Clinical utility of procalcitonin and its association with pathogenic microorganisms

Hematologic cancers and infections: how to detect infections in advance and determine the type?

Infection is one of the leading causes of death in patients with hematologic cancers. Hematologic cancer patients with compromised immune systems are already susceptible to infections, which come on even more rapidly and are difficult to control after they develop neutrophil deficiencies from high-dose chemotherapy. After patients have developed an infection, the determination of the type of infection becomes a priority for clinicians. In this review, we summarize the biomarkers currently used for the prediction of infections in patients with hematologic cancers; procalcitonin, CD64, cytokines, and CD14 et al. can be used to determine bacterial infections, and (1-3)-β-D-glucan and galactomannan et al. can be used as a determination of fungal infections. We have also focused on the use of metagenomic next-generation sequencing in infections in patients with hematologic cancers, which has excellent clinical value in infection prediction and can detect microorganisms that cannot be detected by conventional testing methods such as blood cultures. Of course, we also focused on infection biomarkers that are not yet used in blood cancer patients but could be used as a future research direction, e.g., human neutrophil lipocalin, serum amyloid A, and heparin-binding protein et al. Finally, clinicians need to combine multiple infection biomarkers, the patient's clinical condition, local susceptibility to the type of infection, and many other factors to make a determination of the type of infection.

Sepsis Biomarkers: Advancements and Clinical Applications-A Narrative Review

Sepsis is now defined as a life-threatening syndrome of organ dysfunction triggered by a dysregulated host response to infection, posing significant challenges in critical care. The main objective of this review is to evaluate the potential of emerging biomarkers for early diagnosis and accurate prognosis in sepsis management, which are pivotal for enhancing patient outcomes. Despite advances in supportive care, traditional biomarkers like C-reactive protein and procalcitonin have limitations, and recent studies have identified novel biomarkers with increased sensitivity and specificity, including circular RNAs, HOXA distal transcript antisense RNA, microRNA-486-5p, protein C, triiodothyronine, and prokineticin 2. These emerging biomarkers hold promising potential for the early detection and prognostication of sepsis. They play a crucial role not only in diagnosis but also in guiding antibiotic therapy and evaluating treatment effectiveness. The introduction of point-of-care testing technologies has brought about a paradigm shift in biomarker application, enabling swift and real-time patient evaluation. Despite these advancements, challenges persist, notably concerning biomarker variability and the lack of standardized thresholds. This review summarizes the latest advancements in sepsis biomarker research, spotlighting the progress and clinical implications. It emphasizes the significance of multi-biomarker strategies and the feasibility of personalized medicine in sepsis management. Further verification of biomarkers on a large scale and their integration into clinical practice are advocated to maximize their efficacy in future sepsis treatment.

Hospital Mortality and Morbidity in Diabetic Patients with COVID-19: A Retrospective Analysis from the UAE

BACKGROUND

Although we are four years into the pandemic, there is still conflicting evidence regarding the clinical outcomes of diabetic patients hospitalized with COVID-19. The primary objective of this study was to evaluate the in-hospital mortality and morbidity of diabetic versus nondiabetic patients hospitalized with COVID-19 in the Northern UAE Emirates.

METHODS

A retrospective analysis was performed on clinical data from patients with or without diabetes mellitus (DM) who were admitted to the isolation hospital with COVID-19 during the first and second waves of the disease (March 2020 to April 2021). The assessed endpoints were all-cause in-hospital mortality, length of hospitalization, intensive care unit (ICU) admission, and mechanical ventilation.

RESULTS

A total of 427 patients were included in the analysis, of whom 335 (78.5%) had DM. Compared to nondiabetics, diabetic COVID-19 patients had a significantly longer in-hospital stay (odds ratio (OR) = 2.35; 95% confidence interval (CI) = 1.19-4.62; p = 0.014), and a significantly higher frequency of ICU admission (OR = 4.50; 95% CI = 1.66-7.34; p = 0.002). The need for mechanical ventilation was not significantly different between the two groups (OR: distorted estimates; p = 0.996). Importantly, the overall in-hospital mortality was significantly higher among diabetic patients compared to their nondiabetic counterparts (OR = 2.26; 95% CI = 1.08-4.73; p = 0.03).

CONCLUSION

DM was associated with a more arduous course of COVID-19, including a higher mortality rate, a longer overall hospital stay, and a higher frequency of ICU admission. Our results highlight the importance of DM control in COVID-19 patients to minimize the risk of detrimental clinical outcomes.

Improved diagnostic markers for invasive pulmonary aspergillosis in COPD patients

Background

The prevalence of invasive pulmonary aspergillosis (IPA) among patients with chronic obstructive pulmonary disease (COPD) is steadily increasing, leading to high mortality. Although early diagnosis can significantly reduce mortality, the efficacy of current diagnostic methods is limited. Consequently, there is a need for novel approaches for early IPA detection.

Methods

This retrospective study involved 383 hospitalized COPD patients with GOLD stages III and IV. The IPA group (67 patients) and non-IPA group (316 patients) were identified at the First Affiliated Hospital of Guangzhou Medical University between January 2016 and February 2022. We analyzed common serological indicators in our hospital to identify predictive indicators for the early diagnosis of IPA in COPD patients.

