Polymerase Chain Reaction on Respiratory Tract Specimens of Immunocompromised Patients to Diagnose Pneumocystis Pneumonia: A Systematic Review and Meta-analysis.

BACKGROUND: This meta-analysis examines the comparative diagnostic performance of polymerase chain reaction (PCR) for the diagnosis of Pneumocystis pneumonia (PCP) on different respiratory tract samples, in both human immunodeficiency virus (HIV) and non-HIV populations. METHODS: A total of 55 articles met inclusion criteria, including 11 434 PCR assays on respiratory specimens from 7835 patients at risk of PCP. QUADAS-2 tool indicated low risk of bias across all studies. Using a bivariate and random-effects meta-regression analysis, the diagnostic performance of PCR against the European Organisation for Research and Treatment of Cancer-Mycoses Study Group definition of proven PCP was examined. RESULTS: Quantitative PCR (qPCR) on bronchoalveolar lavage fluid provided the highest pooled sensitivity of 98.7% (95% confidence interval [CI], 96.8%-99.5%), adequate specificity of 89.3% (95% CI, 84.4%-92.7%), negative likelihood ratio (LR-) of 0.014, and positive likelihood ratio (LR+) of 9.19. qPCR on induced sputum provided similarly high sensitivity of 99.0% (95% CI, 94.4%-99.3%) but a reduced specificity of 81.5% (95% CI, 72.1%-88.3%), LR- of 0.024, and LR+ of 5.30. qPCR on upper respiratory tract samples provided lower sensitivity of 89.2% (95% CI, 71.0%-96.5%), high specificity of 90.5% (95% CI, 80.9%-95.5%), LR- of 0.120, and LR+ of 9.34. There was no significant difference in sensitivity and specificity of PCR according to HIV status of patients. CONCLUSIONS: On deeper respiratory tract specimens, PCR negativity can be used to confidently exclude PCP, but PCR positivity will likely require clinical interpretation to distinguish between colonization and active infection, partially dependent on the strength of the PCR signal (indicative of fungal burden), the specimen type, and patient population tested.

Universal digital high-resolution melting for the detection of pulmonary mold infections.

Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM's speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes. IMPORTANCE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

Age difference of patients with and without invasive aspergillosis: a systematic review and meta-analysis.

BACKGROUND: Invasive Aspergillosis (IA) is a life-threatening fungal disease with significant mortality rates. Timely diagnosis and treatment greatly enhance patient outcomes. This study aimed to explore the association between patient age and the development of IA, as well as the potential implications for risk stratification strategies. METHODS: We searched National Center for Biotechnology Information (NCBI) databases for publications until October 2023 containing age characteristics of patients with and without IA. A random-effects model with the application of inverse-variance weighting was used to pool reported estimates from each study, and meta-regression and subgroup analyses were utilized to assess sources of heterogeneity. RESULTS: A systematic review was conducted, resulting in the inclusion of 55 retrospective observational studies with a total of 13,983 patients. Meta-analysis revealed that, on average, patients with IA were approximately two and a half years older (95% Confidence Interval [CI] 1.84-3.31 years; I2 = 26.1%) than those without the disease (p < 0.0001). No significant moderators could explain the observed heterogeneity in age difference. However, subgroup analysis revealed that age differences were more pronounced within particular patient groups compared to others. For example, patients with and without IA who had primary severe lung infections exhibited a greater difference in mean age than other patient cohorts. CONCLUSIONS: Further research, such as individual patient data meta-analysis, is necessary to better understand the potential relationship between increasing age and the likelihood of IA. Improved risk stratification strategies based on patient age could potentially enhance the early detection and treatment of IA, ultimately improving patient outcomes.

The Impact of the Fungal Priority Pathogens List on Medical Mycology: A Northern European Perspective.

Fungal diseases represent a considerable global health concern, affecting >1 billion people annually. In response to this growing challenge, the World Health Organization introduced the pivotal fungal priority pathogens list (FPPL) in late 2022. The FPPL highlights the challenges in estimating the global burden of fungal diseases and antifungal resistance (AFR), as well as limited surveillance capabilities and lack of routine AFR testing. Furthermore, training programs should incorporate sufficient information on fungal diseases, necessitating global advocacy to educate health care professionals and scientists. Established international guidelines and the FPPL are vital in strengthening local guidance on tackling fungal diseases. Future iterations of the FPPL have the potential to refine the list further, addressing its limitations and advancing our collective ability to combat fungal diseases effectively. Napp Pharmaceuticals Limited (Mundipharma UK) organized a workshop with key experts from Northern Europe to discuss the impact of the FPPL on regional clinical practice.