Multicenter Prospective Study of Biomarkers for Diagnosis of Invasive Candidiasis in Children and Adolescents

Neonatal Infectious Disease: A Major Contributor to Infant Mortality Requiring Advances in Point-of-Care Diagnosis

Neonatal infectious disease continues to result in high rates of infant morbidity and mortality. Early- and late-onset disease represent difficult to detect and difficult to treat illnesses, particularly when antimicrobial resistant pathogens are present. Newborns are immunodeficient and are at increased risk of vertical and horizontal infection, with preterm infants increasingly susceptible. Additional risk factors associated with infection include prolonged use of a central catheter and/or ventilation, congenital abnormalities, admittance to intensive care units, and the use of broad-spectrum antibiotics. There is increasing recognition of the importance of the host microbiome and dysbiosis on neonatal infectious disease, including necrotising enterocolitis and sepsis in patients. Current diagnostic methods rely on blood culture, which is unreliable, time consuming, and can result in false negatives. There is a lack of accurate and reliable diagnostic tools available for the early detection of infectious disease in infants; therefore, efficient triage and treatment remains challenging. The application of biomarkers, machine learning, artificial intelligence, biosensors, and microfluidics technology, may offer improved diagnostic methodologies. Point-of-care devices, such diagnostic methodologies, may provide fast, reliable, and accurate diagnostic aids for neonatal patients. This review will discuss neonatal infectious disease as impacted by antimicrobial resistance and will highlight novel point-of-care diagnostic options.

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application

Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1 people's hospital in China from Jan. 2015 to Dec. 2022. To ensure the model's reliability, we validated this model with an external group consisting of 77 patients from other sources. The candidemia to bacteremia ratio is 1:1. We collected relevant clinical procedures and eighteen key examinations or tests features to support the recursive feature elimination (RFE) algorithm. These features included total bilirubin, age, platelet count, hemoglobin, CVC, lymphocyte, Duration of stay in ICU and so on. To construct the candidemia diagnosis model, we employed random forest (RF) algorithm alongside other machine learning methods and conducted internal and external validation with training and testing sets allocated in a 7:3 ratio. The RF model demonstrated the highest area under the receiver operating characteristic (AUC) with values of 0.87 and 0.83 for internal and external validation, respectively. To evaluate the importance of features in predicting candidemia, Shapley additive explanation (SHAP) values were calculated and results revealed that total bilirubin and age were the most important factors in the prediction model. This advancement in candidemia prediction holds significant promise for early intervention and improved patient outcomes in the ICU setting, where timely diagnosis is of paramount crucial.

An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections

BACKGROUND

Invasive fungal infections cause millions of infections annually, but diagnosis remains challenging. There is an increased need for low-cost, easy to use, highly sensitive and specific molecular assays that can differentiate between colonized and pathogenic organisms from different clinical specimens.

AREAS COVERED

We reviewed the literature evaluating the current state of molecular diagnostics for invasive fungal infections, focusing on current and novel molecular tests such as polymerase chain reaction (PCR), digital PCR, high-resolution melt (HRM), and metagenomics/next generation sequencing (mNGS).

EXPERT OPINION

PCR is highly sensitive and specific, although performance can be impacted by prior/concurrent antifungal use. PCR assays can identify mutations associated with antifungal resistance, non-Aspergillus mold infections, and infections from endemic fungi. HRM is a rapid and highly sensitive diagnostic modality that can identify a wide range of fungal pathogens, including down to the species level, but multiplex assays are limited and HRM is currently unavailable in most healthcare settings, although universal HRM is working to overcome this limitation. mNGS offers a promising approach for rapid and hypothesis-free diagnosis of a wide range of fungal pathogens, although some drawbacks include limited access, variable performance across platforms, the expertise and costs associated with this method, and long turnaround times in real-world settings.

