Platelet-Derived Biomarkers: Potential Role in Early Pediatric Serious Bacterial Infection and Sepsis Diagnostics

Biomarkers for Serious Bacterial Infections in Febrile Children

Febrile infections in children are a common cause of presentation to the emergency department (ED). While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; nowadays, a better understanding of immunochemical pathways has led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models, combining them for serious bacterial infection (SBI) diagnosis in febrile children. Literature analysis shows that inflammatory response is a complex mechanism in which many biochemical and immunological factors contribute to the host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined, and for this reason, it is reasonable to take them both into consideration in the evaluation of febrile children. The potential of machine learning tools, which represent a real novelty in medical practice, in conjunction with routine clinical and biological information, may improve the accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pediatric precision medicine still uncertain. More data from further research are needed to improve clinical practice and decision making using these new technologies.

Limited value of platelet-related markers in diagnosing periprosthetic joint infection

OBJECTIVE

To evaluate the diagnostic values of serum platelet count (PC), mean platelet volume ratio (MPV), platelet count to mean platelet volume ratio (PVR), platelet to lymphocyte ratio (PLR), platelet to neutrophil ratio (PNR), PC/Albumin-globulin ratio (PC/AGR), and PC/C-reactive protein (PC/ CRP) in the diagnosis of periprosthetic joint infection (PJI).

METHODS

The medical records were retrospectively analyzed of the 158 patients who had undergone hip or knee revisions from January 2018 to May 2022. Of them, 79 cases were diagnosed with PJI and 79 with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of CRP, the erythrocyte sedimentation rate (ESR), PC, MPV, PVR, PLR, PNR, PC/AGR, and PC/CRP in the 2 groups were recorded and analyzed. In addition, tests were performed according to different joint types. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator. The diagnostic value for each indicator was calculated according to the area under the curve (AUC).

RESULTS

The PC, PVR, PLR and PC/AGR levels in the PJI group were significantly higher than those in the AL group, while PC/CRP levels were significantly lower (P < 0.001). The AUC for PC/CRP, and PC/AGR was 0.804 and 0.802, respectively, which were slightly lower than that of CRP (0.826) and ESR (0.846). ROC analysis for PC/CRP, and PC/AGR revealed a cut-off value of 37.80 and 160.63, respectively, which provided a sensitivity of 73.42% and 84.81% and a specificity of 75.95% and 65.82% for PJI. The area under the curve of PLR and PC was 0.738 and 0.702. The area under the curve values for PVR, PNR, and MPV were 0.672, 0.553, and 0.544, respectively.

CONCLUSIONS

The results of this study suggest that PC, PLR, PC/CRP, and PC/AGR values do not offer significant advantages over ESR or CRP values when employed for the diagnosis of PJI. PVR, PNR, and MPV were not reliable in the diagnosis of PJI.

Prognostic potential of inflammatory markers, oxidative status, thrombocyte indices, and renal biochemical markers in neonatal calf diarrhoea-induced systemic inflammatory response syndrome

The study aimed to assess the prognostic value of inflammatory markers, indicators of oxidative stress, thrombocyte indices, and renal biochemical markers in neonatal calf diarrhoea (NCD) induced by systemic inflammatory response syndrome (SIRS) upon admission. A prospective, observational, and case-control study was conducted on 56 calves diagnosed with NCD. Mean concentrations of interleukin-6 (IL-6), malondialdehyde (MDA), glutathione (GSH), mean platelet volume (MPV), platelet distribution width (PDW), blood urea nitrogen (BUN), and creatinine (Crea) were measured. Furthermore, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were also calculated for SIRS survivors [SIRS (survivor)] and non-survivors [SIRS (non-survivor)] induced by NCD. A prognostic cut-off value for predicting the prognosis of the SIRS's induced by NCD was obtained via receiver operating characteristic (ROC) curve analysis. Upon admission, the SIRS (non-survivor) calves had significantly higher (P < .001) average levels of IL-6, MDA, BUN, Crea, MPV, and PDW compared to the SIRS (survivor) calves and significantly lower (P < .001) average levels of GSH. Despite an apparent increase in the NLR and PLR values of calves diagnosed with SIRS, no significant difference was found between the survival and non-survivor SIRS cases. Positive predictive values (PPVs) for survival were determined as 100 %, 100 %, 80 %, 100 %, 80 %, and 80 %, respectively, using cut-off values of IL-6 (≤259.67 ng/L), MDA (≤2.87 nmol/mL), MPV (≤12.5 fL), PDW (≤34.25 %), BUN (≤168.3 mg/dL), and Crea (≤2.11 mg/dL). The determined threshold values are those obtained upon admission to the hospital. Based on the sensitivity, specificity, and PPVs derived from the ROC analysis, it has been concluded that IL-6, MDA, MPV, PDW, BUN, and Crea are the most relevant biomarkers used for predicting the prognosis of NCD-induced SIRS in calves. Furthermore, it is also noteworthy that IL-6 exhibited the highest effectiveness among all biomarkers.

