Monocyte state 1 (MS1) cells in critically ill patients with sepsis or non-infectious conditions: association with disease course and host response

BACKGROUND

Sepsis is a life-threatening condition arising from an aberrant host response to infection. Recent single-cell RNA sequencing investigations identified an immature bone-marrow-derived CD14+ monocyte phenotype with immune suppressive properties termed "monocyte state 1" (MS1) in patients with sepsis. Our objective was to determine the association of MS1 cell profiles with disease presentation, outcomes, and host response characteristics.

METHODS

We used the transcriptome deconvolution method (CIBERSORTx) to estimate the percentage of MS1 cells from blood RNA profiles of patients with sepsis admitted to the intensive care unit (ICU). We compared these profiles to ICU patients without infection and to healthy controls. Host response dysregulation was further studied by gene co-expression network and gene set enrichment analyses of blood leukocytes, and measurement of 15 plasma biomarkers indicative of pathways implicated in sepsis pathogenesis.

RESULTS

Sepsis patients (n = 332) were divided into three equally-sized groups based on their MS1 cell levels (low, intermediate, and high). MS1 groups did not differ in demographics or comorbidities. The intermediate and high MS1 groups presented with higher disease severity and more often had shock. MS1 cell abundance did not differ between survivors and non-survivors, or between patients who did or did not acquire a secondary infection. Higher MS1 cell percentages were associated with downregulation of lymphocyte-related and interferon response genes in blood leukocytes, with concurrent upregulation of inflammatory response pathways, including tumor necrosis factor signaling via nuclear factor-κB. Previously described sepsis host response transcriptomic subtypes showed different MS1 cell abundances, and MS1 cell percentages positively correlated with the "quantitative sepsis response signature" and "molecular degree of perturbation" scores. Plasma biomarker levels, indicative of inflammation, endothelial cell activation, and coagulation activation, were largely similar between MS1 groups. In ICU patients without infection (n = 215), MS1 cell percentages and their relation with disease severity, shock, and host response dysregulation were highly similar to those in sepsis patients.

CONCLUSIONS

High MS1 cell percentages are associated with increased disease severity and shock in critically ill patients with sepsis or a non-infectious condition. High MS1 cell abundance likely indicates broad immune dysregulation, entailing not only immunosuppression but also anomalies reflecting exaggerated inflammatory responses.

Transitions of blood immune endotypes and improved outcome by anakinra in COVID-19 pneumonia: an analysis of the SAVE-MORE randomized controlled trial

BACKGROUND

Endotype classification may guide immunomodulatory management of patients with bacterial and viral sepsis. We aimed to identify immune endotypes and transitions associated with response to anakinra (human interleukin 1 receptor antagonist) in participants in the SAVE-MORE trial.

METHODS

Adult patients hospitalized with radiological findings of PCR-confirmed severe pneumonia caused by SARS-CoV-2 and plasma-soluble urokinase plasminogen activator receptor levels of ≥ 6 ng/ml in the SAVE-MORE trial (NCT04680949) were characterized at baseline and days 4 and 7 of treatment using a previously defined 33-messenger RNA classifier to assign an immunological endotype in blood. Endpoints were changes in endotypes and progression to severe respiratory failure (SRF) associated with anakinra treatment.

RESULTS

At baseline, 23.2% of 393 patients were designated as inflammopathic, 41.1% as adaptive, and 35.7% as coagulopathic. Only 23.9% were designated as the same endotype at days 4 and 7 compared to baseline, while all other patients transitioned between endotypes. Anakinra-treated patients were more likely to remain in the adaptive endotype during 7-day treatment (24.4% vs. 9.9%; p < 0.001). Anakinra also protected patients with coagulopathic endotype at day 7 against SRF compared to placebo (27.8% vs. 55.9%; p = 0.013).

CONCLUSION

We identify an association between endotypes defined using blood transcriptome and anakinra therapy for COVID-19 pneumonia, with anakinra-treated patients shifting toward endotypes associated with a better outcome, mainly the adaptive endotype. Trial registration ClinicalTrials.gov, NCT04680949, December 23, 2020.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort

Background

Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions.

Methods

We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme.

Findings

Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes.

Interpretation

Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples

BACKGROUND

Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful treatment requires timely diagnosis of viral etiology, which is complicated by overlap in clinical presentation with the non-viral ARIs. Multiple pandemics in the twenty-first century to date have further highlighted the unmet need for effective monitoring of clinically relevant emerging viruses. Recent studies have identified conserved host response to viral infections in the blood.

METHODS

We hypothesize that a similarly conserved host response in nasal samples can be utilized for diagnosis and to rule out viral infection in symptomatic patients when current diagnostic tests are negative. Using a multi-cohort analysis framework, we analyzed 1555 nasal samples across 10 independent cohorts dividing them into training and validation.

RESULTS

Using six of the datasets for training, we identified 119 genes that are consistently differentially expressed in viral ARI patients (N = 236) compared to healthy controls (N = 146) and further down-selected 33 genes for classifier development. The resulting locked logistic regression-based classifier using the 33-mRNAs had AUC of 0.94 and 0.89 in the six training and four validation datasets, respectively. Furthermore, we found that although trained on healthy controls only, in the four validation datasets, the 33-mRNA classifier distinguished viral ARI from both healthy or non-viral ARI samples with > 80% specificity and sensitivity, irrespective of age, viral type, and viral load. Single-cell RNA-sequencing data showed that the 33-mRNA signature is dominated by macrophages and neutrophils in nasal samples.

CONCLUSION

This proof-of-concept signature has potential to be adapted as a clinical point-of-care test ('RespVerity') to improve the diagnosis of viral ARIs.

Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study

BACKGROUND

Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals.

METHODS

We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX^®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding.

RESULTS

Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively).

CONCLUSION

Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Defining critical illness using immunological endotypes in patients with and without of sepsis: A cohort study

Background: Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying characteristic gene expression patterns, or endotypes, in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical ICU with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical intensive care unit (ICU) and at set intervals. Methods: We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020 . Using an FDA-cleared analytical platform (nCounter FLEX ^® , NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause (in-hospital, 30-, 90-day) mortality, development of chronic critical illness (CCI), and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding. Results: Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive , though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p<0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively). Conclusion : Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

Multisite validation of a host response signature for predicting likelihood of bacterial and viral infections in patients with suspected influenza

BACKGROUND

Indiscriminate use of antimicrobials and antimicrobial resistance is a public health threat. IMX-BVN-1, a 29-host mRNA classifier, provides two separate scores that predict likelihoods of bacterial and viral infections in patients with suspected acute infections. We validated the performance of IMX-BVN-1 in adults attending acute health care settings with suspected influenza.

METHOD

We amplified 29-host response genes in RNA extracted from blood by NanoString nCounter. IMX-BVN-1 calculated two scores to predict probabilities of bacterial and viral infections. Results were compared against the infection status (no infection; highly probable/possible infection; confirmed infection) determined by clinical adjudication.

RESULTS

Amongst 602 adult patients (74.9% ED, 16.9% ICU, 8.1% outpatients), 7.6% showed in-hospital mortality and 15.5% immunosuppression. Median IMX-BVN-1 bacterial and viral scores were higher in patients with confirmed bacterial (0.27) and viral (0.62) infections than in those without bacterial (0.08) or viral (0.21) infection, respectively. The AUROC distinguishing bacterial from nonbacterial illness was 0.81 and 0.87 when distinguishing viral from nonviral illness. The bacterial top quartile's positive likelihood ratio (LR) was 4.38 with a rule-in specificity of 88%; the bacterial bottom quartile's negative LR was 0.13 with a rule-out sensitivity of 96%. Similarly, the viral top quartile showed an infinite LR with rule-in specificity of 100%; the viral bottom quartile had a LR of 0.22 and a rule-out sensitivity of 85%.

