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

Diagnostic Performance of a Molecular Assay in Synovial Fluid Targeting Dominant Prosthetic Joint Infection Pathogens

Prosthetic joint infection (PJI) is one of the most serious complications of joint replacement surgery among orthopedic surgeries and occurs in 1 to 2% of primary surgeries. Additionally, the cause of PJIs is mostly bacteria from the Staphylococcus species, accounting for more than 98%, while fungi cause PJIs in only 1 to 2% of cases and can be difficult to manage. The current gold-standard microbiological method of culturing synovial fluid is time-consuming and produces false-negative and -positive results. This study aimed to identify a novel, accurate, and convenient molecular diagnostic method. The DreamDX primer-hydrolysis probe set was designed for the pan-bacterial and pan-fungal detection of DNA from pathogens that cause PJIs. The sensitivity and specificity of DreamDX primer-hydrolysis probes were 88.89% (95% CI, 56.50-99.43%) and 97.62% (95% CI, 87.68-99.88%), respectively, compared with the microbiological method of culturing synovial fluid, and receiver operating characteristic (ROC) area under the curve (AUC) was 0.9974 (*** p < 0.0001). It could be concluded that the DreamDX primer-hydrolysis probes have outstanding potential as a molecular diagnostic method for identifying the causative agents of PJIs, and that host inflammatory markers are useful as adjuvants in the diagnosis of PJIs.

Exploring the Impact of Adequate Energy Supply on Nutrition, Immunity, and Inflammation in Elderly Patients with Chronic Obstructive Pulmonary Disease

Background

Chronic Obstructive Pulmonary Disease (COPD) progression in the elderly is notably influenced by nutritional, immune, and inflammatory status. This study aimed to investigate the impact of adequate energy supply on these indicators in COPD patients.

Methods

COPD patients meeting specific criteria were recruited and categorized into energy-adequate and energy-deficient groups based on their energy supply. Comparable demographic factors such as age, gender, smoking and drinking history, COPD duration, inhaled drug classification, and home oxygen therapy application were observed. Notable differences were found in BMI and inhaled drug use between the two groups.

Results

The energy-adequate group exhibited significant improvements in various health indicators, including lymphocyte count, hemoglobin, CRP, total cholesterol, prealbumin, albumin, PNI, SII, SIRI, CAR, and CONUT scores in the secondary auxiliary examination. These positive changes suggest a notable enhancement in nutritional, immune, and inflammatory status.

Conclusion

This research highlights the substantial benefits of adequate energy supply in elderly COPD patients. The observed improvements in nutritional, immune, and inflammatory markers underscore the importance of addressing energy needs to positively influence disease-related outcomes in this population. These findings have implications for developing targeted interventions to optimize the well-being of elderly individuals with COPD.

Non-linear relationship between preoperative albumin-globulin ratio and postoperative pneumonia in patients with hip fracture

BACKGROUND AND OBJECTIVE

Postoperative pneumonia (POP) is the leading cause of death among patients with hip fractures. Simple and cost-effective markers can be used to assess the risk of these patients. This study aims to investigate the association between POP and preoperative albumin-globulin ratio (AGR) in patients with hip fractures.

METHODS

A retrospective analysis was conducted on data from 1417 hip fracture patients admitted to the Department of Orthopaedics at the hospital. Generalized additive and logistic regression models were used to determine both linear and non-linear associations between preoperative AGR and POP. A two-piece regression model was employed to determine the threshold effect.

RESULTS

The study included 1417 participants, with a mean age of 77.57 (8.53) years and 26.96% (382/1417) male patients. The prevalence of POP was 6.21%. Following full covariate adjustment, each unit increase in AGR was associated with a 79% reduction in the incidence of POP (OR, 0.23; 95% CI: 0.08-0.63; P = 0.0046). The inflection point was found to be 1.33 using a two-piecewise regression model. For each unit increase in AGR on the left side of the inflection point, the incidence of POP decreased by 93% (OR, 0.07; 95%CI: 0.02-0.34; P = 0.0010). However, there was no statistically significant correlation on the right side of the inflection point (OR, 0.84; 95% CI: 0.17-4.10; P = 0.8287).

CONCLUSION

There exists a non-linear association between preoperative AGR and the incidence of POP in elderly hip fracture patients. When AGR is less than 1.33, the incidence of POP is negatively correlated with AGR. However, there is no correlation when AGR is greater than 1.33.

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

During the persistent COVID-19 pandemic, the swift progression of acute myocarditis has emerged as a profound concern due to its augmented mortality, underscoring the urgency of prompt diagnosis. This study analyzed blood samples from 5,230 COVID-19 individuals, identifying key blood and myocardial markers that illuminate the relationship between COVID-19 severity and myocarditis. A predictive model, applying Bayesian and random forest methodologies, was constructed for myocarditis' early identification, unveiling a balanced gender distribution in myocarditis cases contrary to a male predominance in COVID-19 occurrences. Particularly, older men exhibited heightened vulnerability to severe COVID-19 strains. The analysis revealed myocarditis was notably prevalent in younger demographics, and two subvariants COVID-19 progression paths were identified, characterized by symptom intensity and specific blood indicators. The enhanced myocardial marker model displayed remarkable diagnostic accuracy, advocating its valuable application in future myocarditis detection and treatment strategies amidst the COVID-19 crisis.

