Gene-expression patterns reveal underlying biological processes in Kawasaki disease

Background: Kawasaki disease (KD) is an acute self-limited vasculitis and the leading cause of acquired heart disease in children in developed countries. No etiologic agent(s) has been identified, and the processes that mediate formation of coronary artery aneurysms and abatement of fever following treatment with intravenous immunoglobulin (IVIG) remain poorly understood. Results: In an initial survey, we used DNA microarrays to examine patterns of gene expression in peripheral whole blood from 20 children with KD; each was sampled during the acute, subacute, and convalescent phases of the illness. Acute KD was characterized by increased relative abundance of gene transcripts associated with innate immune and proinflammatory responses and decreased abundance of transcripts associated with natural killer cells and CD8+ lymphocytes. There was significant temporal variation in transcript levels during the acute disease phase and stabilization thereafter. We confirmed these temporal patterns in a second cohort of 64 patients, and identified additional inter-individual differences in transcript abundance. Notably, higher levels of transcripts of the gene for carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) were associated with an increased percentage of unsegmented neutrophils, fewer days of illness, higher levels of C-reactive protein, and subsequent non-response to IVIG; this last association was confirmed by quantitative reverse transcription PCR in a third cohort of 33 patients, and was independent of day of illness. Conclusion: Acute KD is characterized by dynamic and variable gene-expression programs that highlight the importance of neutrophil activation state and apoptosis in KD pathogenesis. Our findings also support the feasibility of extracting biomarkers associated with clinical prognosis from gene-expression profiles of individuals with systemic inflammatory illnesses

Diagnosis of Kawasaki disease using a minimal whole blood gene expression signature

Importance There is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective To identify a whole blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design Case-control discovery study groups comprising training, test, and validation groups of children with KD or comparator febrile illness. Setting Hospitals in the UK, Spain, Netherlands and USA. Participants The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, 242 bacterial or viral infections) and 55 healthy controls. The independent validation group included 130 febrile children and 102 KD patients, including 72 in the first 7 days of illness. Exposures Whole blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (Parallel Deterministic Model Search). Main outcomes and measures The ability of transcript signatures - implemented as Disease Risk Scores - to discriminate KD cases from controls, was assessed by Area Under the Curve (AUC), sensitivity, and specificity at the optimal cut-point according to Youden’s index. Results A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set with AUC, sensitivity, and specificity (95% confidence intervals (CI)) of 96.2% (92.5-99.9), 81.7% (60.0-94.8), and 92.1% (84.0-97.0), respectively. In the validation set, the signature distinguished KD from febrile controls with AUC, sensitivity, and specificity (95% CI) of 94.6% (91.3-98.0), 85.9% (76.8-92.6), and 89.1% (83.0-93.7) respectively. The signature was applied to clinically defined categories of Definite, Highly Probable and Possible KD resulting in AUCs of 98.1%, 96.3% and 70.0% respectively, mirroring clinical certainty. Conclusions and relevance A 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript Disease Risk Score may enable earlier diagnosis and treatment of KD, and reduce inappropriate treatment in those with other diagnoses

Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children.

IMPORTANCE: Because clinical features do not reliably distinguish bacterial from viral infection, many children worldwide receive unnecessary antibiotic treatment, while bacterial infection is missed in others. OBJECTIVE: To identify a blood RNA expression signature that distinguishes bacterial from viral infection in febrile children. DESIGN, SETTING, AND PARTICIPANTS: Febrile children presenting to participating hospitals in the United Kingdom, Spain, the Netherlands, and the United States between 2009-2013 were prospectively recruited, comprising a discovery group and validation group. Each group was classified after microbiological investigation as having definite bacterial infection, definite viral infection, or indeterminate infection. RNA expression signatures distinguishing definite bacterial from viral infection were identified in the discovery group and diagnostic performance assessed in the validation group. Additional validation was undertaken in separate studies of children with meningococcal disease (n = 24) and inflammatory diseases (n = 48) and on published gene expression datasets. EXPOSURES: A 2-transcript RNA expression signature distinguishing bacterial infection from viral infection was evaluated against clinical and microbiological diagnosis. MAIN OUTCOMES AND MEASURES: Definite bacterial and viral infection was confirmed by culture or molecular detection of the pathogens. Performance of the RNA signature was evaluated in the definite bacterial and viral group and in the indeterminate infection group. RESULTS: The discovery group of 240 children (median age, 19 months; 62% male) included 52 with definite bacterial infection, of whom 36 (69%) required intensive care, and 92 with definite viral infection, of whom 32 (35%) required intensive care. Ninety-six children had indeterminate infection. Analysis of RNA expression data identified a 38-transcript signature distinguishing bacterial from viral infection. A smaller (2-transcript) signature (FAM89A and IFI44L) was identified by removing highly correlated transcripts. When this 2-transcript signature was implemented as a disease risk score in the validation group (130 children, with 23 definite bacterial, 28 definite viral, and 79 indeterminate infections; median age, 17 months; 57% male), all 23 patients with microbiologically confirmed definite bacterial infection were classified as bacterial (sensitivity, 100% [95% CI, 100%-100%]) and 27 of 28 patients with definite viral infection were classified as viral (specificity, 96.4% [95% CI, 89.3%-100%]). When applied to additional validation datasets from patients with meningococcal and inflammatory diseases, bacterial infection was identified with a sensitivity of 91.7% (95% CI, 79.2%-100%) and 90.0% (95% CI, 70.0%-100%), respectively, and with specificity of 96.0% (95% CI, 88.0%-100%) and 95.8% (95% CI, 89.6%-100%). Of the children in the indeterminate groups, 46.3% (63/136) were classified as having bacterial infection, although 94.9% (129/136) received antibiotic treatment. CONCLUSIONS AND RELEVANCE: This study provides preliminary data regarding test accuracy of a 2-transcript host RNA signature discriminating bacterial from viral infection in febrile children. Further studies are needed in diverse groups of patients to assess accuracy and clinical utility of this test in different clinical settings