CONCLUSION

IMX-BVN-1 showed high accuracy for differentiating bacterial and viral infections from noninfectious illness in patients with suspected influenza. Clinical utility of IMX-BVN will be validated following integration into a point of care system.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

1723 Diagnostic accuracy of a novel transcriptomic classifier for bacterial and viral infections – an individual patient data meta-analysis

Aims, Objectives and Background

Arising from demographic differences between healthcare systems, patients in the emergency department (ED) present with a broad range of diagnoses and clinical severities. Independent validation of a novel diagnostic tool is critical to ensure reliable and reproducible clinical performance. So far, three independent cohort studies have validated the performance of the machine-learning classifier IMX-Bacterial/Viral/Non-infected (IMX-BVN) to diagnose bacterial and viral infections; those have been combined to facilitate an individual patient data meta-analysis of performance.

Method and Design

ED patients (n=1,277) with suspected infection from three international, observational studies (USA/Germany/Greece) were included. Of those, 661 had a unanimous (‘consensus’) ground truth of infection status established by panel adjudication. Quantitative expression of 29-signature mRNAs was measured on a NanoString nCounter® SPRINT system. The classifier BVN version 3 (IMX-BVN-3) was applied to generate scores, which fall into four discrete interpretation bands (very unlikely, unlikely, possible, very likely). Sensitivity, specificity, and corresponding nominal likelihood ratios were calculated with 95% confidence intervals for each interpretation band.

Results and Conclusion

360 patients (54.4%) were consensus adjudicated to have a bacterial infection (range: 37.9–81.2%) and 153 (23.1%) to have a viral infection (range: 15.3–44.1%). Pooled likelihood ratios of the interpretation bands for bacterial infections were (from ‘very unlikely bacterial’ to ‘very likely bacterial’) 0.082 (0.039–0.176)/0.333 (0.264–0.419)/2.244 (1.598–3.152)/9.459 (5.808–15.404), associated with a rule-in specificity of 0.947 (0.915–0.967) and a rule-out sensitivity of 0.981 (0.960–0.991) in the outer interpretation bands. Pooled likelihood ratios of the interpretation bands for viral infections were (from ‘very unlikely viral’ to ‘very likely viral’) 0.182 (0.102–0.324)/0.292 (0.181–0.471)/0.956 (0.593–1.540)/6.021 (4.636–7.821), associated with a rule-in specificity of 0.884 (0.853–0.909) and rule-out sensitivity of 0.928 (0.876–0.959).

The IMX-BVN-3 classifier exhibits strong performance in a combined cohort of patients from different geographies and settings to rule-in and rule-out patients presenting to EDs with suspected bacterial and viral infections.

A 29-MRNA HOST RESPONSE WHOLE-BLOOD SIGNATURE IMPROVES PREDICTION OF 28-DAY MORTALITY AND 7-DAY INTENSIVE CARE UNIT CARE IN ADULTS PRESENTING TO THE EMERGENCY DEPARTMENT WITH SUSPECTED ACUTE INFECTION AND/OR SEPSIS

ABSTRACT

Background: Risk stratification of emergency department patients with suspected acute infections and/or suspected sepsis remains challenging. We prospectively validated a 29-messenger RNA host response classifier for predicting severity in these patients. Methods: We enrolled adults presenting with suspected acute infections and at least one vital sign abnormality to six emergency departments in Greece. Twenty-nine target host RNAs were quantified on NanoString nCounter and analyzed with the Inflammatix Severity 2 (IMX-SEV-2) classifier to determine risk scores as low, moderate, and high severity. Performance of IMX-SEV-2 for prediction of 28-day mortality was compared with that of lactate, procalcitonin, and quick sequential organ failure assessment (qSOFA). Results: A total of 397 individuals were enrolled; 38 individuals (9.6%) died within 28 days. Inflammatix Severity 2 classifier predicted 28-day mortality with an area under the receiver operator characteristics curve of 0.82 (95% confidence interval [CI], 0.74-0.90) compared with lactate, 0.66 (95% CI, 0.54-0.77); procalcitonin, 0.67 (95% CI, 0.57-0.78); and qSOFA, 0.81 (95% CI, 0.72-0.89). Combining qSOFA with IMX-SEV-2 improved prognostic accuracy from 0.81 to 0.89 (95% CI, 0.82-0.96). The high-severity (rule-in) interpretation band of IMX-SEV-2 demonstrated 96.9% specificity for predicting 28-day mortality, whereas the low-severity (rule-out) band had a sensitivity of 78.9%. Similarly, IMX-SEV-2 alone accurately predicted the need for day-7 intensive care unit care and further boosted overall accuracy when combined with qSOFA. Conclusions: Inflammatix Severity 2 classifier predicted 28-day mortality and 7-day intensive care unit care with high accuracy and boosted the accuracy of clinical scores when used in combination.

Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients

Importance

Rapid and accurate discrimination of sepsis and its potential severity currently require multiple assays with slow processing times that are often inconclusive in discerning sepsis from sterile inflammation.

Objective

To analyze a whole-blood, multivalent, host-messenger RNA expression metric for estimating the likelihood of bacterial infection and 30-day mortality and compare performance of the metric with that of other diagnostic and prognostic biomarkers and clinical parameters.

Design, Setting, and Participants

This prospective diagnostic and prognostic study was performed in the surgical intensive care unit (ICU) of a single, academic health science center. The analysis included 200 critically ill adult patients admitted with suspected sepsis (cohort A) or those at high risk for developing sepsis (cohort B) between July 1, 2020, and July 30, 2021.

Exposures

Whole-blood sample measurements of a custom 29-messenger RNA transcriptomic metric classifier for likelihood of bacterial infection (IMX-BVN-3) or 30-day mortality (severity) (IMX-SEV-3) in a clinical-diagnostic laboratory setting using an analysis platform (510[k]-cleared nCounter FLEX; NanoString, Inc), compared with measurement of procalcitonin and interleukin 6 (IL-6) plasma levels, and maximum 24-hour sequential organ failure assessment (SOFA) scores.

Main Outcomes and Measures

Estimated sepsis and 30-day mortality performance.

Results

Among the 200 patients included (124 men [62.0%] and 76 women [38.0%]; median age, 62.5 [IQR, 47.0-72.0] years), the IMX-BVN-3 bacterial infection classifier had an area under the receiver operating characteristics curve (AUROC) of 0.84 (95% CI, 0.77-0.90) for discriminating bacterial infection at ICU admission, similar to procalcitonin (0.85 [95% CI, 0.79-0.90]; P = .79) and significantly better than IL-6 (0.67 [95% CI, 0.58-0.75]; P < .001). For estimating 30-day mortality, the IMX-SEV-3 metric had an AUROC of 0.81 (95% CI, 0.66-0.95), which was significantly better than IL-6 levels (0.57 [95% CI, 0.37-0.77]; P = .006), marginally better than procalcitonin levels (0.65 [95% CI, 0.50-0.79]; P = .06), and similar to the SOFA score (0.76 [95% CI, 0.62-0.91]; P = .48). Combining IMX-BVN-3 and IMX-SEV-3 with procalcitonin or IL-6 levels or SOFA scores did not significantly improve performance. Among patients with sepsis, IMX-BVN-3 scores decreased over time, reflecting the resolution of sepsis. In 11 individuals at high risk (cohort B) who subsequently developed sepsis during their hospital course, IMX-BVN-3 bacterial infection scores did not decline over time and peaked on the day of documented infection.

