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

Brakenridge, Scott C.; Chen, Uan-I; Loftus, Tyler J.; Ungaro, Ricardo; Dirain, Marvin; Kerr, Austin W; Zhong, Luer; Bacher, Rhonda; Starostik, Petr; Ghita, Gabriella; Midic, Uros; Darden, Dijoia B; Fenner, Brittany; Wacker, James; Efron, Philip A.; Liesenfeld, Oliver; Sweeney, Timothy E.; Moldawer, Lyle L.

doi:10.1001/jamanetworkopen.2022.21520

Cited by 13 publications

(18 citation statements)

References 26 publications

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“…Study Designs: This study performed transcriptomic analyses on samples from two single-site, prospective, observational cohorts that enrolled a total of 522 patients admitted to non-cardiac, surgical ICUs and were classi ed as either (1) critically ill patients with a diagnosis of sepsis (Septic), or (2) non-septic critically-ill patients, at high risk for subsequently developing sepsis (At-Risk or Non-Septic; Fig. 1) [25,26].…”

Section: Methodsmentioning

confidence: 99%

“…The second prospective diagnostic and prognostic study (INF-06) was conducted between July 2020 and July 2021 [25] and enrolled two cohorts of critically ill patients at the time of surgical ICU admission (NCT04414189). One cohort included patients with a suspected diagnosis of sepsis admitted to the ICU for protocolized sepsis management, as in previously described study.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we apply 29-and 33-gene transcriptomic signatures to simultaneously classify sepsis severity and endotype, respectively [23,[25][26][27]. These transcriptomic signatures were originally validated in non-surgical patients with bacterial or viral sepsis, and were classi ed into adaptative, in ammopathic, and coagulopathic endotypes based on gene ontology analysis [23].…”

Section: Introductionmentioning

confidence: 99%

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

Balch

Chen²,

Liesenfeld³

et al. 2023

Preprint

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Balch

Chen²,

Liesenfeld³

et al. 2023

Preprint

Self Cite

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“…A combination of three non-overlapping signatures identified from a multi-cohort analysis (Sweeney et al, 2015;Sweeney et al, 2018b) has led to TriVerity (formerly known as InSepTM HostDxTMSepsis and Inflammatix) (Mayhew et al, 2020). This 29-gene expression-based test with a turnaround time Frontiers in Genetics frontiersin.org 10 less than 30 min is expected to identify the presence, type (bacterial or viral) and risk of mortality of infection (Mayhew et al, 2020;Bauer et al, 2021;Safarika et al, 2021;Brakenridge et al, 2022;Galtung et al, 2022). A Point-of-Care Test claiming to distinguish bacterial from viral infections in children is in its infancy (Pennisi et al, 2021).…”

Section: Biomarkers For Sepsis In the Pipelinementioning

confidence: 99%

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Tsakiroglou

Evans²,

Pirmohamed³

2023

Front. Genet.

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Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

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“…Identification of sepsis remains a major challenge to implement prompt treatment in surgical patients suffering from this condition. Correct and quick discrimination between sepsis and surgical related inflammation allows to early implement measures aimed to control the infection source with surgery or antibiotics (1,2). Biomarkers are a potential useful tool to improve sepsis detection, complementary to clinical information and to image and/or microbiological tests, but the information regarding biomarkers must be provided in minutes in order to be useful (3,4) .…”

Section: Introductionmentioning

confidence: 99%

Biomarkers’ performance in the SEPSIS-3 era

Fuente

López‐Sánchez

Vaquero‐Roncero

et al. 2023

Preprint

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Objective: the biomarkers' performance for diagnosis and severity stratification of sepsis has not been properly evaluated anew using the SEPSIS-3 criteria introduced in 2016. We evaluated the accuracy of 21 biomarkers classically tested in sepsis research to identify infection, sepsis, and septic shock in surgical patients classified using SEPSIS-3. Methods: four groups of adult surgical patients were compared: post-surgical patients with no infection, patients with infection but no sepsis, patients with sepsis, and patients with septic shock were recruited prospectively from the surgery departments and surgical ICUs from four Spanish hospital. The area under the curve (AUC) to differentiate between groups was calculated for each biomarker. Results: A total of 187 patients were recruited (50 uninfected post-surgery controls, 50 patients with infection, 47 with sepsis and 40 with septic shock). The AUCs indicated that none of the biomarkers tested was accurate enough to differentiate those patients with infection from the uninfected controls. In contrast, procalcitonin, lipocalin 2, pentraxin 3, IL-15, TNF-α, IL-6, angiopoietin 2, TREM-1, D-dimer and C-reactive protein yielded AUCs > 0.80 to discriminate the patients with sepsis or septic shock from those with no infection. C-reactive protein and IL-6 were the most accurate markers to differentiate plain infection from sepsis (AUC = 0.82). Finally, our results revealed that sepsis and septic shock shared similar profiles of biomarkers. Conclusion: Revaluation in the SEPSIS-3 era identified the scenarios where biomarkers do and do not provide useful information to improve the management of surgical patients with infection or sepsis.

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

Cited by 13 publications

References 26 publications

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

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

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Biomarkers’ performance in the SEPSIS-3 era

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