Browsing by Author "Glickman, Seth W"
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Item Open Access An integrated transcriptome and expressed variant analysis of sepsis survival and death.(Genome Med, 2014) Tsalik, Ephraim L; Langley, Raymond J; Dinwiddie, Darrell L; Miller, Neil A; Yoo, Byunggil; van Velkinburgh, Jennifer C; Smith, Laurie D; Thiffault, Isabella; Jaehne, Anja K; Valente, Ashlee M; Henao, Ricardo; Yuan, Xin; Glickman, Seth W; Rice, Brandon J; McClain, Micah T; Carin, Lawrence; Corey, G Ralph; Ginsburg, Geoffrey S; Cairns, Charles B; Otero, Ronny M; Fowler, Vance G; Rivers, Emanuel P; Woods, Christopher W; Kingsmore, Stephen FBACKGROUND: Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality. METHODS: The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (SIRS due to infection), including 78 sepsis survivors and 28 sepsis non-survivors who had previously undergone plasma proteomic and metabolomic profiling. Gene expression differences were identified between sepsis survivors, sepsis non-survivors, and SIRS followed by gene enrichment pathway analysis. Expressed sequence variants were identified followed by testing for association with sepsis outcomes. RESULTS: The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflecting immune activation in sepsis. Expression of 1,238 genes differed with sepsis outcome: non-survivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease-rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors. The presence of variants was associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology. CONCLUSIONS: The activation of immune response-related genes seen in sepsis survivors was muted in sepsis non-survivors. The association of sepsis survival with a robust immune response and the presence of missense variants in VPS9D1 warrants replication and further functional studies. TRIAL REGISTRATION: ClinicalTrials.gov NCT00258869. Registered on 23 November 2005.Item Open Access Are patients with longer emergency department wait times less likely to consent to research?(Acad Emerg Med, 2012-04) Limkakeng, Alexander T; Glickman, Seth W; Shofer, Frances; Mani, Giselle; Drake, Weiying; Freeman, Debbie; Ascher, Simon; Pietrobon, Ricardo; Cairns, Charles BOBJECTIVES: There are unique challenges to enrolling patients in emergency department (ED) clinical research studies, including the time-sensitive nature of emergency conditions, the acute care environment, and the lack of an established relationship with patients. Prolonged ED wait times have been associated with a variety of adverse effects on patient care. The objective of this study was to assess the effect of ED wait times on patient participation in ED clinical research. The hypothesis was that increased ED wait times would be associated with reduced ED clinical research consent rates. METHODS: This was a retrospective cohort study of all patients eligible for two diagnostic clinical research studies from January 1, 2008, through December 31, 2008, in an urban academic ED. Sex, age, race, study eligibility, and research consent decisions were recorded by trained study personnel. The wait times to registration and to be seen by a physician were obtained from administrative databases and compared between consenters and nonconsenters. An analysis of association between patient wait times for the outcome of consent to participate was performed using a multivariate logistic regression model. RESULTS: A total of 903 patients were eligible for enrollment and were asked for consent. Overall, 589 eligible patients (65%) gave consent to research participation. The consent rates did not change when patients were stratified by the highest and lowest quartile wait times for both time from arrival to registration (68% vs. 65%, p = 0.35) and time to be seen by a physician (65% vs. 66%, p = 0.58). After adjusting for patient demographics (age, race, and sex) and study, there was still no relationship between wait times and consent (p > 0.4 for both wait times). Furthermore, median time from arrival to registration did not differ between those who consented to participate (15 minutes; interquartile range [IQR] = 9 to 36 minutes) versus those who did not (15.5 minutes; IQR = 10 to 39 minutes; p = 0.80; odds ratio [OR] = 1.00, 95% confidence interval [CI] = 0.99 to 1.01). Similarly, there was no difference in the median time to be seen by a physician between those who consented (25 minutes; IQR = 15 to 55 minutes) versus those who did not (25 minutes; IQR = 15 to 56 minutes; p = 0.70; OR = 1.00, 95% CI = 0.99 to 1.01). CONCLUSIONS: Regardless of wait times, nearly two-thirds of eligible patients were willing to consent to diagnostic research studies in the ED. These findings suggest that effective enrollment in clinical research is possible in the ED, despite challenges with prolonged wait times.Item Open Access Discriminating Bacterial and Viral Infection Using a Rapid Host Gene Expression Test.(Critical care medicine, 2021-10) Tsalik, Ephraim L; Henao, Ricardo; Montgomery, Jesse L; Nawrocki, Jeff W; Aydin, Mert; Lydon, Emily C; Ko, Emily R; Petzold, Elizabeth; Nicholson, Bradly P; Cairns, Charles B; Glickman, Seth W; Quackenbush, Eugenia; Kingsmore, Stephen F; Jaehne, Anja K; Rivers, Emanuel P; Langley, Raymond J; Fowler, Vance G; McClain, Micah T; Crisp, Robert J; Ginsburg, Geoffrey S; Burke, Thomas W; Hemmert, Andrew C; Woods, Christopher W; Antibacterial Resistance Leadership GroupObjectives