Conclusions and Relevance

In this diagnostic and prognostic study, a novel, multivalent, transcriptomic metric accurately estimated the presence of bacterial infection and risk for 30-day mortality in patients admitted to a surgical ICU. The performance of this single transcriptomic metric was equivalent to or better than multiple alternative diagnostic and prognostic metrics when measured at admission and provided additional information when measured over time.

A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.

Diagnostic Host Gene Expression Analysis by Quantitative Reverse Transcription Loop-Mediated Isothermal Amplification to Discriminate between Bacterial and Viral Infections

BACKGROUND

Early and accurate diagnosis of acute infections can help minimize the overprescription of antibiotics and improve patient outcomes. Discrimination between bacterial and viral etiologies in acute infection based on changes in host gene expression has been described. Unfortunately, established technologies used for gene expression profiling are typically expensive and slow, confounding integration into clinical workflows. Here we report the development of an ultra-rapid test system for host gene expression profiling from blood based on quantitative reverse transcription followed by loop-mediated isothermal amplification (qRT-LAMP).

METHODS

We developed 10 messenger ribonucleic acid-specific assays based on qRT-LAMP targeting 7 informative biomarkers to discriminate viral from bacterial infections and 3 housekeeping reference genes. We optimized qRT-LAMP formulations to achieve a turnaround time of 12 min without sacrificing specificity or precision. The accuracy of the test system was verified utilizing blood samples from 57 patients and comparing qRT-LAMP results to profiles obtained using an orthogonal reference technology.

RESULTS

We observed a Pearson coefficient of 0.90 between bacterial/viral metascores generated by qRT-LAMP and the reference technology.

CONCLUSIONS

qRT-LAMP assays can provide sufficiently accurate gene expression profiling data to enable discrimination between bacterial and viral etiologies using an established set of biomarkers and a classification algorithm.

A host transcriptomic signature for identification of respiratory viral infections in the community

BACKGROUND

Fever-7 is a test evaluating host mRNA expression levels of IFI27, JUP, LAX, HK3, TNIP1, GPAA1 and CTSB in blood able to detect viral infections. This test has been validated mostly in hospital settings. Here we have evaluated Fever-7 to identify the presence of respiratory viral infections in a Community Health Center.

METHODS

A prospective study was conducted in the "Servicio de Urgencias de Atención Primaria" in Salamanca, Spain. Patients with clinical signs of respiratory infection and at least one point in the National Early Warning Score were recruited. Fever-7 mRNAs were profiled on a Nanostring nCounter® SPRINT instrument from blood collected upon patient enrolment. Viral diagnosis was performed on nasopharyngeal aspirates (NPAs) using the Biofire-RP2 panel.

RESULTS

A respiratory virus was detected in the NPAs of 66 of the 100 patients enrolled. Median National Early Warning Score was 7 in the group with no virus detected and 6.5 in the group with a respiratory viral infection (P > .05). The Fever-7 score yielded an overall AUC of 0.81 to predict a positive viral syndromic test. The optimal operating point for the Fever-7 score yielded a sensitivity of 82% with a specificity of 71%. Multivariate analysis showed that Fever-7 was a robust marker of viral infection independently of age, sex, major comorbidities and disease severity at presentation (OR [CI95%], 3.73 [2.14-6.51], P < .001).

CONCLUSIONS

Fever-7 is a promising host immune mRNA signature for the early identification of a respiratory viral infection in the community.

A Multi-mRNA Prognostic Signature for Anti-TNFα Therapy Response in Patients with Inflammatory Bowel Disease

BACKGROUND

Anti-TNF-alpha (anti-TNFα) therapies have transformed the care and management of inflammatory bowel disease (IBD). However, they are expensive and ineffective in greater than 50% of patients, and they increase the risk of infections, liver issues, arthritis, and lymphoma. With 1.6 million Americans suffering from IBD and global prevalence on the rise, there is a critical unmet need in the use of anti-TNFα therapies: a test for the likelihood of therapy response. Here, as a proof-of-concept, we present a multi-mRNA signature for predicting response to anti-TNFα treatment to improve the efficacy and cost-to-benefit ratio of these biologics.

METHODS

We surveyed public data repositories and curated four transcriptomic datasets (n = 136) from colonic and ileal mucosal biopsies of IBD patients (pretreatment) who were subjected to anti-TNFα therapy and subsequently adjudicated for response. We applied a multicohort analysis with a leave-one-study-out (LOSO) approach, MetaIntegrator, to identify significant differentially expressed (DE) genes between responders and non-responders and then used a greedy forward search to identify a parsimonious gene signature. We then calculated an anti-TNFα response (ATR) score based on this parsimonious gene signature to predict responder status and assessed discriminatory performance via an area-under-receiver operating-characteristic curve (AUROC).

RESULTS

We identified 324 significant DE genes between responders and non-responders. The greedy forward search yielded seven genes that robustly distinguish anti-TNFα responders from non-responders, with an AUROC of 0.88 (95% CI: 0.70-1). The Youden index yielded a mean sensitivity of 91%, mean specificity of 76%, and mean accuracy of 86%.

CONCLUSIONS

Our findings suggest that there is a robust transcriptomic signature for predicting anti-TNFα response in mucosal biopsies from IBD patients prior to treatment initiation. This seven-gene signature should be further investigated for its potential to be translated into a predictive test for clinical use.

A Novel 29-Messenger RNA Host-Response Assay From Whole Blood Accurately Identifies Bacterial and Viral Infections in Patients Presenting to the Emergency Department With Suspected Infections: A Prospective Observational Study

OBJECTIVES

The rapid diagnosis of acute infections and sepsis remains a serious challenge. As a result of limitations in current diagnostics, guidelines recommend early antimicrobials for suspected sepsis patients to improve outcomes at a cost to antimicrobial stewardship. We aimed to develop and prospectively validate a new, 29-messenger RNA blood-based host-response classifier Inflammatix Bacterial Viral Non-Infected version 2 (IMX-BVN-2) to determine the likelihood of bacterial and viral infections.

DESIGN

Prospective observational study.

SETTING

Emergency Department, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Germany.

PATIENTS

Three hundred twelve adult patients presenting to the emergency department with suspected acute infections or sepsis with at least one vital sign change.

INTERVENTIONS

None (observational study only).

MEASUREMENTS AND MAIN RESULTS

Gene expression levels from extracted whole blood RNA was quantified on a NanoString nCounter SPRINT (NanoString Technologies, Seattle, WA). Two predicted probability scores for the presence of bacterial and viral infection were calculated using the IMX-BVN-2 neural network classifier, which was trained on an independent development set. The IMX-BVN-2 bacterial score showed an area under the receiver operating curve for adjudicated bacterial versus ruled out bacterial infection of 0.90 (95% CI, 0.85-0.95) compared with 0.89 (95% CI, 0.84-0.94) for procalcitonin with procalcitonin being used in the adjudication. The IMX-BVN-2 viral score area under the receiver operating curve for adjudicated versus ruled out viral infection was 0.83 (95% CI, 0.77-0.89).

CONCLUSIONS

IMX-BVN-2 demonstrated accuracy for detecting both viral infections and bacterial infections. This shows the potential of host-response tests as a novel and practical approach for determining the causes of infections, which could improve patient outcomes while upholding antimicrobial stewardship.