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

Background: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. Methods: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a ‘‘cost’’ weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identifiedwas further validated in a new RNA sequencing dataset comprising 411 febrile children. Findings: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort andbenchmarked against existingdichotomousRNA signatures. Conclusions: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. Funding: European Union’s Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC

Autoantibodies Against Proteins Previously Associated With Autoimmunity in Adult and Pediatric Patients With COVID-19 and Children With MIS-C

The antibody profile against autoantigens previously associated with autoimmune diseases and other human proteins in patients with COVID-19 or multisystem inflammatory syndrome in children (MIS-C) remains poorly defined. Here we show that 30% of adults with COVID-19 had autoantibodies against the lung antigen KCNRG, and 34% had antibodies to the SLE-associated Smith-D3 protein. Children with COVID-19 rarely had autoantibodies; one of 59 children had GAD65 autoantibodies associated with acute onset of insulin-dependent diabetes. While autoantibodies associated with SLE/Sjögren's syndrome (Ro52, Ro60, and La) and/or autoimmune gastritis (gastric ATPase) were detected in 74% (40/54) of MIS-C patients, further analysis of these patients and of children with Kawasaki disease (KD), showed that the administration of intravenous immunoglobulin (IVIG) was largely responsible for detection of these autoantibodies in both groups of patients. Monitoring in vivo decay of the autoantibodies in MIS-C children showed that the IVIG-derived Ro52, Ro60, and La autoantibodies declined to undetectable levels by 45-60 days, but gastric ATPase autoantibodies declined more slowly requiring >100 days until undetectable. Further testing of IgG and/or IgA antibodies against a subset of potential targets identified by published autoantigen array studies of MIS-C failed to detect autoantibodies against most (16/18) of these proteins in patients with MIS-C who had not received IVIG. However, Troponin C2 and KLHL12 autoantibodies were detected in 2 of 20 and 1 of 20 patients with MIS-C, respectively. Overall, these results suggest that IVIG therapy may be a confounding factor in autoantibody measurements in MIS-C and that antibodies against antigens associated with autoimmune diseases or other human proteins are uncommon in MIS-C

Emergency department documentation templates: variability in template selection and association with physical examination and test ordering in dizziness presentations

Abstract Background Clinical documentation systems, such as templates, have been associated with process utilization. The T-System emergency department (ED) templates are widely used but lacking are analyses of the templates association with processes. This system is also unique because of the many different template options available, and thus the selection of the template may also be important. We aimed to describe the selection of templates in ED dizziness presentations and to investigate the association between items on templates and process utilization. Methods Dizziness visits were captured from a population-based study of EDs that use documentation templates. Two relevant process outcomes were assessed: head computerized tomography (CT) scan and nystagmus examination. Multivariable logistic regression was used to estimate the probability of each outcome for patients who did or did not receive a relevant-item template. Propensity scores were also used to adjust for selection effects. Results The final cohort was 1,485 visits. Thirty-one different templates were used. Use of a template with a head CT item was associated with an increase in the adjusted probability of head CT utilization from 12.2% (95% CI, 8.9%-16.6%) to 29.3% (95% CI, 26.0%-32.9%). The adjusted probability of documentation of a nystagmus assessment increased from 12.0% (95%CI, 8.8%-16.2%) when a nystagmus-item template was not used to 95.0% (95% CI, 92.8%-96.6%) when a nystagmus-item template was used. The associations remained significant after propensity score adjustments. Conclusions Providers use many different templates in dizziness presentations. Important differences exist in the various templates and the template that is used likely impacts process utilization, even though selection may be arbitrary. The optimal design and selection of templates may offer a feasible and effective opportunity to improve care delivery.http://deepblue.lib.umich.edu/bitstream/2027.42/112490/1/12913_2010_Article_1586.pd

A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses

<p>Abstract</p> <p>Background</p> <p>Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.</p> <p>Methods</p> <p>Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.</p> <p>Results</p> <p>Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.</p> <p>Conclusions</p> <p>A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.</p

Predicting Hemolytic Uremic Syndrome and Renal Replacement Therapy in Shiga Toxin-producing Escherichia coli-infected Children.

