Procalcitonin to Detect Bacterial Infections in Critically Ill Pediatric Patients

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.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission

BACKGROUND AND OBJECTIVES

Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics.

METHODS

We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model.

RESULTS

A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient.

CONCLUSIONS

Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.

The detection of bacterial infections in leukemia patients using procalcitionin levels

Bacteremic infections are the most common complications in patients with leukemia. This study aimed to assess the value of procalctionin levels in the detection of bacterial infections in leukemia patients. Blood samples of in-patients with leukemia were collected. Statistical analysis was performed to assess the correlation between the procalcitionin level on the day of the first positive blood culture and bacteremic infection. Infected patients had significantly higher procalctionin levels than uninfected patients (p < 0.001). Receiver operating characteristic curve analysis showed a high level of accuracy regarding the discrimination of bacterarmic infection (area under the curve, 0.883) and Gram-negative bateremia (area under the curve, 0.779). Procalctionin levels may help in the identification of bacterial infections in leukemia patients. Further multicentre studies are needed to verify our data regarding the use of procalctionin to distinguish between Gram-positive and Gram-negative infections.

Procalcitonin Value Is an Early Prognostic Factor Related to Mortality in Admission to Pediatric Intensive Care Unit

Introduction

Procalcitonin (PCT) and C-reactive protein (CRP) are already known predictive markers in serious bacterial infections, and it is emphasized that these biomarkers can be used as a marker of increased mortality in critically ill patients. Herein, we aimed to evaluate the initial serum PCT and CRP levels on the outcome of patients in pediatric intensive care units (PICUs) and find out if these biomarkers can be used to predict mortality.

Materials and Methods

The relationship between the initial serum PCT and CRP levels and invasive mechanical ventilation (IMV) and noninvasive mechanical ventilation (NIV) support, inotropic drug need, acute renal kidney injury (AKI), continuous renal replacement therapy (CRRT), mortality, and hospitalization period was investigated retrospectively.

Results

In total, 418 suitable patients (226 males and 192 females) were included in the study. Age distributions of patients ranged from 1 month to 17 years. There was a statistically significant relationship between PCT levels in the first biochemical analysis performed during admission and MV support, inotropic drug use, mortality, ARF, hospitalization in the intensive care unit, CRRT and blood component transfusion. There was a statistically significant relationship between CRP levels and MV support, NIV, inotropic drug use, mortality, AKI, hospitalization in the intensive care unit, CRRT, and blood component transfusion.

Conclusion

We suggest that the initial PCT and CRP levels during admission can be used to predict the outcome of patients in PICU.

Usefulness of the procalcitonin test in young febrile infants between 1 and 3 months of age

Purpose

To study the usefulness of the procalcitonin (PCT) test in young febrile infants between 1 and 3 months of age.

Methods

We evaluated the medical records of 336 febrile infants between 1 and 3 months of age who visited the Emergency Department or outpatient department of Samsung Changwon Hospital from May 2015 to February 2017, and analyzed the clinical characteristics between infants in the serious bacterial infection (SBI) group and non-SBI group.

Results

Among the 336 infants, 38 (11.3%) had definitive SBI (bacteremia, n=3; meningitis, n=1; urinary tract infection, n=34). The mean PCT (6.4±11.9 ng/mL) and C-reactive protein (CRP) level (3.8±2.6 mg/dL), and the absolute neutrophil count (ANC) (6,984±4,675) for patients in the SBI group were significantly higher than those for patients in the non-SBI group (PCT, 0.3±1.2 ng/mL; CRP, 1.3±1.6 mg/dL; ANC, 4,888±3,661). PCT had lower sensitivity (43.6%), but higher specificity (92.6%) and accuracy (86.9%) than CRP (92.3%, 25.3%, and 33.0%) for identifying SBI. The area under the receiver operating characteristic curves (AUCs) for definitive SBI were PCT 77.0%, CRP 80.8%, WBC 56.8%, ANC 67.8%, and PLT 48.1%. The AUCs for definitive SBI were PCT+CRP 85.4%, PCT+WBC 77.2%, PCT+ANC 81.3%, CRP+WBC 80.1%, and CRP+ANC 81.6%.

Conclusion

Our results suggest that the PCT test or a combination of PCT and CRP tests is a more accurate and specific biomarker to detect and rule out SBIs.