A machine learning-based model for "In-time" prediction of periprosthetic joint infection

Background

Previous criteria had limited value in early diagnosis of periprosthetic joint infection (PJI). Here, we constructed a novel machine learning (ML)-derived, "in-time" diagnostic system for PJI and proved its validity.

Methods

We filtered "in-time" diagnostic indicators reported in the literature based on our continuous retrospective cohort of PJI and aseptic prosthetic loosening patients. With the indicators, we developed a two-level ML model with six base learners including Elastic Net, Linear Support Vector Machine, Kernel Support Vector Machine, Extra Trees, Light Gradient Boosting Machine and Multilayer Perceptron), and one meta-learner, Ensemble Learning of Weighted Voting. The prediction performance of this model was compared with those of previous diagnostic criteria (International Consensus Meeting in 2018 (ICM 2018), etc.). Another prospective cohort was used for internal validation. Based on our ML model, a user-friendly web tool was developed for swift PJI diagnosis in clinical practice.

Results

A total of 254 patients (199 for development and 55 for validation cohort) were included in this study with 38.2% of them diagnosed as PJI. We included 21 widely accessible features including imaging indicators (X-ray and CT) in the model. The sensitivity and accuracy of our ML model were significantly higher than ICM 2018 in development cohort (90.6% vs. 76.1%, P = 0.032; 94.5% vs. 86.7%, P = 0.020), which was supported by internal validation cohort (84.2% vs. 78.6%; 94.6% vs. 81.8%).

Conclusions

Our novel ML-derived PJI "in-time" diagnostic system demonstrated significantly improved diagnostic potency for surgical decision-making compared with the commonly used criteria. Moreover, our web-based tool greatly assisted surgeons in distinguishing PJI patients comprehensively.

Level of evidence

Diagnostic Level III.

Diagnostic value of albumin/fibrinogen ratio and C-reactive protein/albumin/globulin ratio for periprosthetic joint infection: a retrospective study

Background

The study aims to explore diagnostic value of albumin/fibrinogen ratio (AFR) and C-reactive protein (CRP)/albumin (ALB)/globulin (GLO) ratio (CAGR) for periprosthetic joint infection (PJI).

Methods

A retrospective analysis was conducted on clinical data collected from 190 patients who had joint replacement surgery in Qilu Hospital of Shandong University (Qingdao), from January 2017 to December 2022. Based on the occurrence of PJI after surgery, patients were divided as an infection group (10 cases) and non-infection group (180 cases). Diagnostic indicators were analyzed, univariate and multivariate logistic regression analyses were further performed to identify factors related to PJI. Sensitivity and specificity of AFR and CAGR, both individually and in combination, were calculated using ROC curves, and their diagnostic performance was compared based on the area under the curve (AUC).

Results

Levels of CRP, ESR, FIB, GLO, and CAGR were significantly higher in the infection group than in non-infection group (P < 0.05). Levels of ALB and AFR were significantly lower in infection group (P < 0.05). Multivariate logistic regression analysis reviewed that CRP (OR = 3.324), ESR (OR = 2.118), FIB (OR = 3.142), ALB (OR = 0.449), GLO (OR = 1.985), AFR (OR = 0.587), and CAGR (OR = 2.469) were factors influencing PJI (P < 0.05). The AUC for AFR and CAGR in diagnosing PJI were 0.739 and 0.780, while AUC for their combined detection was 0.858.

Conclusion

Abnormal levels of AFR and CAGR are associated with PJI, and their combined use has certain diagnostic value for PJI.

Accuracy of Albumin, Globulin, and Albumin-Globulin Ratio for Diagnosing Periprosthetic Joint Infection: A Systematic Review and Meta-Analysis

Periprosthetic joint infection (PJI) is one of the most intractable orthopedic diseases, partly because of the difficulty in differentiating septic from aseptic conditions. We aimed to evaluate and consolidate the diagnostic accuracy of the quantitative assessment of serum albumin (Alb), globulin (Glb), and albumin-globulin ratio (AGR), alone or in combination with the inflammatory marker, C-reactive protein (CRP), for PJI. We searched the PubMed, CINAHL, and Cochrane Library databases for studies that quantitatively measured Alb, Glb, or AGR for the diagnosis of PJI up until the 30 April 2023. A total of 2339 patients were included from 10 studies, including 845 patients with a definitive diagnosis of PJI and 1494 with non-PJI. The pooled sensitivity, specificity, and area under the curve (AUC) in the summary receiver-operating characteristic curve were as follows: 0.625, 0.732, and 0.715 for Alb; 0.815, 0.857, and 0.887 for Glb; 0.753, 0.757, and 0.875 for AGR; 0.788, 0.837, and 0.876 for CRP; 0.879, 0.890, and 0.917 for the CRP-Alb ratio; and 0.845, 0.855, and 0.908 for the CRP-AGR ratio. Serum Alb, Glb, and AGR levels are feasible and accurate diagnostic markers for PJI, and the combination of these markers with CRP levels may potentially improve preoperative serum diagnostic accuracy. Future prospective studies are required to verify these findings because of the small numbers of included studies.