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) infections are leading causes of pediatric acute renal failure. Identifying hemolytic uremic syndrome (HUS) risk factors is needed to guide care. METHODS: We conducted a multicenter, historical cohort study to identify features associated with development of HUS (primary outcome) and need for renal replacement therapy (RRT) (secondary outcome) in STEC-infected children without HUS at initial presentation. Children agedeligible. RESULTS: Of 927 STEC-infected children, 41 (4.4%) had HUS at presentation; of the remaining 886, 126 (14.2%) developed HUS. Predictors (all shown as odds ratio [OR] with 95% confidence interval [CI]) of HUS included younger age (0.77 [.69-.85] per year), leukocyte count ≥13.0 × 103/μL (2.54 [1.42-4.54]), higher hematocrit (1.83 [1.21-2.77] per 5% increase) and serum creatinine (10.82 [1.49-78.69] per 1 mg/dL increase), platelet count \u3c250 \u3e× 103/μL (1.92 [1.02-3.60]), lower serum sodium (1.12 [1.02-1.23 per 1 mmol/L decrease), and intravenous fluid administration initiated ≥4 days following diarrhea onset (2.50 [1.14-5.46]). A longer interval from diarrhea onset to index visit was associated with reduced HUS risk (OR, 0.70 [95% CI, .54-.90]). RRT predictors (all shown as OR [95% CI]) included female sex (2.27 [1.14-4.50]), younger age (0.83 [.74-.92] per year), lower serum sodium (1.15 [1.04-1.27] per mmol/L decrease), higher leukocyte count ≥13.0 × 103/μL (2.35 [1.17-4.72]) and creatinine (7.75 [1.20-50.16] per 1 mg/dL increase) concentrations, and initial intravenous fluid administration ≥4 days following diarrhea onset (2.71 [1.18-6.21]). CONCLUSIONS: The complex nature of STEC infection renders predicting its course a challenge. Risk factors we identified highlight the importance of avoiding dehydration and performing close clinical and laboratory monitoring

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. METHODS: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children. FINDINGS: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures. CONCLUSIONS: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. FUNDING: European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC

Diagnosis of Kawasaki Disease Using a Minimal Whole-Blood Gene Expression Signature.

Importance: To date, there is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective: To identify a whole-blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design, Setting, and Participants: The case-control study comprised a discovery group that included a training and test set and a validation group of children with KD or comparator febrile illness. The setting was pediatric centers in the United Kingdom, Spain, the Netherlands, and the United States. The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, and 242 bacterial or viral infections) and 55 healthy controls. The independent validation group comprised 102 patients with KD, including 72 in the first 7 days of illness, and 130 febrile controls. The study dates were March 1, 2009, to November 14, 2013, and data analysis took place from January 1, 2015, to December 31, 2017. Main Outcomes and Measures: Whole-blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (parallel regularized regression model search). The ability of transcript signatures (implemented as disease risk scores) to discriminate KD cases from controls was assessed by area under the curve (AUC), sensitivity, and specificity at the optimal cut point according to the Youden index. Results: Among 404 patients in the discovery set, there were 78 with KD (median age, 27 months; 55.1% male) and 326 febrile controls (median age, 37 months; 56.4% male). Among 202 patients in the validation set, there were 72 with KD (median age, 34 months; 62.5% male) and 130 febrile controls (median age, 17 months; 56.9% male). A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set, with AUC of 96.2% (95% CI, 92.5%-99.9%), sensitivity of 81.7% (95% CI, 60.0%-94.8%), and specificity of 92.1% (95% CI, 84.0%-97.0%). In the validation set, the signature distinguished KD from febrile controls, with AUC of 94.6% (95% CI, 91.3%-98.0%), sensitivity of 85.9% (95% CI, 76.8%-92.6%), and specificity of 89.1% (95% CI, 83.0%-93.7%). The signature was applied to clinically defined categories of definite, highly probable, and possible KD, resulting in AUCs of 98.1% (95% CI, 94.5%-100%), 96.3% (95% CI, 93.3%-99.4%), and 70.0% (95% CI, 53.4%-86.6%), respectively, mirroring certainty of clinical diagnosis. Conclusions and Relevance: In this study, a 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript disease risk score may enable earlier diagnosis and treatment of KD and reduce inappropriate treatment in those with other diagnoses