Scoping review on diagnostic criteria and investigative approach in sepsis of unknown origin in critically ill patients.
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ABSTRACT: Background
Up to 11% of critically ill patients with sepsis have an unknown source, where the pathogen and site of infection are unclear. The aim of this scoping review is to document currently reported diagnostic criteria of sepsis of unknown origin (SUO) and identify the types and breadth of existing evidence supporting diagnostic processes to identify the infection source in critically ill patients with suspected SUO.Methods
A literature search of Embase, MEDLINE and PubMed for published studies from 1910 to August 19, 2021 addressing the topic of SUO was performed. Study type, country of origin according to World Bank classification, diagnostic criteria of sepsis of unknown origin, and investigative approaches were extracted from the studies.Results
From an initial 722 studies, 89 unique publications fulfilled the inclusion and exclusion criteria and were included for full text review. The most common publication type was case report/series 45/89 (51%). Only 10/89 (11%) of studies provided a diagnostic criteria of SUO, but a universally accepted diagnostic criterion was not identified. The included studies discussed 30/89 (34%) history, 23/89 (26%) examination, 57/89 (64%) imaging, microbiology 39/89 (44%), and special tests 32/89 (36%) as part of the diagnostic processes in patients with SUO.Conclusions
Universally accepted diagnostic criteria for SUO was not found. Prospective studies on investigative processes in critically ill patients managed as SUO across different healthcare settings are needed to understand the epidemiology and inform the diagnostic criteria required to diagnose SUO.
SUBMITTER: Ling L
PROVIDER: S-EPMC9465866 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Scoping review on diagnostic criteria and investigative approach in sepsis of unknown origin in critically ill patients.
Ling Lowell L Mui Oliver Oi Yat OOY Laupland Kevin B KB Lefrant Jean-Yves JY Roberts Jason A JA Gopalan Pragasan Dean PD Lipman Jeffrey J Joynt Gavin M GM
Journal of intensive care 20220911 1
<h4>Background</h4>Up to 11% of critically ill patients with sepsis have an unknown source, where the pathogen and site of infection are unclear. The aim of this scoping review is to document currently reported diagnostic criteria of sepsis of unknown origin (SUO) and identify the types and breadth of existing evidence supporting diagnostic processes to identify the infection source in critically ill patients with suspected SUO.<h4>Methods</h4>A literature search of Embase, MEDLINE and PubMed fo ...[more]
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Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator
2015-10-24 | E-GEOD-74224 | biostudies-arrayexpress
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
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2015-10-24 | GSE74224 | GEO
Project description:IntroductionInvasive pulmonary aspergillosis (IPA) is a common infection in intensive care units (ICUs). There are no consensus criteria for defining IPA in the ICU. We aimed to compare the diagnosis and prognosis performances of three criteria (the 2020 EORTC/MSG criteria, the 2021 EORTC/MSG ICU criteria, the modified AspICU criteria (M-AspICU)) for IPA in the ICU.MethodsIn this retrospective study from our single center, we applied the three different criteria for IPA in patients with suspected pneumonia and undergoing at least one mycological test between November 10, 2016 and November 10, 2021. We compared the diagnosis agreement and prognosis performances of these three criteria in the ICU.ResultsOverall, 2403 patients were included. The rates of IPA according to the 2020 EORTC/MSG, 2021 EORTC/MSG ICU, and M-AspICU were 3.37%, 6.53%, and 23.10%, respectively. Diagnostic agreement among these criteria was poor (Cohen's kappa 0.208-0.666). IPA diagnosed by either the 2020 EORTC/MSG (odds ratio = 2.709, P < 0.001) or the 2021 EORTC/MSG ICU (odds ratio = 2.086, P = 0.001) criteria was independently associated with 28-day mortality. IPA diagnosed by M-AspICU is an independent risk factor of 28-day mortality (odds ratio = 1.431, P = 0.031) when excluding patients who fulfilled neither host criteria nor radiological factors of 2021 EORTC/MSG ICU.ConclusionsAlthough M-AspICU criteria have the highest "sensitivity", IPA diagnosed by M-AspICU was not an independent risk factor of 28-day mortality. Caution is required when using the M-AspICU criteria in ICU, especially in patients with non-specific infiltration and non-classical host factors.
