  Google Scholar 

  17. Serio I, Arnaud L, Mathian A, Hausfater P, Amoura Z. Can procalcitonin be used to distinguish between disease flare and infection in patients with systemic lupus erythematosus? A systematic literature review. Clin Rheumatol. 2014;33(9):1209–15.

    Article  PubMed  Google Scholar 

  18. Wallbach M, Vasko R, Hoffmann S, Niewold TB, Müller GA, Korsten P. Elevated procalcitonin levels in a severe lupus flare without infection. Lupus. 2016;25:1625–6.

    Article  CAS  PubMed  Google Scholar 

  19. Lanoix JP, Bourgeois AM, Schmidt J, et al. Serum procalcitonin does not differentiate between infection and disease flare in patients with systemic lupus erythematosus. Lupus. 2011;20(2):125–30.

    Article  CAS  PubMed  Google Scholar 

  20. Li Z, Xiao Y, Zhang L. Application of procalcitonin, white blood cell count, and neutrophil-to-lymphocyte ratio in the diagnosis of systemic lupus erythematosus with a bacterial infection. Ann Palliat Med. 2020;9(6):3870–6. https://doiorg.publicaciones.saludcastillayleon.es/10.21037/apm-20-1777.

    Article  PubMed  Google Scholar 

  21. Jung JY, Yoon D, Choi Y, Kim HA, Suh CH. Associated clinical factors for serious infections in patients with systemic lupus erythematosus. Sci Rep. 2019;9:14749.

    Google Scholar 

  22. Zhai X, Feng M, Guo H, Liang Z, Wang Y, Qin Y, Wu Y, Zhao X, Gao C, Luo J. Development of prediction models for new integrated models and a bioscore system to identify bacterial infections in systemic lupus erythematosus. Front Cell Infect Microbiol. 2021;11: 620372. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fcimb.2021.620372.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Luo KL, Yang YH, Lin YT, Hu YC, Yu HH, Wang LC, et al. Differential parameters between activity flare and acute infection in pediatric patients with systemic lupus erythematosus. Sci Rep. 2020;10:19913. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-020-76789-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sari MK, Satria CD, Arguni E. Predictors of infection in children with systemic lupus erythematosus: a single-center study in Indonesia. Glob Pediatr Health. 2021;8:2333794X211005609. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/2333794X211005609.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Gladman DD, Urowitz MB, Caron D, Austin HA, Bell DA, Bloch DA, et al. Derivation of the SLEDAI, a disease activity index for lupus patients. Arthritis Rheum. 1992;35(6):630–40.

    Article  PubMed  Google Scholar 

  26. Shamim R, Farman S, Batool S, Khan SEA, Raja MKH. Association of systemic lupus erythematosus disease activity index score with clinical and laboratory parameters in pediatric-onset systemic lupus erythematosus. Pak J Med Sci. 2020;36(3):562–7.

    Article  Google Scholar 

  27. Khadijah S, Nazri SM, Wong KK, Zuraida W. Pediatric systemic lupus erythematosus. Retrospective analysis of clinico-laboratory parameters and their association with Systemic Lupus Erythematosus Disease Activity Index score. Saudi Med J. 2018;39(6):627–31. https://doiorg.publicaciones.saludcastillayleon.es/10.15537/smj.2018.6.22112.

  28. Santamaría-Alza Y, Fernández-Martínez D, Maceira-Duch M. Diagnostic performance of paraclinical indices in differentiating infection and clinical activity in patients with systemic lupus erythematosus in Medellín, Colombia. 2021. Repositorio Institucional de la Universitat Oberta de Catalunya. Available from: http://hdl.handle.net/10609/127011.