Prospective validation of an 11-gene mRNA host response score for mortality risk stratification in the intensive care unit

Several clinical calculators predict intensive care unit (ICU) mortality, however these are cumbersome and often require 24 h of data to calculate. Retrospective studies have demonstrated the utility of whole blood transcriptomic analysis in predicting mortality. In this study, we tested prospective validation of an 11-gene messenger RNA (mRNA) score in an ICU population. Whole blood mRNA from 70 subjects in the Stanford ICU Biobank with samples collected within 24 h of Emergency Department presentation were used to calculate an 11-gene mRNA score. We found that the 11-gene score was highly associated with 60-day mortality, with an area under the receiver operating characteristic curve of 0.68 in all patients, 0.77 in shock patients, and 0.98 in patients whose primary determinant of prognosis was acute illness. Subjects with the highest quartile of mRNA scores were more likely to die in hospital (40% vs 7%, p < 0.01) and within 60 days (40% vs 15%, p = 0.06). The 11-gene score improved prognostication with a categorical Net Reclassification Improvement index of 0.37 (p = 0.03) and an Integrated Discrimination Improvement index of 0.07 (p = 0.02) when combined with Simplified Acute Physiology Score 3 or Acute Physiology and Chronic Health Evaluation II score. The test performed poorly in the 95 independent samples collected > 24 h after emergency department presentation. Tests will target a 30-min turnaround time, allowing for rapid results early in admission. Moving forward, this test may provide valuable real-time prognostic information to improve triage decisions and allow for enrichment of clinical trials.

A 29-mRNA host response test from blood accurately distinguishes bacterial and viral infections among emergency department patients

BACKGROUND

Whether or not to administer antibiotics is a common and challenging clinical decision in patients with suspected infections presenting to the emergency department (ED). We prospectively validate InSep, a 29-mRNA blood-based host response test for the prediction of bacterial and viral infections.

METHODS

The PROMPT trial is a prospective, non-interventional, multi-center clinical study that enrolled 397 adult patients presenting to the ED with signs of acute infection and at least one vital sign change. The infection status was adjudicated using chart review (including a syndromic molecular respiratory panel, procalcitonin and C-reactive protein) by three infectious disease physicians blinded to InSep results. InSep (version BVN-2) was performed using PAXgene Blood RNA processed and quantified on NanoString nCounter SPRINT. InSep results (likelihood of bacterial and viral infection) were compared to the adjudicated infection status.

RESULTS

Subject mean age was 64 years, comorbidities were significant for diabetes (17.1%), chronic obstructive pulmonary disease (13.6%), and severe neurological disease (6.8%); 16.9% of subjects were immunocompromised. Infections were adjudicated as bacterial (14.1%), viral (11.3%) and noninfected (0.25%): 74.1% of subjects were adjudicated as indeterminate. InSep distinguished bacterial vs. viral/noninfected patients and viral vs. bacterial/noninfected patients using consensus adjudication with AUROCs of 0.94 (95% CI 0.90-0.99) and 0.90 (95% CI 0.83-0.96), respectively. AUROCs for bacterial vs. viral/noninfected patients were 0.88 (95% CI 0.79-0.96) for PCT, 0.80 (95% CI 0.72-89) for CRP and 0.78 (95% CI 0.69-0.87) for white blood cell counts (of note, the latter biomarkers were provided as part of clinical adjudication). To enable clinical actionability, InSep incorporates score cutoffs to allocate patients into interpretation bands. The Very Likely (rule in) InSep bacterial band showed a specificity of 98% compared to 94% for the corresponding PCT band (> 0.5 µg/L); the Very Unlikely (rule-out) band showed a sensitivity of 95% for InSep compared to 86% for PCT. For the detection of viral infections, InSep demonstrated a specificity of 93% for the Very Likely band (rule in) and a sensitivity of 96% for the Very Unlikely band (rule out).

CONCLUSIONS

InSep demonstrated high accuracy for predicting the presence of both bacterial and viral infections in ED patients with suspected acute infections or suspected sepsis. When translated into a rapid, point-of-care test, InSep will provide ED physicians with actionable results supporting early informed treatment decisions to improve patient outcomes while upholding antimicrobial stewardship. Registration number at Clinicaltrials.gov NCT03295825.

Sepsis Subclasses: A Framework for Development and Interpretation

Sepsis is defined as a dysregulated host response to infection that leads to life-threatening acute organ dysfunction. It afflicts approximately 50 million people worldwide annually and is often deadly, even when evidence-based guidelines are applied promptly. Many randomized trials tested therapies for sepsis over the past 2 decades, but most have not proven beneficial. This may be because sepsis is a heterogeneous syndrome, characterized by a vast set of clinical and biologic features. Combinations of these features, however, may identify previously unrecognized groups, or "subclasses" with different risks of outcome and response to a given treatment. As efforts to identify sepsis subclasses become more common, many unanswered questions and challenges arise. These include: 1) the semantic underpinning of sepsis subclasses, 2) the conceptual goal of subclasses, 3) considerations about study design, data sources, and statistical methods, 4) the role of emerging data types, and 5) how to determine whether subclasses represent "truth." We discuss these challenges and present a framework for the broader study of sepsis subclasses. This framework is intended to aid in the understanding and interpretation of sepsis subclasses, provide a mechanism for explaining subclasses generated by different methodologic approaches, and guide clinicians in how to consider subclasses in bedside care.

External Corroboration That Corticosteroids May Be Harmful to Septic Shock Endotype A Patients

OBJECTIVES

We previously reported gene expression-based endotypes of pediatric septic shock, endotypes A and B, and that corticosteroid exposure was independently associated with increased mortality among pediatric endotype A patients. The Vasopressin vs Norepinephrine as Initial Therapy in Septic Shock trial tested the efficacy of vasopressin as initial vasopressor therapy for septic shock among adult patients, when compared with norepinephrine. Patients who reached a prespecified dose of either vasopressor were further randomized to receive hydrocortisone or placebo. A proportion of patients in the Vasopressin vs Norepinephrine as Initial Therapy in Septic Shock trial had transcriptomic data generated at baseline using whole blood-derived messenger RNA. We used the publicly available transcriptomic data from the Vasopressin vs Norepinephrine as Initial Therapy in Septic Shock trial to assign the study subjects to pediatric septic shock endotype A or B, and tested the hypothesis that hydrocortisone treatment is associated with increased mortality among patients in endotype A.

DESIGN

Secondary analysis of publicly available transcriptomic data.

SETTING

Multiple adult ICUs.

PATIENTS

Adults with septic shock randomized to hydrocortisone (n = 47) or placebo (n = 50).

INTERVENTIONS

Randomization to the Vasopressin vs Norepinephrine as Initial Therapy in Septic Shock trial experimental arms.

MEASUREMENTS AND MAIN RESULTS

Endotype A patients receiving hydrocortisone had a mortality rate of 46%, whereas endotype A patients receiving placebo had a mortality rate of 22% (p = 0.105). In contrast, the mortality rates for endotype B patients receiving hydrocortisone or placebo were 19% and 22%, respectively. The odds of death were more than three times greater in endotype A patients receiving hydrocortisone than endotype A patients receiving placebo (p = 0.05).

CONCLUSIONS

This exploratory analysis provides further evidence that corticosteroid exposure may be associated with increased mortality among septic shock endotype A patients.