American Society for Transplantation and Cellular Therapy Series, #6: Management of Invasive Candidiasis in Hematopoietic Cell Transplantation Recipients

The Practice Guidelines Committee of the American Society of Transplantation and Cellular Therapy (ASTCT) partnered with its Transplant Infectious Disease Special Interest Group (TID-SIG) to update its 2009 compendium-style infectious disease guidelines for hematopoietic cell transplantation (HCT). A completely new approach was taken with the goal of better serving clinical providers by publishing each standalone topic in the infectious disease series as a concise format of frequently asked questions (FAQ), tables, and figures. Adult and pediatric infectious disease and HCT content experts developed and then answered FAQs and finalized topics with harmonized recommendations made by assigning an A through E strength of recommendation paired with a level of supporting evidence graded I through III. This sixth guideline in the series focuses on invasive candidiasis (IC) with FAQs to address epidemiology, clinical diagnosis, prophylaxis, and treatment of IC, plus special considerations for pediatric, cord blood, haploidentical, and T cell-depleted HCT recipients and chimeric antigen receptor T cell recipients, as well as future research directions.

Approach to the diagnosis of invasive fungal infections of the respiratory tract in the immunocompromised host

PURPOSE OF REVIEW

The burden of invasive fungal infection is increasing worldwide, largely due to a growing population at-risk. Most serious human fungal pathogens enter the host via the respiratory tract. Early identification and treatment of invasive fungal respiratory infections (IFRIs) in the immunocompromised host saves lives. However, their accurate diagnosis is a difficult challenge for clinicians and mortality remains high.

RECENT FINDINGS

This article reviews IFRIs, focussing on host susceptibility factors, clinical presentation, and mycological diagnosis. Several new diagnostic tools are coming of age including molecular diagnostics and point-of-care antigen tests. As diagnosis of IFRI relies heavily on invasive procedures like bronchoalveolar lavage and lung biopsy, several novel noninvasive diagnostic techniques are in development, such as metagenomics, 'volatilomics' and advanced imaging technologies.

SUMMARY

Where IFRI cannot be proven, clinicians must employ a 'weights-of-evidence' approach to evaluate host factors, clinical and mycological data. Implementation studies are needed to understand how new diagnostic tools can be best applied within clinical pathways. Differentiating invasive infection from colonization and identifying antifungal resistance remain key challenges. As our diagnostic arsenal expands, centralized clinical mycology laboratories and efforts to ensure access to new diagnostics in low-resource settings will become increasingly important.

Quantitatively detecting Candida albicans enolase1 with a one-step double monoclonal antibody sandwich ELISA assay

Invasive candidiasis (IC) is often a cause of severe concern for the hospitalized patients, particularly those who are critically sick. However management of this disease is challenging due to a lack of effective laboratory diagnostic techniques. Hence, we have developed a one-step double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) using a pair of specific monoclonal antibodies (mAbs) for the quantitative detection of Candida albicans enolase1 (CaEno1), which is considered as an important diagnostic biomarker for IC. The diagnostic efficiency of the DAS-ELISA was evaluated by using a rabbit model of systemic candidiasis and compared with other assays. The method validation results demonstrated that the developed method was sensitive, reliable, and feasible. The findings of the rabbit model plasma analysis indicated that the diagnostic efficiency of the CaEno1 detection assay was better in comparison to the (1,3)-β-D-glucan detection and blood culture. CaEno1 is present in the blood of infected rabbits for a brief period and at relatively low levels and thus the combination of CaEno1 antigen and IgG antibodies detection could aid to increase diagnostic efficiency. However, to improve the clinical application of CaEno1 detection in the future, efforts should be made to increase the detection limit of the test by promoting technical developments and by optimizing the protocol for the clinical serial determinations.

Invasive Candidiasis in Children: Challenges Remain

How to cite this article: Baalaaji M. Invasive Candidiasis in Children: Challenges Remain. Indian J Crit Care Med 2022;26(6):667-668.