  29. Abdel-Magied RA, Mokhtar NW, Abdullah NM, Abdel-Naiem ASM. Infection versus disease activity in systemic lupus erythematosus patients with fever. BMC Rheumatol. 2024;8:34. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41927-024-00395-6.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Avar-Aydın PÖ, Brunner HI. Revisiting childhood-onset systemic lupus erythematosus. Turk Arch Pediatr. 2024;59(4):336–44. https://doiorg.publicaciones.saludcastillayleon.es/10.5152/TurkArchPediatr.2024.24097. PMID:39102578;PMCID:PMC11332533.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We express our deepest gratitude to the Public Health and Epidemiology Department at the Faculty of Health, Universidad Libre Seccional Cali, for their invaluable guidance and support throughout the development of this study. We also extend our heartfelt thanks to the Statistics Department at Fundación Clínica Infantil Club Noel, Cali, Colombia, for their expertise and collaboration in the data analysis, which greatly enhanced the rigor and validity of our research.

Funding

No funding was received for the redaction of the case report.

Author information

Authors and Affiliations

  1. Universidad Libre Seccional Cali, Cali, Valle del Cauca, Colombia

    José Fernando Gómez-Urrego, Nathalie Yepes-Madrid, Mónica Alexandra Gil-Artunduaga, María del Pilar Gómez-Mora, Luis Fernando Mejía-Rivera, Robinson Pacheco-López & Juan Pablo Rojas-Hernández

  2. Fundación Clínica Infantil Club Noel, Cali, Colombia

    José Fernando Gómez-Urrego, María del Pilar Gómez-Mora & Luis Fernando Mejía-Rivera

  3. Pediatric Research Group (GRINPED), Cali, Colombia

    José Fernando Gómez-Urrego, Nathalie Yepes-Madrid, Mónica Alexandra Gil-Artunduaga & Juan Pablo Rojas-Hernández

  4. Pediatric Rheumatology Service, Cali, Colombia

    María del Pilar Gómez-Mora

  5. Pediatric Infectious Disease Service, Cali, Colombia

    Luis Fernando Mejía-Rivera

  6. Valle del Cauca, Cali, Colombia

    José Fernando Gómez-Urrego, Nathalie Yepes-Madrid, Mónica Alexandra Gil-Artunduaga, María del Pilar Gómez-Mora, Luis Fernando Mejía-Rivera, Robinson Pacheco-López & Juan Pablo Rojas-Hernández

Authors
  1. José Fernando Gómez-Urrego
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  2. Nathalie Yepes-Madrid
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  3. Mónica Alexandra Gil-Artunduaga
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  4. María del Pilar Gómez-Mora
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  5. Luis Fernando Mejía-Rivera
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  6. Robinson Pacheco-López
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  7. Juan Pablo Rojas-Hernández
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Contributions

Each author contributed to the redaction, proofreading, and correction of the manuscript. JFGU and NYM contributed to the research, data recollection, analysis, writing, and proofreading, while MAGA, MPGM, LFMR, JPRH and RPL contributed in analysis data, corrected and adding relevant medical changes to the case. All authors read and approved the final manuscript. All authors participated in the acquisition, analysis, and interpretation of the data. Each author has agreed both to be personally accountable for their contributions and to ensure that questions related to the accuracy or integrity of any part of the work, (even ones in which the author was not personally involved), are appropriately investigated, resolved, and the resolution documented in the literature. All authors read and approved the final manuscript.

Corresponding author

Correspondence to José Fernando Gómez-Urrego.

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Approved by Fundación Clínica Infantil Club Noel Ethical Committee for Research, and Bioethical and Ethical Committee for Research at Universidad Libre, Sectional Cali.

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Not applied for this study.

Competing interests

The authors declare no competing interests.

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Gómez-Urrego, J.F., Yepes-Madrid, N., Gil-Artunduaga, M.A. et al. Bacterial infection in patients with juvenile systemic lupus erythematosus and fever. Pediatr Rheumatol 23, 39 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12969-025-01088-1

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12969-025-01088-1

Keywords

Pediatric Rheumatology

ISSN: 1546-0096