A 33-mRNA Classifier Is Able to Produce Inflammopathic, Adaptive, and Coagulopathic Endotypes with Prognostic Significance: The Outcomes of Metabolic Resuscitation Using Ascorbic Acid, Thiamine, and Glucocorticoids in the Early Treatment of Sepsis (ORANGES) Trial

Background: Retrospective analysis of the transcriptomic host response in sepsis has demonstrated that sepsis can be separated into three endotypes—inflammatory (IE), adaptive (AE), and coagulopathic (CE), which have demonstrated prognostic significance. We undertook a prospective transcriptomic host response analysis in a subgroup of patients enrolled in the Outcomes of Metabolic Resuscitation Using Ascorbic Acid, Thiamine, and Glucocorticoids in the Early Treatment of Sepsis (ORANGES) trial. Methods: Blood was obtained from 51 patients and profiled using a pre-established 33-mRNA classifier to determine sepsis endotypes. Endotypes were compared to therapy subgroups and clinical outcomes. Results: We redemonstrated a statistically significant difference in mortality between IE, AE, and CE patients, with CE patients demonstrating the highest mortality (40%), and AE patients the lowest mortality (5%, p = 0.032). A higher CE score was a predictor of mortality; coronary artery disease (CAD) and elevated CE scores were associated with an increase in mortality (CAD: HR = 12.3, 95% CI 1.5–101; CE score: HR = 15.5 95% CI 1.15–211). Kaplan–Meier (KM) analysis of the entire cohort (n = 51) demonstrated a decrease survival in the CE group, p = 0.026. KM survival analysis of hydrocortisone, ascorbic acid, and thiamine (HAT) therapy and control patients not receiving steroids (n = 45) showed CE and IE was associated with a decrease in survival (p = 0.003); of interest, there was no difference in survival in CE patients after stratifying by HAT therapy (p = 0.18). These findings suggest a possible treatment effect of corticosteroids, HAT therapy, endotype, and outcome. Conclusion: This subset of patients from the ORANGES trial confirmed previous retrospective findings that a 33-mRNA classifier can group patients into IE, AE, and CE endotypes having prognostic significance. A novel finding of this study identifying an association between endotype and corticosteroid therapy warrants further study in support of future diagnostic use of the endotyping classifier.

Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p < 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56^bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2.

A Multi-mRNA Host-Response Molecular Blood Test for the Diagnosis and Prognosis of Acute Infections and Sepsis: Proceedings from a Clinical Advisory Panel

Current diagnostics are insufficient for diagnosis and prognosis of acute infections and sepsis. Clinical decisions including prescription and timing of antibiotics, ordering of additional diagnostics and level-of-care decisions rely on understanding etiology and implications of a clinical presentation. Host mRNA signatures can differentiate infectious from noninfectious etiologies, bacterial from viral infections, and predict 30-day mortality. The 29-host-mRNA blood-based InSep^TM test (Inflammatix, Burlingame, CA, formerly known as HostDx^TM Sepsis) combines machine learning algorithms with a rapid point-of-care platform with less than 30 min turnaround time to enable rapid diagnosis of acute infections and sepsis, as well as prediction of disease severity. A scientific advisory panel including emergency medicine, infectious disease, intensive care and clinical pathology physicians discussed technical and clinical requirements in preparation of successful introduction of InSep into the market. Topics included intended use; patient populations of greatest need; patient journey and sample flow in the emergency department (ED) and beyond; clinical and biomarker-based decision algorithms; performance characteristics for clinical utility; assay and instrument requirements; and result readouts. The panel identified clear demand for a solution like InSep, requirements regarding test performance and interpretability, and a need for focused medical education due to the innovative but complex nature of the result readout. Innovative diagnostic solutions such as the InSep test could improve management of patients with suspected acute infections and sepsis in the ED, thereby lessening the overall burden of these conditions on patients and the healthcare system.

Cost Impact Model of a Novel Multi-mRNA Host Response Assay for Diagnosis and Risk Assessment of Acute Respiratory Tract Infections and Sepsis in the Emergency Department

BACKGROUND

Early identification of acute infections and sepsis remains an unmet medical need. While early detection and initiation of treatment reduces mortality, inappropriate treatment leads to adverse events and the development of antimicrobial resistance. Current diagnostic and prognostic solutions, including procalcitonin, lack required accuracy. A novel blood-based host response test, HostDx™ Sepsis by Inflammatix, Inc., assesses the likelihood of a bacterial infection, the likelihood of a viral infection, and the severity of the condition.

OBJECTIVES

We estimated the economic impact of adopting HostDx Sepsis testing among patients with suspected acute respiratory tract infection (ARTI) in the emergency department (ED).

METHODS

Our cost impact model estimated costs for adult ED patients with suspected ARTI under the standard of care versus with the adoption of HostDx Sepsis from the perspective of US payers. Included costs were those assumed to be associated with an episode of sepsis diagnosis, management, and treatment. Projected accuracies for test predictions, disease prevalence, and clinical parameters was derived from patient-level meta-analysis data of randomized trials, supplemented with published performance data for HostDx Sepsis. One-way sensitivity analysis was performed on key input parameters.

RESULTS

Compared to standard of care including procalcitonin, the superior test characteristics of HostDx Sepsis resulted in an average cost savings of approximately US$1974 per patient (-31.3%) exclusive of the cost of HostDx Sepsis. Reductions in hospital days (-0.80 days, -36.7%), antibiotic days (-1.49 days, -29.5%), and percent 30-day mortality (-1.67%, -13.64%) were driven by HostDx Sepsis providing fewer "noninformative" moderate risk predictions and more "certain" low- or high-risk predictions compared to standard of care, especially for patients who were not severely ill. These results were robust to changes in key parameters, including disease prevalence.

CONCLUSIONS

Our model shows substantial savings associated with introduction of HostDx Sepsis among patients with ARTIs in EDs. These results need confirmation in interventional trials.

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral infections. We use training data (N = 1069) from 18 retrospective transcriptomic studies. Using only 29 preselected host mRNAs, we train a neural-network classifier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95% CI 0.90–0.93) and a viral-vs-other AUROC 0.92 (95% CI 0.90–0.93). We then apply this classifier, inflammatix-bacterial-viral-noninfected-version 1 (IMX-BVN-1), without retraining, to an independent cohort (N = 163). In this cohort, IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.86 (95% CI 0.77–0.93), and viral-vs.-other 0.85 (95% CI 0.76–0.93). In patients enrolled within 36 h of hospital admission (N = 70), IMX-BVN-1 AUROCs are: bacterial-vs.-other 0.92 (95% CI 0.83–0.99), and viral-vs.-other 0.91 (95% CI 0.82–0.98). With further study, IMX-BVN-1 could provide a tool for assessing patients with suspected infection and sepsis at hospital admission.

Diagnosing acute infections based on transcriptional host response shows promise, but generalizability is wanting. Here, the authors use a co-normalization framework to train a classifier to diagnose acute infections and apply it to independent data on a targeted diagnostic platform.

649. Prospective Validation of an 11-mRNA Host Immune Signature as a Novel Blood Test for Acute Septic Arthritis

Background

Methods

Results

Conclusion

Disclosures

A 20-Gene Set Predictive of Progression to Severe Dengue

There is a need to identify biomarkers predictive of severe dengue. Single-cohort transcriptomics has not yielded generalizable results or parsimonious, predictive gene sets. We analyzed blood samples of dengue patients from seven gene expression datasets (446 samples, five countries) using an integrated multi-cohort analysis framework and identified a 20-gene set that predicts progression to severe dengue. We validated the predictive power of this 20-gene set in three retrospective dengue datasets (84 samples, three countries) and a prospective Colombia cohort (34 patients), with an area under the receiver operating characteristic curve of 0.89, 100% sensitivity, and 76% specificity. The 20-gene dengue severity scores declined during the disease course, suggesting an infection-triggered host response. This 20-gene set is strongly associated with the progression to severe dengue and represents a predictive signature, generalizable across ages, host genetic factors, and virus strains, with potential implications for the development of a host response-based dengue prognostic assay.