A predictive model for the identification of the risk of sepsis in patients with Gram-positive bacteria in the intensive care unit

Background

Gram-positive bacterial infections are very common in the intensive care unit (ICU) and may lead to sepsis. However, there are no models to predict the risk of sepsis in persons with Gram-positive bacterial infections. Therefore, the purpose of this study was to create and validate a nomogram for predicting the risk of sepsis in patients with common gram-positive bacterial infections.

Methods

Patients infected with three common Gram-positive bacteria who were admitted to the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC IV) database were included in this retrospective cohort study. A Cox regression model was used to develop a nomogram for predicting 3-day, 1-week, 2-week, and 1-month sepsis probability. The performance of the nomogram was analyzed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves.

Results

In total, 19,961 eligible patients were enrolled from MIMIC IV datasets. All participants were allocated to training and validation cohorts at random in a 7:3 ratio. The use of more than 3 types of antibiotics, dementia, ethnicity, aspartate aminotransferase (AST), neutrophils, the use of antifungal drug, ventilation and need for vasopressors were all discovered to be highly correlated with enhanced probability of sepsis in patients with Gram-positive bacteria. A prediction nomogram was constructed using these 8 predictors. The area under the curve (AUC) for predicting 3-day, 1-week, 2-week, and 1-month sepsis risk in the training cohort was 0.857, 0.774, 0.740, and 0.728, respectively, and that in the validation cohort was 0.855, 0.781, 0.742, and 0.742, respectively. The predictive power of our model is better than the SOFA score. The model had good predictive performance in all three classes of Gram-positive bacteria. Based on the calibration and clinical decision curves, the nomogram correctly predicted sepsis in patients with Gram-positive bacteria.

Conclusions

We were able to build a nomogram to predict the probability of sepsis in patients with Gram-positive bacteria, particularly those infected with Streptococcus spp. and Staphylococcus spp. This model performs effectively, and it might be used clinically to manage patients with Gram-positive bacteria.

Platelet role in the prediction of MIS-C severity

Introduction

Multisystem inflammatory syndrome in children (MIS-C) has been reported as one of the cytokine storm syndromes associated with COVID-19. Despite the several proposed diagnostic criteria, MIS-C remains a diagnostic and clinical challenge. Recent studies have demonstrated that platelets (PLTs) play a crucial role in COVID-19 infection and its prognosis. This study aimed to investigate the clinical importance of PLT count and PLT indices in predicting MIS-C severity in children.

Patients and methods

We conducted a retrospective single-center study at our university hospital. A total of 43 patients diagnosed with MIS-C during a 2-year period (from October 2020 to October 2022) were included in the study. MIS-C severity was evaluated according to the composite severity score.

Results

Half of the patients were treated in the pediatric intensive care unit. No single clinical sign was associated with a severe condition, except for shock (p = 0.041). All the routine biomarkers, such as complete blood count (CBC) and C-reactive protein (CRP), used for MIS-C diagnosis were significant in predicting MIS-C severity. Single PLT parameters, such as mean PLT volume, plateletcrit, or PLT distribution width, did not differ between the severity groups. However, we found that a combination of PLT count and the previously mentioned PLT indices had the potential to predict MIS-C severity.

Conclusions

Our study emphasizes the importance of PLT in MIS-C pathogenesis and severity. It revealed that together with routine biomarkers (e.g., CBC and CRP), it could highly improve the prediction of MIS-C severity.