Results

The sensitivity and specificity of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), lactate dehydrogenase (LDH), and ceruloplasmin (CER) for diagnosing IPA in COPD patients were as follows: CRP (91.2%, 57.7%), ESR (77.5%, 73.0%), PCT (60.5%, 71.4%), LDH (50.0%, 88.8%), and CER (60.7%, 74.3%). Combinations of biomarkers, such as CRP-ESR, CRP-LDH, ESR-LDH, ESR-CER, and LDH-CER, showed promising diagnostic potential, with larger area under the curve (AUC) values for IPA diagnosis in COPD patients. However, no statistically significant difference was observed between the diagnostic efficacy of single biomarkers and combined biomarkers. Notably, compared to those in the unassisted ventilation group, the patients in the assisted ventilation group (including noninvasive ventilation and tracheal intubation/incision-assisted ventilation group) exhibited significantly greater PCT and LDH levels, while the CER significantly decreased (p=0.021). There were no significant differences in biomarker levels between the ICU group and the non-ICU group. CRP (p<0.01), ESR (p=0.028), PCT (p<0.01), and CER (p<0.01) were positively correlated with hospitalization duration, whereas LDH was not correlated with hospitalization duration.

Conclusion

Our study highlights the diagnostic potential of CRP, ESR, PCT, LDH, and CER for IPA in COPD patients. CRP and LDH can also initially predict the need for assisted ventilation, while CRP can initially estimate the length of hospitalization. This study represents the first report of the potential of CER for diagnosing IPA, suggesting its significance for further research.

Ultrasensitive and Rapid Detection of Procalcitonin via Waveguide-Enhanced Nanogold-Linked Immunosorbent Assay for Early Sepsis Diagnosis

Sepsis, a life-threatening inflammatory response, demands economical, accurate, and rapid detection of biomarkers during the critical "golden hour" to reduce the patient mortality rate. Here, we demonstrate a cost-effective waveguide-enhanced nanogold-linked immunosorbent assay (WENLISA) based on nanoplasmonic waveguide biosensors for the rapid and sensitive detection of procalcitonin (PCT), a sepsis-related inflammatory biomarker. To enhance the limit of detection (LOD), we employed sandwich assays using immobilized capture antibodies and detection antibodies conjugated to gold nanoparticles to bind the target analyte, leading to a significant evanescent wave redistribution and strong nanoplasmonic absorption near the waveguide surface. Experimentally, we detected PCT for a wide linear response range of 0.1 pg/mL to 1 ng/mL with a record-low LOD of 48.7 fg/mL (3.74 fM) in 8 min. Furthermore, WENLISA has successfully identified PCT levels in the blood plasma of patients with sepsis and healthy individuals, offering a promising technology for early sepsis diagnosis.

Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency

BACKGROUND

Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown.

METHODS

Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection.

RESULTS

Several metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively.

CONCLUSIONS

The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.

Monocyte distribution width (MDW) in sepsis

Sepsis is a life-threatening syndrome due to a dysregulated host response to infection, which can be caused by bacterial, viral, or fungal infection. Thus, it is crucial to know how the different microorganisms influence the levels of a biomarker. In the last decade, monocyte distribution width (MDW) has emerged as a promising sepsis biomarker, especially in acute settings, such as the Emergency Department and Intensive Care Unit. In this article, we explore the relationship between MDW and the different pathogens causing infection. Noteworthy, MDW is not a biological molecule, but it is calculated by a mathematical formula based on monocyte characteristics. Monocytes represent the first line defence against microorganisms and undergo activation upon infection, independently from the invading pathogen. According to the knowledge on the biomarker biology and the few literatures evidence, MDW may be considered a biomarker of sepsis, independent of the causative pathogen. However, further investigations are warranted before drawing definite conclusion.

Age, comorbidities, c-reactive protein and procalcitonin as predictors of severity in confirmed COVID-19 patients in the Philippines

Background

The Coronavirus Disease 2019 (COVID-19) pandemic has been affecting people globally, and the Philippines is one of the countries greatly struck by the virus. The continued rise of new positive cases has drawn attention to the urgent need for healthcare management to cope with this challenge. Severity prediction could help improve medical decision-making and optimise the patient's treatment plan with a good clinical outcome. This study aimed to identify the determinants of COVID-19 disease severity.

Methods

Demographic characteristics and laboratory findings were collected from electronic medical records and paper forms of all confirmed COVID-19 cases reported by the University of Perpetual Help DALTA Medical Center between the September 1, 2020 and the October 31, 2021. We performed statistical analyses and interpretation of data to compare severe and non-severe groups.