119. Prospective Validation of a 3-Gene Signature for Tuberculosis Diagnosis, Predicting Progression and Evaluating Treatment Response

Background

The World Health Organization (WHO) has identified the need for a nonsputum-based triage test for tuberculosis (TB) that can be used to identify those who need further testing to identify active disease. We investigated whether our previously described 3-gene TB score could identify individuals with active tuberculosis (ATB) prior to seeking care (“active case detection”) and how the 3-gene TB score correlated with the timing of disease onset, disease severity, and response to treatment.

Methods

This study consisted of a prospective nested case–control trial, Brazil Active Screening Study (BASS; 2016), and re-analysis of data from 2 prospective cohort studies, the Adolescent Cohort Study (ACS; 2005–2007), and the Catalysis Treatment Response Cohort (CTRC; 2010–2013). The BASS case–control subcohort contained 81 adults (ages 20–72 years, 33 ATB, 48 controls). The ACS contained 153 adolescents (ages 12–18 years, 46 ATB, 107 LTBI). The CTRC-contained 138 adults (ages 17–67 years, 100 ATB, 17 other lung disease patients, 21 healthy controls).

Results

The 3-gene TB score diagnosed ATB patients with high accuracy: BASS cohort AUC = 0.87 (95% CI = 0.82–0.91, Figure 1A), ACS cohort AUC = 0.86 (95% CI = 0.76–0.97, Figure 1B), and CTRC AUC = 0.93 (95% CI = 0.88–0.97). In the ACS, the 3-gene TB score predicted progression from LTBI to ATB 6 months prior to positive sputum test (AUC = 0.86; 95% CI = 0.79–0.92, Figure 1B). In the CTRC, the 3-gene TB score correlated with glycolytic activity ratio of PET-CT at baseline (correlation = 0.54, P = 3.98 × 10⁻⁸, Figure 1C) and at the end of treatment (correlation = −0.408, P = 3.72 × 10⁻⁵). In the CTRC, the 3-gene TB score at baseline predicted the likelihood of prolonged sputum positivity following treatment initiation and treatment response at 6 months (P = 3.6 × 10⁻⁵). Collectively, across all cohorts, the 3-gene TB score identified ATB patients with 90% sensitivity and 70% specificity, and had 99% negative predictive value (NPV) at 5% prevalence.

Conclusion

Across 3 independent prospective cohorts, the 3-gene TB score closely approaches the WHO target product profile benchmarks for non-sputum–based triage test at high NPV. These performance characteristics make it a potential test for ruling out ATB and for monitoring disease status.

Disclosures

T. E. Sweeney, Inflammatix, Inc.: Employee and Shareholder, Salary. P. Khatri, Inflammatix Inc.: Board Member, Equity

2565. A Novel Prognostic Gene Set for the Prediction of Severe Dengue

Background

There is an urgent need for the identification of biomarkers predictive of severe dengue. Single cohort transcriptomic studies have not yielded a parsimonious gene set predictive of severe dengue. We hypothesized that integration of gene expression data from heterogeneous patient populations with dengue infection would yield a set of conserved genes that is predictive of severe dengue and generalizable across cohorts.

Methods

Ten dengue gene expression datasets were identified in publicly available microarray repositories. A novel integrated multicohort platform was used to detect differentially expressed gene transcripts between uncomplicated and severe dengue patients and validate the identified putative signature in silico and prospectively in a new cohort of 34 dengue patients in Colombia. Dengue diagnosis was made by NS1 antigen and anti-DENV IgM antibody and confirmed by RT-PCR assays, ELISA, and IgG avidity measurements. The expression level of the signature genes was measured via microfluidic qRT-PCR assays in blood samples collected longitudinally during the course of illness.

Results

Using the multicohort analysis to analyze 446 peripheral blood samples of patients with dengue infection from 7 publicly available gene expression datasets, we identified a 20 gene set that predicts the development of severe dengue. We in silico validated the diagnostic power of this gene set to separate severe dengue from dengue with or without warning signs in 3 independent datasets composed of 84 samples with a global area under the ROC curve (AUC) of 0.80 [95% CI 0.68–0.88]. We prospectively validated the gene set in a new cohort composed of 34 dengue patients from Colombia with an AUC of 0.89 [95% CI 0.81–0.97]. The severity scores measured in patients with severe dengue progressively declined in longitudinal samples.

Conclusion

Our data indicate that the identified 20 gene signature predicts the development of severe dengue in patients prior to its onset and suggest that dengue infection itself triggers this host response. These findings may provide new insight into the pathogenesis of severe dengue and have implications for the development of a prognostic molecular assay to identify patients at risk to develop severe dengue.

Disclosures

T. E. Sweeney, Inflammatix, Inc.: Employee and Shareholder, Salary. P. Khatri, Inflammatix, Inc: Scientific Advisor and Shareholder, Licensing agreement or royalty.

Assessment of Validity of a Blood-Based 3-Gene Signature Score for Progression and Diagnosis of Tuberculosis, Disease Severity, and Treatment Response

IMPORTANCE

The World Health Organization identified the need for a non-sputum-based triage test to identify those in need of further tuberculosis (TB) testing.

OBJECTIVE

To determine whether the 3-gene TB score can be a diagnostic tool throughout the course of TB disease, from latency to diagnosis to treatment response, and posttreatment residual inflammation.

DESIGN, SETTING, AND PARTICIPANTS

This nested case-control study analyzed the 3-gene TB score in 3 cohorts, each focusing on a different stage of TB disease: (1) the Adolescent Cohort Study profiled whole-blood samples from adolescents with latent Mycobacterium tuberculosis infection, some of which progressed to active TB (ATB), using RNA sequencing; (2) the Brazil Active Screen Study collected whole blood from an actively screened case-control cohort of adult inmates from 2 prisons in Mato Grosso do Sul, Brazil, for ATB from January 2016 to February 2016; and (3) the Catalysis Treatment Response Cohort (CTRC) identified culture-positive adults in primary health care clinics in Cape Town, South Africa, from 2005 to 2007 and collected whole blood for RNA sequencing from patients with ATB at diagnosis and weeks 1, 4, and 24. The CTRC patients also had positron emission tomography-computed tomography scans at diagnosis, week 4, and week 24. Analyses were performed from September 2017 to June 2018.

MAIN OUTCOMES AND MEASURES

A 3-gene messenger RNA expression score, measured by quantitative polymerase chain reaction or RNA sequencing, was evaluated for distinguishing the following: individuals who progressed to ATB from those who did not, individuals with ATB from those without, and individuals with slower treatment response during TB therapy.

RESULTS

Patients evaluated in this study included 144 adolescents from the Adolescent Cohort Study (aged 12-18 years; 96 female and 48 male), 81 adult prison inmates from the Brazil Active Screen Study (aged 20-72 years; 81 male), and 138 adult community members from the CTRC (aged 17-64 years; 81 female and 57 male). The 3-gene TB score identified progression from latent M tuberculosis infection to ATB 6 months prior to sputum conversion with 86% sensitivity and 84% specificity (area under the curve [AUC], 0.86; 95% CI, 0.77-0.96) and patients with ATB in the Brazil Active Screen Study cohort (AUC, 0.87; 95% CI, 0.78-0.95) and CTRC (AUC, 0.94; 95% CI, 0.88-0.99). It also identified CTRC patients with failed treatment at the end of treatment (AUC, 0.93; 95% CI, 0.83-1.00). Collectively, across all cohorts, the 3-gene TB score identified patients with ATB with 90% sensitivity, 70% specificity, and 99.3% negative predictive value at 4% prevalence.