Host gene expression signatures discriminate bacterial and viral infection but have not been translated to a clinical test platform. This study enrolled an independent cohort of patients to describe and validate a first-in-class host response bacterial/viral test.Design
Subjects were recruited from 2006 to 2016. Enrollment blood samples were collected in an RNA preservative and banked for later testing. The reference standard was an expert panel clinical adjudication, which was blinded to gene expression and procalcitonin results.Setting
Four U.S. emergency departments.Patients
Six-hundred twenty-three subjects with acute respiratory illness or suspected sepsis.Interventions
Forty-five-transcript signature measured on the BioFire FilmArray System (BioFire Diagnostics, Salt Lake City, UT) in ~45 minutes.Measurements and main results
Host response bacterial/viral test performance characteristics were evaluated in 623 participants (mean age 46 yr; 45% male) with bacterial infection, viral infection, coinfection, or noninfectious illness. Performance of the host response bacterial/viral test was compared with procalcitonin. The test provided independent probabilities of bacterial and viral infection in ~45 minutes. In the 213-subject training cohort, the host response bacterial/viral test had an area under the curve for bacterial infection of 0.90 (95% CI, 0.84-0.94) and 0.92 (95% CI, 0.87-0.95) for viral infection. Independent validation in 209 subjects revealed similar performance with an area under the curve of 0.85 (95% CI, 0.78-0.90) for bacterial infection and 0.91 (95% CI, 0.85-0.94) for viral infection. The test had 80.1% (95% CI, 73.7-85.4%) average weighted accuracy for bacterial infection and 86.8% (95% CI, 81.8-90.8%) for viral infection in this validation cohort. This was significantly better than 68.7% (95% CI, 62.4-75.4%) observed for procalcitonin (p < 0.001). An additional cohort of 201 subjects with indeterminate phenotypes (coinfection or microbiology-negative infections) revealed similar performance.Conclusions
The host response bacterial/viral measured using the BioFire System rapidly and accurately discriminated bacterial and viral infection better than procalcitonin, which can help support more appropriate antibiotic use.Item Open Access Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.(PLoS One, 2013) Ahn, Sun Hee; Tsalik, Ephraim L; Cyr, Derek D; Zhang, Yurong; van Velkinburgh, Jennifer C; Langley, Raymond J; Glickman, Seth W; Cairns, Charles B; Zaas, Aimee K; Rivers, Emanuel P; Otero, Ronny M; Veldman, Tim; Kingsmore, Stephen F; Kingsmore, Stephen F; Lucas, Joseph; Woods, Christopher W; Ginsburg, Geoffrey S; Fowler, Vance GStaphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host's inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.Item Open Access Host gene expression classifiers diagnose acute respiratory illness etiology.(Sci Transl Med, 2016-01-20) Tsalik, Ephraim L; Henao, Ricardo; Nichols, Marshall; Burke, Thomas; Ko, Emily R; McClain, Micah T; Hudson, Lori L; Mazur, Anna; Freeman, Debra H; Veldman, Tim; Langley, Raymond J; Quackenbush, Eugenia B; Glickman, Seth W; Cairns, Charles B; Jaehne, Anja K; Rivers, Emanuel P; Otero, Ronny M; Zaas, Aimee K; Kingsmore, Stephen F; Lucas, Joseph; Fowler, Vance G; Carin, Lawrence; Ginsburg, Geoffrey S; Woods, Christopher WAcute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.Item Open Access Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients.(Ann Am Thorac Soc, 2016-03) Tsalik, Ephraim L; Li, Yanhong; Hudson, Lori L; Chu, Vivian H; Himmel, Tiffany; Limkakeng, Alex T; Katz, Jason N; Glickman, Seth W; McClain, Micah T; Welty-Wolf, Karen E; Fowler, Vance G; Ginsburg, Geoffrey S; Woods, Christopher W; Reed, Shelby DRATIONALE: Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections. OBJECTIVES: We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing. METHODS: The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d. MEASUREMENTS AND MAIN RESULTS: In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved. CONCLUSIONS: Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.Item Open Access Renal systems biology of patients with systemic inflammatory response syndrome.