Fibrinogen, albumin-to-globulin ratio, and fibrinogen to albumin-to-globulin ratio may be potential diagnostic biomarkers for infected tibial nonunion

AIM

The accurate preoperative diagnosis of infected tibial nonunion remains challenging. Hence, we evaluated the diagnostic potential of novel biomarkers for infected tibial nonunion.

METHODS

This single-center retrospective study was conducted in 252 patients divided into two groups: infected tibial nonunion (67 patients) and aseptic tibial nonunion (185 patients). The preoperative clinical biomarkers included D-dimer, fibrinogen, albumin, globulin, total protein, and C-reactive protein (CRP) levels; albumin-to-globulin ratio (AGR); erythrocyte sedimentation rate (ESR); and white blood cell (WBC) count. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were utilized to compare the biomarkers' diagnostic potential.

RESULTS

The area under the curve (AUC) values for fibrinogen and AGR were 0.829 and 0.821, respectively, suggesting similarly good diagnostic potentials for infected tibial nonunion. Fibrinogen and AGR were better diagnostic biomarkers for infected tibial nonunion than the WBC count; ESR; D-dimer, albumin, globulin, CRP, and total protein levels, whose AUC values were 0.623, 0.684, 0.741, 0.797, 0.765, 0.715, and 0.554, respectively. The sensitivity and specificity of fibrinogen with a cut-off value of 3.35 g/L were 71.64% and 84.86%, respectively. The corresponding values for AGR with a cut-off value of 1.33 were 73.13% and 86.49%. Moreover, the fibrinogen-AGR (FAGR), i.e., the combination of fibrinogen and AGR, had the highest diagnostic accuracy for infected tibial nonunion (AUC = 0.906). The optimal FAGR cut-off was 2.69, with fair sensitivity (74.63%) but the highest specificity (94.59%).

CONCLUSION

Fibrinogen, AGR, and FAGR are promising biomarkers for the diagnosis of infected tibial nonunion.

Diagnosis of Periprosthetic Joint Infection: The Utility of Biomarkers in 2023

Periprosthetic joint infection (PJI) is a rare yet devastating complication following total joint arthroplasty (TJA). Early and accurate diagnosis of PJI is paramount in order to maximize the chances of successful treatment. However, we are yet to identify a single "gold standard" test for the diagnosis of PJI. As a result, the diagnosis of PJI is often challenging. Currently, the 2018 ICM definition of PJI is the only validated diagnostic criteria available. This article will review the importance of serum and synovial biomarkers in the diagnosis of PJI. In addition, it will provide a brief overview of the emerging modalities for the identification of infections in this setting.

C-Reactive Protein-to-Albumin Ratio (CAR) and C-Reactive Protein-to-Lymphocyte Ratio (CLR) are Valuable Inflammatory Biomarker Combination for the Accurate Prediction of Periprosthetic Joint Infection

Background

Periprosthetic joint infection (PJI) is a catastrophic complication after total joint arthroplasty (TJA). Timely and accurate diagnosis is important for the management of PJI. Currently, many biomarkers are available for the diagnosis of PJI, but which inflammatory biomarker combination has the best diagnostic value has not been reported.

Materials and Methods

We retrospectively analyzed 244 patients who underwent revision knee or hip arthroplasty in our institution. They were divided into two groups: 87 in the PJI group and 157 in the aseptic failure (AF) group. The preoperative C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), CRP-to-albumin ratio (CAR), CRP-to-lymphocyte ratio (CLR), neutrophil-to-albumin ratio (NAR) and platelet-to-albumin ratio (PAR) were determined and compared between the two groups. Receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to assess the diagnostic value of all biomarkers, and the optimal cut-off value, positive predictive value (PPV) and negative predictive value (NPV) were further calculated by the Youden index.

Results

The NLR, PLR, CAR, CLR, NAR and PAR of the PJI group were significantly higher than those of the AF group (P<0.001). According to the ROC and AUC results, the diagnostic value of CAR and CLR was considered excellent with AUCs of 0.931 and 0.935, respectively. The diagnostic value of NAR (0.739) and PAR (0.785) were fair, the diagnostic value of NLR (0.694) was poor, and PLR (0.535) had no diagnostic ability. Subgroup analysis showed no significant differences in combined inflammatory biomarkers between the two groups.

Conclusion

CAR and CLR are valuable combined inflammatory biomarkers for diagnosing PJI, while other markers were of limited value for the diagnosis of PJI.

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

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.