CONCLUSIONS AND RELEVANCE

Across 3 independent prospective cohorts, the 3-gene TB score approaches the World Health Organization target product profile benchmarks for non-sputum-based triage test with high negative predictive value. This gene expression diagnostic approach should be considered for further validation and future implementation.

Validation of the Sepsis MetaScore for Diagnosis of Neonatal Sepsis

WHAT’S KNOWN ON THIS SUBJECT

Neonates are at increased risk for developing sepsis, but this population often exhibits ambiguous clinical signs that complicate the diagnosis of infection. No biomarker has yet shown enough diagnostic accuracy to rule out sepsis at the time of clinical suspicion.

WHAT THIS STUDY ADDS

We show that a gene-expression-based signature is an accurate objective measure of the risk of sepsis in a neonate or preterm infant, and it substantially improves diagnostic accuracy over that of commonly used laboratory-based testing. Implementation might decrease inappropriate antibiotic use.

BACKGROUND

Neonatal sepsis can have devastating consequences, but accurate diagnosis is difficult. As a result, up to 200 neonates with suspected sepsis are treated with empiric antibiotics for every 1 case of microbiologically confirmed sepsis. These unnecessary antibiotics enhance bacterial antibiotic resistance, increase economic costs, and alter gut microbiota composition. We recently reported an 11-gene diagnostic test for sepsis (Sepsis MetaScore) based on host whole-blood gene expression in children and adults, but this test has not been evaluated in neonates.

METHODS

We identified existing gene expression microarray-based cohorts of neonates with sepsis. We then tested the accuracy of the Sepsis MetaScore both alone and in combination with standard diagnostic laboratory tests in diagnosing sepsis.

RESULTS

We found 3 cohorts with a total of 213 samples from control neonates and neonates with sepsis. The Sepsis MetaScore had an area under the receiver operating characteristic curve of 0.92-0.93 in all 3 cohorts. We also found that, as a diagnostic test for sepsis, it outperformed standard laboratory measurements alone and, when used in combination with another test(s), resulted in a significant net reclassification index (0.3-0.69) in 5 of 6 comparisons. The mean point estimates for sensitivity and specificity were 95% and 60%, respectively, which, if confirmed prospectively and applied in a high-risk cohort, could reduce inappropriate antibiotic usage substantially.

CONCLUSIONS

The Sepsis MetaScore had excellent diagnostic accuracy across 3 separate cohorts of neonates from 3 different countries. Further prospective targeted study will be needed before clinical application.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

Multicohort Analysis of Whole-Blood Gene Expression Data Does Not Form a Robust Diagnostic for Acute Respiratory Distress Syndrome

OBJECTIVES

To identify a novel, generalizable diagnostic for acute respiratory distress syndrome using whole-blood gene expression arrays from multiple acute respiratory distress syndrome cohorts of varying etiologies.

DATA SOURCES

We performed a systematic search for human whole-blood gene expression arrays of acute respiratory distress syndrome in National Institutes of Health Gene Expression Omnibus and ArrayExpress. We also included the Glue Grant gene expression cohorts.

STUDY SELECTION

We included investigator-defined acute respiratory distress syndrome within 48 hours of diagnosis and compared these with relevant critically ill controls.

DATA EXTRACTION

We used multicohort analysis of gene expression to identify genes significantly associated with acute respiratory distress syndrome, both with and without adjustment for clinical severity score. We performed gene ontology enrichment using Database for Annotation, Visualization and Integrated Discovery and cell type enrichment tests for both immune cells and pneumocyte gene expression. Finally, we selected a gene set optimized for diagnostic power across the datasets and used leave-one-dataset-out cross validation to assess robustness of the model.

DATA SYNTHESIS

We identified datasets from three adult cohorts with sepsis, one pediatric cohort with acute respiratory failure, and two datasets of adult patients with trauma and burns, for a total of 148 acute respiratory distress syndrome cases and 268 critically ill controls. We identified 30 genes that were significantly associated with acute respiratory distress syndrome (false discovery rate < 20% and effect size >1.3), many of which had been previously associated with sepsis. When metaregression was used to adjust for clinical severity scores, none of these genes remained significant. Cell type enrichment was notable for bands and neutrophils, suggesting that the gene expression signature is one of acute inflammation rather than lung injury per se. Finally, an attempt to develop a generalizable diagnostic gene set for acute respiratory distress syndrome showed a mean area under the receiver-operating characteristic curve of only 0.63 on leave-one-dataset-out cross validation.

CONCLUSIONS

The whole-blood gene expression signature across a wide clinical spectrum of acute respiratory distress syndrome is likely confounded by systemic inflammation, limiting the utility of whole-blood gene expression studies for uncovering a generalizable diagnostic gene signature.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

Robust classification of bacterial and viral infections via integrated host gene expression diagnostics

Improved diagnostics for acute infections could decrease morbidity and mortality by increasing early antibiotics for patients with bacterial infections and reducing unnecessary antibiotics for patients without bacterial infections. Several groups have used gene expression microarrays to build classifiers for acute infections, but these have been hampered by the size of the gene sets, use of overfit models, or lack of independent validation. We used multicohort analysis to derive a set of seven genes for robust discrimination of bacterial and viral infections, which we then validated in 30 independent cohorts. We next used our previously published 11-gene Sepsis MetaScore together with the new bacterial/viral classifier to build an integrated antibiotics decision model. In a pooled analysis of 1057 samples from 20 cohorts (excluding infants), the integrated antibiotics decision model had a sensitivity and specificity for bacterial infections of 94.0 and 59.8%, respectively (negative likelihood ratio, 0.10). Prospective clinical validation will be needed before these findings are implemented for patient care.

Pediatric Sepsis Endotypes Among Adults With Sepsis

OBJECTIVES

Recent transcriptomic studies describe two subgroups of adults with sepsis differentiated by a sepsis response signature. The implied biology and related clinical associations are comparable with recently reported pediatric sepsis endotypes, labeled "A" and "B." We classified adults with sepsis using the pediatric endotyping strategy and the sepsis response signature and determined how endotype assignment, sepsis response signature membership, and age interact with respect to mortality.

DESIGN

Retrospective analysis of publically available transcriptomic data representing critically ill adults with sepsis from which the sepsis response signature groups were derived and validated.

SETTING

Multiple ICUs.

PATIENTS

Adults with sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Transcriptomic data were conormalized into a single dataset yielding 549 unique cases with sepsis response signature assignments. Each subject was assigned to endotype A or B using the expression data for the 100 endotyping genes. There were 163 subjects (30%) assigned to endotype A and 386 to endotype B. There was a weak, positive correlation between endotype assignment and sepsis response signature membership. Mortality rates were similar between patients assigned endotype A and those assigned endotype B. A multivariable logistic regression model fit to endotype assignment, sepsis response signature membership, age, and the respective two-way interactions revealed that endotype A, sepsis response signature 1 membership, older age, and the interactions between them were associated with mortality. Subjects coassigned to endotype A, and sepsis response signature 1 had the highest mortality.

CONCLUSIONS

Combining the pediatric endotyping strategy with sepsis response signature membership might provide complementary, age-dependent, biological, and prognostic information.