(Kidney Int, 2015-10) Tsalik, Ephraim L; Willig, Laurel K; Rice, Brandon J; van Velkinburgh, Jennifer C; Mohney, Robert P; McDunn, Jonathan E; Dinwiddie, Darrell L; Miller, Neil A; Mayer, Eric S; Glickman, Seth W; Jaehne, Anja K; Glew, Robert H; Sopori, Mohan L; Otero, Ronny M; Harrod, Kevin S; Cairns, Charles B; Fowler, Vance G; Rivers, Emanuel P; Woods, Christopher W; Kingsmore, Stephen F; Langley, Raymond JA systems biology approach was used to comprehensively examine the impact of renal disease and hemodialysis (HD) on patient response during critical illness. To achieve this, we examined the metabolome, proteome, and transcriptome of 150 patients with critical illness, stratified by renal function. Quantification of plasma metabolites indicated greater change as renal function declined, with the greatest derangements in patients receiving chronic HD. Specifically, 6 uremic retention molecules, 17 other protein catabolites, 7 modified nucleosides, and 7 pentose phosphate sugars increased as renal function declined, consistent with decreased excretion or increased catabolism of amino acids and ribonucleotides. Similarly, the proteome showed increased levels of low-molecular-weight proteins and acute-phase reactants. The transcriptome revealed a broad-based decrease in mRNA levels among patients on HD. Systems integration revealed an unrecognized association between plasma RNASE1 and several RNA catabolites and modified nucleosides. Further, allantoin, N1-methyl-4-pyridone-3-carboxamide, and N-acetylaspartate were inversely correlated with the majority of significantly downregulated genes. Thus, renal function broadly affected the plasma metabolome, proteome, and peripheral blood transcriptome during critical illness; changes were not effectively mitigated by hemodialysis. These studies allude to several novel mechanisms whereby renal dysfunction contributes to critical illness.Item Open Access Validation of a host response test to distinguish bacterial and viral respiratory infection.(EBioMedicine, 2019-10-17) Lydon, Emily C; Henao, Ricardo; Burke, Thomas W; Aydin, Mert; Nicholson, Bradly P; Glickman, Seth W; Fowler, Vance G; Quackenbush, Eugenia B; Cairns, Charles B; Kingsmore, Stephen F; Jaehne, Anja K; Rivers, Emanuel P; Langley, Raymond J; Petzold, Elizabeth; Ko, Emily R; McClain, Micah T; Ginsburg, Geoffrey S; Woods, Christopher W; Tsalik, Ephraim LBACKGROUND:Distinguishing bacterial and viral respiratory infections is challenging. Novel diagnostics based on differential host gene expression patterns are promising but have not been translated to a clinical platform nor extensively tested. Here, we validate a microarray-derived host response signature and explore performance in microbiology-negative and coinfection cases. METHODS:Subjects with acute respiratory illness were enrolled in participating emergency departments. Reference standard was an adjudicated diagnosis of bacterial infection, viral infection, both, or neither. An 87-transcript signature for distinguishing bacterial, viral, and noninfectious illness was measured from peripheral blood using RT-PCR. Performance characteristics were evaluated in subjects with confirmed bacterial, viral, or noninfectious illness. Subjects with bacterial-viral coinfection and microbiologically-negative suspected bacterial infection were also evaluated. Performance was compared to procalcitonin. FINDINGS:151 subjects with microbiologically confirmed, single-etiology illness were tested, yielding AUROCs 0•85-0•89 for bacterial, viral, and noninfectious illness. Accuracy was similar to procalcitonin (88% vs 83%, p = 0•23) for bacterial vs. non-bacterial infection. Whereas procalcitonin cannot distinguish viral from non-infectious illness, the RT-PCR test had 81% accuracy in making this determination. Bacterial-viral coinfection was subdivided. Among 19 subjects with bacterial superinfection, the RT-PCR test identified 95% as bacterial, compared to 68% with procalcitonin (p = 0•13). Among 12 subjects with bacterial infection superimposed on chronic viral infection, the RT-PCR test identified 83% as bacterial, identical to procalcitonin. 39 subjects had suspected bacterial infection; the RT-PCR test identified bacterial infection more frequently than procalcitonin (82% vs 64%, p = 0•02). INTERPRETATION:The RT-PCR test offered similar diagnostic performance to procalcitonin in some subgroups but offered better discrimination in others such as viral vs. non-infectious illness and bacterial/viral coinfection. Gene expression-based tests could impact decision-making for acute respiratory illness as well as a growing number of other infectious and non-infectious diseases.