Evaluation of Serum Albumin and Globulin in Combination With C-Reactive Protein Improves Serum Diagnostic Accuracy for Low-Grade Periprosthetic Joint Infection

Choe, Han-Cheol; Kobayashi, Naomi; Abe, Koki; Hieda, Yuta; Tezuka, T.; Inaba, Yutaka

doi:10.1016/j.arth.2022.09.011

Cited by 19 publications

(20 citation statements)

References 32 publications

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“…Given the wasting condition of infectious diseases, serum proteins are increasingly focused during the diagnosis and treatment of multiple infectious and inflammatory diseases ( Chen et al., 2020a ). Several recent studies, including our study, have reported serum protein as potential markers for diagnosing PJI ( Wang et al., 2021 ; Choe et al., 2022 ; Shang et al., 2022 ). Although the independent diagnostic value of overall serum globulin was only fair in our previous study, the potential of serum globulin for diagnosing PJI is still attractive since globulin is related to immune reaction and has little correlation with coagulative system.…”

Section: Introductionmentioning

confidence: 59%

“…Although Hyonmin et al. pointed out that serum albumin has high diagnostic value in mild PJI ( Choe et al., 2022 ), most recent studies failed to confirm its independent diagnostic value. As a result, serum albumin could only be used as a numerator or denominator in a calculated ratio such as albumin to globulin ratio ( Shi et al., 2021 ; Shang et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

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Significance of serum protein electrophoresis in the diagnosis of Tsukayama type IV periprosthetic joint infection

Lu,

Hu,

et al. 2024

Front. Cell. Infect. Microbiol.

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ObjectivesTo investigate the efficacy of serum protein electrophoresis (SPE) in the diagnosis of periprosthetic joint infection (PJI) after hip and knee arthroplasty.MethodsThe medical records of patients undergoing hip and knee arthroplasty at a class A tertiary hospital between August 2013 and January 2021 were retrospectively investigated. A total of 179 patients were included and divided into two groups: 66 patients in the PJI group and 113 patients in the aseptic loosening (AL) group. Serum C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), D-dimer, Fibrinogen, Serum albumin and the proportion of serum protein in SPE were compared between the two groups. The diagnostic sensitivity and specificity were determined using the receiver operating characteristic (ROC) curve, and the diagnostic value was compared using the area under the ROC curve (AUC).ResultsThere was no significant difference in age, sex and body mass index (BMI) between PJI group and AL group (P>0.05), but there was significant difference in the ratio of hip to knee (X2 = 22.043, P<0.001). The CRP, ESR, D-dimer, Fibrinogen and the proportion of α1 globulin band in PJI group was 22.99(10.55,40.58) mg/L, 37.00(23.00,61.70) mm/h, 790.00(500.00,1500.00) ng/ml, 4.84(3.81,5.55) g/L and 5.80(5.00,7.73) % which was higher than that in AL group [1.89(0.50,4.12) mg/L, U=7.984, P<0.001; 10.10(7.00,16.90) mm/h, U=8.095, P<0.001; 570.00(372.50,780.00) ng/ml, U=3.448, P<0.001; 2.84(2.45,3.43) g/L, U=8.053, P<0.001 and 4.20(3.90,4.80) %, U=8.154, P<0.001]. The Serum albumin and the proportion of Albumin band in PJI group was 36.10(33.10,39.00) g/L and 49.00(44.95,52.20) % which was lower than that in AL group [38.10(34.00,41.10) g/L, U=-2.383, P=0.017 and 54.40(51.55,56.70) %, U=-6.162, P<0.001]. The proportion of In PJI group, the AUC of proportion of α1 globulin was 0.8654, which was equivalent to CRP (0.8698), ESR (0.8680) and outperformed that of fibrinogen (0.8025).ConclusionsElevated proportion of α1 globulin in SPE presented with good diagnostic value for Tsukayama type IV PJI, and its accuracy was comparable to those of ESR and CRP. And α1 globulin can assist with CRP and ESR to determining the timing of second-stage revision.

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Section: Introductionmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Significance of serum protein electrophoresis in the diagnosis of Tsukayama type IV periprosthetic joint infection

Lu,

Hu,

et al. 2024

Front. Cell. Infect. Microbiol.

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“…The AGR with a cutoff value of 1.2 has been identified as a highly sensitive combination marker. 23 This is visually represented in Figure 3 A–3C, where the red line denotes an AGR of 1.2. The blue line, fitted using loess regression, provides a fundamental representation of the two-dimensional data distribution.…”

Section: Resultsmentioning

confidence: 96%

“…The results ( Figures 3 B and 3C) also clearly showed that the AGR of patients with COVID-19 and myocarditis were significantly lower than that of healthy people, which are consistent with previous research results. 23 , 34 , 35 Furthermore, the heatmap and bar chart ( Figures 3 A–3C) provide a visual and intuitive way to understand the distribution of these indicators, which could be helpful in both clinical and research settings. The clear demarcation in the AGR between healthy individuals and those with COVID-19 or myocarditis could serve as a valuable tool for early detection and intervention.…”

Section: Discussionmentioning

confidence: 99%

Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices

Li,

Cheng,

et al. 2024

iScience

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“…GLB is closely related to human immunity. When the human body is invaded by viruses or cancer cells, GLB will rapidly increase [ 38 ]. As we know, the total protein is the sum of albumin and globulin, so ALB/GLB ratio (AGR) is one of the key indicators in the combination of indicators for screening cancer.…”

Section: Resultsmentioning

confidence: 99%

A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis

Wang

Huang

2022

Current Oncology

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Clear cell renal cell carcinoma (ccRCC) accounts for more than 90% of all renal cancers. The five-year survival rate of early-stage (TNM 1) ccRCC reaches 96%, while the advanced-stage (TNM 4) is only 23%. Therefore, early screening of patients with renal cancer is essential for the treatment of renal cancer and the long-term survival of patients. In this study, blood samples of patients were collected and a pre-defined set of blood indicators were measured. A random forest (RF) model was established to predict based on each indicator in the blood, and was trained with all relevant indicators for comprehensive predictions. In our study, we found that there was a high statistical significance (p < 0.001) for all indicators of healthy individuals and early cancer patients, except for uric acid (UA). At the same time, ccRCC also presented great differences in most blood indicators between males and females. In addition, patients with ccRCC had a higher probability of developing a low ratio of albumin (ALB) to globulin (GLB) (AGR < 1.2). Eight key indicators were used to classify and predict renal cell carcinoma. The area under the receiver operating characteristic (ROC) curve (AUC) of the eight-indicator model was as high as 0.932, the sensitivity was 88.2%, and the specificity was 86.3%, which are acceptable in many applications, thus realising early screening for renal cancer by blood indicators in a simple blood-draw physical examination. Furthermore, the composite indicator prediction method described in our study can be applied to other clinical conditions or diseases, where multiple blood indicators may be key to enhancing the diagnostic potential of screening strategies.

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Evaluation of Serum Albumin and Globulin in Combination With C-Reactive Protein Improves Serum Diagnostic Accuracy for Low-Grade Periprosthetic Joint Infection

Cited by 19 publications

References 32 publications

Significance of serum protein electrophoresis in the diagnosis of Tsukayama type IV periprosthetic joint infection

Significance of serum protein electrophoresis in the diagnosis of Tsukayama type IV periprosthetic joint infection

Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices

A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis

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