Rapid clinical diagnosis of lower respiratory infection
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PROVIDER: PRJEB39606 | ENA |
REPOSITORIES: ENA
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Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO
Project description:Interventions: Gold Standard:Expert polyp recognition results;Index test:Experimental group: lower gastrointestinal endoscopy imaging-assisted diagnosis software;
Control group: doctor identification.
Primary outcome(s): sensitivity;Specificity
Study Design: Parallel
| 2702167 | ecrin-mdr-crc
Project description:Genomic distinctions between metastatic lower and upper tract urothelial carcinoma revealed through rapid autopsy
| PRJNA528123 | ENA
Project description:Genomic distinctions between metastatic lower and upper tract urothelial carcinoma revealed through rapid autopsy
| PRJNA528124 | ENA
Project description:COBRA - clinical trial for colorectal cancer diagnosis from breath samples
2019-08-23 | MSV000084227 | MassIVE
Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI. 158 samples, no replicates; bacterial LRTI n=22, viral LRTI n=71, bacterial-viral coinfections n=25, and healthy controls n=40
2015-09-14 | E-GEOD-60244 | biostudies-arrayexpress
Project description:BackgroundNanopore metagenomics has been used for infectious disease diagnosis for bacterial pathogens. However, this technology currently lacks comprehensive performance studies in clinical settings for simultaneous detection of bacteria, fungi, and viruses.MethodsWe developed a dual-process of Nanopore sequencing for one sample, with unbiased metagenomics in Meta process and target enrichment in Panel process (Nanopore Meta-Panel process, NanoMP) and prospectively enrolled 450 respiratory specimens from multiple centers. The filter system of pathogen detection was established with machine learning and receiver operator characteristic (ROC) curve to optimize the detection accuracy based on orthogonal test of 21 species. Antimicrobial resistance (AMR) genes were identified based on the Comprehensive Antibiotic Resistance Database (CARD) and single-nucleotide polymorphism matrix.FindingsOur approach showed high sensitivity in Meta process, with 82.9%, 88.7%, and 75.0% for bacteria, fungi (except Aspergillus), and Mycobacterium tuberculosis groups, respectively. Moreover, target amplification improved the sensitivity of virus (>80.0% vs. 39.4%) and Aspergillus (81.8% vs. 42.3%) groups in Panel process compared with Meta process. Overall, NanoMP achieved 80.2% sensitivity and 98.8% specificity compared with the composite reference standard, and we were able to accurately detect AMR genes including blaKPC-2, blaOXA-23 and mecA and distinguish their parent organisms in patients with mixed infections.InterpretationWe combined metagenomic and enriched Nanopore sequencing for one sample in parallel. Our NanoMP approach simultaneously covered bacteria, viruses and fungi in respiratory specimens and demonstrated good diagnostic performance in real clinical settings.FundingNational Key Research and Development Program of China and National Natural Science Foundation of China.
| S-EPMC10652131 | biostudies-literature
Project description:<p>Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3,500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. As such, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe <i>GJB2</i>-related skin disease in one neonate; <i>BRAT1</i>-related lethal neonatal rigidity and multifocal seizure syndrome in another infant, identified <i>BCL9L</i> as a novel, recessive visceral heterotaxy gene (<i>HTX6</i>) in a pedigree, and ruled out known candidate genes in one infants. Sequencing of parents or affected siblings expedited the identification of disease gene in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling.</p> <p>Reprinted from Saunders et. al, Rapid Whole-Genome Sequencing for Genetic Disease Diagnosis in Neonatal Intensive Care Units. Sci. Transl. Med. 4, 154ra135 (2012; <a href="http://www.ncbi.nlm.nih.gov/pubmed?term=Rapid%20Whole-Genome%20Sequencing%20for%20Genetic%20Disease%20Diagnosis%20in%20Neonatal%20Intensive%20Care%20Units">PMID: 23035047</a>) with permission from AAAS.</p>
| phs000564 | dbGaP
Project description:<p>Monogenic diseases are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth. More than 3,500 monogenic diseases have been characterized, but clinical testing is available for only some of them and many feature clinical and genetic heterogeneity. As such, an immense unmet need exists for improved molecular diagnosis in infants. Because disease progression is extremely rapid, albeit heterogeneous, in newborns, molecular diagnoses must occur quickly to be relevant for clinical decision-making. We describe 50-hour differential diagnosis of genetic disorders by whole-genome sequencing (WGS) that features automated bioinformatic analysis and is intended to be a prototype for use in neonatal intensive care units. Retrospective 50-hour WGS identified known molecular diagnoses in two children. Prospective WGS disclosed potential molecular diagnosis of a severe <i>GJB2</i>-related skin disease in one neonate; <i>BRAT1</i>-related lethal neonatal rigidity and multifocal seizure syndrome in another infant, identified <i>BCL9L</i> as a novel, recessive visceral heterotaxy gene (<i>HTX6</i>) in a pedigree, and ruled out known candidate genes in one infants. Sequencing of parents or affected siblings expedited the identification of disease gene in prospective cases. Thus, rapid WGS can potentially broaden and foreshorten differential diagnosis, resulting in fewer empirical treatments and faster progression to genetic and prognostic counseling.</p> <p>Reprinted from Saunders et. al, Rapid Whole-Genome Sequencing for Genetic Disease Diagnosis in Neonatal Intensive Care Units. Sci. Transl. Med. 4, 154ra135 (2012; <a href="http://www.ncbi.nlm.nih.gov/pubmed?term=Rapid%20Whole-Genome%20Sequencing%20for%20Genetic%20Disease%20Diagnosis%20in%20Neonatal%20Intensive%20Care%20Units">PMID: 23035047 </a>) with permission from AAAS.</p>
| phs000564.v1.p1 | EGA