Early Differentiation Between Sepsis and Sterile Inflammation via Urinary Gene Signatures of Metabolic Dysregulation

Bandyopadhyay, Sanjoy; Loftus, Tyler J.; Peng, Ying-Chih; Lopez, Maria-Cecilia; Baker, Henry V.; Segal, Mark S.; Graim, Kiley; Ozrazgat‐Baslanti, Tezcan; Rashidi, Parisa

doi:10.1097/shk.0000000000001952

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…Furthermore, given the significant variation in inciting cause of sepsis among patients, changes in host immune response, and those in response to therapeutic intervention, it is unsurprising that gene-expression profiles among patients change over time. Complicating matters further, the true onset of sepsis is imperceptible and can be substantially delayed among some patients (26,27). It is thus plausible that the lack of consensus to determining gene-expression signatures predictive of sepsis mortality in part stems from these critical issues related to timing and sampling differences between studies (28).…”

Section: Discussionmentioning

confidence: 99%

A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

Huang,

Atreya,

Holder

et al. 2023

Shock

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Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units (ICU) and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the ICU to elucidate temporally stable gene expression markers between sepsis survivors and non-survivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine (SVM) classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients’ transcriptome was sampled at alternate time points, with an area under the curve (AUC) of 0.89 (95% CI: 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1 that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

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Section: Discussionmentioning

confidence: 99%

A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

Huang,

Atreya,

Holder

et al. 2023

Shock

View full text Add to dashboard Cite

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“…Similarly, elevated biomarkers of anti-angiogenesis (i.e., Ang2, and sFlt-1) were observed. We also reported on a whole-genome analyses of RNA isolated from the urinary cells within 12 hours of sepsis onset 48,49 . A set of 239 genes was identified that shows excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts.…”

Section: Older Sepsis Patientsmentioning

confidence: 99%

“…We also reported on a whole-genome analyses of RNA isolated from the urinary cells within 12 hours of sepsis onset. 48,49 A set of 239 genes was identified that shows excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes revealed disrupted biological pathways in early sepsis and key molecular networks driving its pathogenesis.…”

Section: Acute Kidney Injurymentioning

confidence: 99%

The persistent inflammation, immunosuppression, and catabolism syndrome 10 years later

Efron¹,

Brakenridge²,

Mohr³

et al. 2023

J Trauma Acute Care Surg

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M ultiple organ failure (MOF) emerged as a deadly syndrome in surgical intensive care units (ICUs) in the early 1970s. Sepsis and trauma were the primary inciting events. With tremendous the advances in care over the ensuing four decades, the epidemiology of MOF evolved from a fulminant phenotype of progressive organ failure leading to early death into a lingering phenotype of chronic critical illness (CCI) leading to indolent death. 1,2 CCI was first described in a 1985 article entitled "to save or let die." 3 This was followed by reports in the 1990s describing CCI in ventilator-dependent patients who were discharged to long-term acute care (LTAC) facilities for ventilator weaning. 4 These reports focused on long-term functional disability using descriptive terms including "polyneuropathy of critical illness," "myopathy of critical illness," and "ICU-acquired weakness." 5 It was subsequently recognized that CCI affected other systems. 6 Most recently, the CCI literature has popularized the term "postintensive care unit syndrome," adding that ICU delirium contributes to long-term cognitive impairments with depression and posttraumatic stress disorders. 7 These reports have largely come from heterogeneous medical ICU patients and implicate different risk factors depending upon the patient population, but offer no unifying underlying pathobiology for CCI.In a 2012 review article, the University of Florida Sepsis Critical Illness Research Center coined the term persistent inflammation, immunosuppression, and catabolism syndrome (PICS) to describe underlying pathobiology of the CCI phenotype that is now commonly seen in surgical ICU survivors. 8 This term was proposed to provide a mechanistic paradigm in which to study CCI in surgical ICU patients who are now surviving previously lethal inflammatory insults (e.g., trauma, sepsis, burns, and pancreatitis). The purpose of this review article is to summarize the shift from MOF into PICS-CCI in surgical patients.

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Early Differentiation Between Sepsis and Sterile Inflammation via Urinary Gene Signatures of Metabolic Dysregulation

Cited by 2 publications

References 39 publications

A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

A machine learning model derived from analysis of time-course gene-expression datasets reveal temporally stable gene markers predictive of sepsis mortality

The persistent inflammation, immunosuppression, and catabolism syndrome 10 years later

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