Integration of Next–Generation Sequencing, Viral Sequencing, and Host-Response Profiling for the Diagnosis of Acute Infections

Background

To guide treatment of infectious diseases, clinicians need sensitive, specific, and rapid diagnostics. We aim to incorporate complementary methods of microbial sequencing and host-response profiling to improve the diagnosis of patients at risk for acute infections.

Methods

We enrolled 200 adult patients with systemic inflammatory response syndrome (SIRS) at the Stanford Emergency Department. Physicians with specialty training in infectious diseases conducted retrospective two-physician chart review to establish likely admission diagnoses. Blood samples were tested with a previously described 18-gene host-response integrated antibiotics decision model (IADM) that distinguishes noninfectious SIRS, bacterial infections and viral infections. Plasma samples were tested with shotgun metagenomic next-generation sequencing (NGS) and viral sequencing with VirCapSeq. A novel statistical algorithm was developed to identify contaminant organism sequences in NGS data.

Results

The physician chart review classified 99 patients (49%) as infected, 69 (35%) possibly infected and 32 (16%) non-infected. Compared with chart review, the IADM distinguished bacterial from viral infections with an area under curve of 0.85 (95% confidence interval 0.77–0.93). NGS results to date confirmed positive blood cultures in seven of nine patients, with two of four blood culture-positive E. coli patients turning up negative on NGS due to E. coli contamination. NGS also confirmed positive cultures from other sites in two of six patients with negative blood cultures. Preliminary VirCapSeq data from 23 patients confirmed positive viral tests in five of six patients with Hepatitis C, BK Virus, Cytomegalovirus and Epstein–Barr Virus infections. VirCapSeq did not identify a causative agent in the plasma of 11 patients with confirmed respiratory viral infection and intestinal Norovirus infection, and six patients with idiopathic illness. Interestingly, VirCapSeq found viral reactivation in 8 of 12 immunocompromised patients.

Conclusion

The diagnosis of suspected infections may be enhanced by integrating host-response and microbial data alongside clinical judgment. Our results and large cohort lay the foundation to demonstrate the utility of this approach and in which patients these tools may be most useful.

Disclosures

T. E. Sweeney, Inflammatix, Inc: Employee and Shareholder, Salary; T. Briese, Roche: Columbia University has licensed VirCapSeq to Roche, Licensing agreement or royalty; W. I. Lipkin, Roche: Columbia University has licensed VirCapSeq to Roche., Licensing agreement or royalty; P. Khatri, Inflammatix, Inc.: Co-founder, Scientific Advisor and Shareholder, Licensing agreement or royalty and ownership stock; D. A. Relman, Karius: Consultant, Stock options; Arc Bio LLC: Consultant, Stock options

Encouraging Mindfulness in Medical House Staff via Smartphone App: A Pilot Study

BACKGROUND

Stress and burnout are increasingly recognized as urgent issues among resident physicians, especially given the concerning implications of burnout on physician well-being and patient care outcomes.

OBJECTIVE

The authors assessed how a mindfulness and meditation practice among residents, supported via a self-guided, smartphone-based mindfulness app, affects wellness as measured by prevalidated surveys.

METHODS

Residents in the departments of general surgery, anesthesia, and obstetrics and gynecology were recruited for participation in this survey-based, four-week, single-arm study. All participants used the app (Headspace) on a self-guided basis, and took surveys at enrollment, at 2 weeks, and at 4 weeks. The Positive and Negative Affect Schedule (PANAS) assessed mood, and the Freiburg Mindfulness Inventory (FMI) measured mindfulness.

RESULTS

Forty-three residents enrolled in this study from April 2015 to August 2016; 30 residents (90% female) completed two or more surveys, and so were included for further analysis. In a comparison of baseline scores to week four scores, there was a significant increase in FMI at week four (36.88 ± 7.00; Cohen's d = 0.77, p = 0.005), a trend toward increase in the positive affect score (PAS) (31.73 ± 6.07; Cohen's d = 0.38, p = 0.08), and no change in negative affect score (NAS) (21.62 ± 7.85; Cohen's d = -0.15, p = NS). In mixed-effect multivariate modeling, both the PAS and the FMI scores showed significant positive change with increasing use of the smartphone app (PAS, 0.31 (95% CI 0.03-0.57); FMI, 0.38 (95% CI 0.11-0.66)), while the NAS did not show significant change.

CONCLUSIONS

Study limitations include self-guided app usage, a homogenous study subject population, insufficient study subjects to perform stratified analysis of the impact of specialty on the findings, lack of control group, and possible influence from the Hawthorne effect. This study suggests the feasibility and efficacy of a short mindfulness intervention delivered by a smartphone app to improve mindfulness and associated resident physician wellness parameters.

Gene Expression Analysis to Assess the Relevance of Rodent Models to Human Lung Injury

The relevance of animal models to human diseases is an area of intense scientific debate. The degree to which mouse models of lung injury recapitulate human lung injury has never been assessed. Integrating data from both human and animal expression studies allows for increased statistical power and identification of conserved differential gene expression across organisms and conditions. We sought comprehensive integration of gene expression data in experimental acute lung injury (ALI) in rodents compared with humans. We performed two separate gene expression multicohort analyses to determine differential gene expression in experimental animal and human lung injury. We used correlational and pathway analyses combined with external in vitro gene expression data to identify both potential drivers of underlying inflammation and therapeutic drug candidates. We identified 21 animal lung tissue datasets and three human lung injury bronchoalveolar lavage datasets. We show that the metasignatures of animal and human experimental ALI are significantly correlated despite these widely varying experimental conditions. The gene expression changes among mice and rats across diverse injury models (ozone, ventilator-induced lung injury, LPS) are significantly correlated with human models of lung injury (Pearson r = 0.33-0.45, P < 1E^-16). Neutrophil signatures are enriched in both animal and human lung injury. Predicted therapeutic targets, peptide ligand signatures, and pathway analyses are also all highly overlapping. Gene expression changes are similar in animal and human experimental ALI, and provide several physiologic and therapeutic insights to the disease.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Complement pathway amplifies caspase-11-dependent cell death and endotoxin-induced sepsis severity

Caspase-11–dependent cell death is controlled by carboxypeptidase B1 by inducing the cleavage of C3 and activation of C3aR.

Cell death and release of proinflammatory mediators contribute to mortality during sepsis. Specifically, caspase-11–dependent cell death contributes to pathology and decreases in survival time in sepsis models. Priming of the host cell, through TLR4 and interferon receptors, induces caspase-11 expression, and cytosolic LPS directly stimulates caspase-11 activation, promoting the release of proinflammatory cytokines through pyroptosis and caspase-1 activation. Using a CRISPR-Cas9–mediated genome-wide screen, we identified novel mediators of caspase-11–dependent cell death. We found a complement-related peptidase, carboxypeptidase B1 (Cpb1), to be required for caspase-11 gene expression and subsequent caspase-11–dependent cell death. Cpb1 modifies a cleavage product of C3, which binds to and activates C3aR, and then modulates innate immune signaling. We find the Cpb1–C3–C3aR pathway induces caspase-11 expression through amplification of MAPK activity downstream of TLR4 and Ifnar activation, and mediates severity of LPS-induced sepsis (endotoxemia) and disease outcome in mice. We show C3aR is required for up-regulation of caspase-11 orthologues, caspase-4 and -5, in primary human macrophages during inflammation and that c3aR1 and caspase-5 transcripts are highly expressed in patients with severe sepsis; thus, suggesting that these pathways are important in human sepsis. Our results highlight a novel role for complement and the Cpb1–C3–C3aR pathway in proinflammatory signaling, caspase-11 cell death, and sepsis severity.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.