Results

5,396 confirmed cases were examined. Most of the severe cases were elderly, male, had blood type A, and with comorbidities. Cycle threshold (Ct) values were lower in the severe group. Most patients had higher-than-normal levels of all blood parameters except platelet, white blood cell (WBC), neutrophil, and lymphocyte counts. Age, sex, ABO blood groups, comorbidities, open reading frame 1 ab (ORF1ab) and nucleocapsid (N) gene Ct values, ferritin, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, white blood cell (WBC) count, neutrophil count, and lymphocyte count were significantly associated with disease severity. In multivariate analysis, age groups >60 and 30-59 years, presence of comorbidities, CRP level >5 ng/mL, and PCT >0.05 ng/mL were identified as disease severity predictors.

Conclusions

Based on our results, age, comorbidities, CRP, and PCT level may be utilised as primary assessment factors for possible hospital admission and close monitoring upon testing. Early detection of these risk factors may provide strategic interventions that help reduce mortality, hospital admissions, and more expensive and extensive treatments.

Biomarkers as Prognostic Predictors and Therapeutic Guide in Critically Ill Patients: Clinical Evidence

A biomarker is a molecule that can be measured in a biological sample in an objective, systematic, and precise way, whose levels indicate whether a process is normal or pathological. Knowing the most important biomarkers and their characteristics is the key to precision medicine in intensive and perioperative care. Biomarkers can be used to diagnose, in assessment of disease severity, to stratify risk, to predict and guide clinical decisions, and to guide treatments and response to them. In this review, we will analyze what characteristics a biomarker should have and how to ensure its usefulness, and we will review the biomarkers that in our opinion can make their knowledge more useful to the reader in their clinical practice, with a future perspective. These biomarkers, in our opinion, are lactate, C-Reactive Protein, Troponins T and I, Brain Natriuretic Peptides, Procalcitonin, MR-ProAdrenomedullin and BioAdrenomedullin, Neutrophil/lymphocyte ratio and lymphopenia, Proenkephalin, NefroCheck, Neutrophil gelatinase-associated lipocalin (NGAL), Interleukin 6, Urokinase-type soluble plasminogen activator receptor (suPAR), Presepsin, Pancreatic Stone Protein (PSP), and Dipeptidyl peptidase 3 (DPP3). Finally, we propose an approach to the perioperative evaluation of high-risk patients and critically ill patients in the Intensive Care Unit (ICU) based on biomarkers.

Procalcitonin and Adrenomedullin in Infectious Diseases

Calcitonin (CT) and adrenomedullin (ADM) are members of the CT family. Procalcitonin (PCT) is a prohormone of CT. Elevations in serum PCT and ADM levels are associated with severe sepsis and coronavirus disease 2019 (COVID-19). PCT enhances sepsis mortality and it binds to the CGRP receptor, which is a heterodimer of CT receptor-like receptor and receptor activity-modifying protein 1. The N-terminal truncated form of PCT, PCT3-116, is produced by the cleavage of PCT by dipeptidyl peptidase 4 (DPP-4) and is the main form of PCT in serum during sepsis, inducing microvascular permeability. Mid-regional pro-adrenomedullin (MR-proADM) is used instead of ADM as a biological indicator because ADM is rapidly degraded, and MR-proADM is released at the same rate as ADM. ADM reduces endothelial permeability and promotes endothelial stability. Endothelial dysfunction is responsible for multiple organ failure in sepsis and COVID-19 patients. Therefore, ADM may be an important molecule for improving the severity associated with sepsis and COVID-19. This review focuses on the current knowledge of PCT and ADM in sepsis and COVID-19.

Predicting bacterial infection risk in patients with ANCA-associated vasculitis in southwest China: development of a new nomogram

OBJECTIVES

The aim of this study was to develop and assess a risk nomogram of bacterial infection in patients with ANCA-associated vasculitis (AAV) in southwest China.

METHOD

We established a prediction model based on a training dataset of 249 AAV patients. The least absolute shrinkage and selection operator (Lasso) was used to screen feature variables. Multivariate logistic regression analysis was used to build a prediction model for feature variables. Nomogram was used to predict the risk of bacterial infection in AAV patients. Receiver operating characteristic (ROC) curve was used to evaluate and verify the prediction accuracy of the model. Calibration and clinical useful range was assessed using calibration curve and decision curve analysis, respectively.

RESULTS

Bactericidal permeability enhancement protein of ANCAs (BPI-ANCAs), procalcitonin (PCT), and white blood cell (WBC) were the characteristic variables in this study. Nomogram showed that positive BPI-ANCAs and PCT had higher positive predictive value for bacterial infection in AAV patients. The area under curve (AUC) of the model was 0.703 (95% confidence interval: 0.640-0.766). In the validation model, the AUC was 0.745 (95% confidence interval: 0.617-0.872). Decision curve analysis showed that the nonadherence nomogram was clinically useful within the threshold probability range of 0.31-0.85.

CONCLUSIONS

Nomogram combined with BPI-ANCAs and PCT has the guiding significance for predicting bacterial infection risk in AAV. As an ANCA-specific autoantibody, BPI-ANCAs is helpful for clinicians to understand the role of specific autoantibodies in the pathogenesis of AAV. Key Points • BPI-ANCAs, PCT, and WBC could predict bacterial infection in AAV patients. • Nomogram showed that positive BPI-ANCAs had a high positive predictive value for bacterial infection in AAV patients.