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Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. Keywords: disease state analysis, gram-positive sepsis, gram-negative sepsis
2007-12-01 | GSE6535 | GEO
Project description:BackgroundSepsis-3 definition uses SOFA score to discriminate sepsis from uncomplicated infection, replacing SIRS criteria that were criticized for being inaccurate. Eligibility of sepsis-3 criteria for sepsis diagnosis and the applied validation methodology using mortality as endpoint are topic of ongoing debate. We assessed the impact of different criteria on sepsis diagnosis in our ICU and devised a mathematical approach for mortality-based validation of sepsis criteria. As infectious status is often unclear at clinical deterioration, we integrated non-infected patients into analysis.MethodsSuspected infection, SOFA and SIRS were captured for an ICU cohort of a university center over one year. For raw scores (SIRS/SOFA) and sepsis criteria (SIRS≥2/SOFA≥2/SOFA_change≥2) frequencies and associations with in-hospital mortality were assessed. Using a mathematical approach, we estimated the correlation between sepsis and in-hospital mortality serving as reference for evaluation of observed mortality correlations of sepsis criteria.ResultsOf 791 patients, 369 (47%) were infected and 422 (53%) non-infected, with an in-hospital mortality of 39% and 15%. SIRS≥2 indicated sepsis in 90% of infected patients, SOFA≥2 in 99% and SOFA_change≥2 in 77%. In non-infected patients, SIRS, SOFA and SOFA_change were ≥2 in 78%, 88% and 58%. In AUROC analyses neither SOFA nor SIRS displayed superior mortality discrimination in infected compared to non-infected patients. The mathematically estimated correlation of sepsis and in-hospital mortality was 0.10 in infected and 0 in non-infected patients. Among sepsis criteria, solely SIRS≥2 agreed with expected correlations in both subgroups (infected: r = 0.19; non-infected: r = 0.02).ConclusionsSOFA≥2 yielded a more liberal sepsis diagnosis than SIRS≥2. None of the criteria showed an infection specific occurrence that would be essential for reliable sepsis detection. However, SIRS≥2 matched the mortality association pattern of a valid sepsis criterion, whereas SOFA-based criteria did not. With this study, we establish a mathematical approach to mortality-based evaluation of sepsis criteria.
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Project description:IntroductionThe COVID-19 pandemic required careful management of intensive care unit (ICU) admissions, to reduce ICU overload while facing limitations in resources. We implemented a standardized, physiology-based, ICU admission criteria and analyzed the mortality rate of patients refused from the ICU.Materials and methodsIn this retrospective observational study, COVID-19 patients proposed for ICU admission were consecutively analyzed; Do-Not-Resuscitate patients were excluded. Patients presenting an oxygen peripheral saturation (SpO2) lower than 85% and/or dyspnea and/or mental confusion resulted eligible for ICU admission; patients not presenting these criteria remained in the ward with an intensive monitoring protocol. Primary outcome was both groups' survival rate. Secondary outcome was a sub analysis correlating SpO2 cutoff with ICU admission.ResultsFrom March 2020 to January 2021, 1623 patients were admitted to our Center; 208 DNR patients were excluded; 97 patients were evaluated. The ICU-admitted group (n = 63) mortality rate resulted 15.9% at 28 days and 27% at 40 days; the ICU-refused group (n = 34) mortality rate resulted 0% at both intervals (p < 0.001). With a SpO2 cut-off of 85%, a significant correlation was found (p = 0.009), but with a 92% a cut-off there was no correlation with ICU admission (p = 0.26). A similar correlation was also found with dyspnea (p = 0.0002).ConclusionIn COVID-19 patients, standardized ICU admission criteria appeared to safely reduce ICU overload. In the absence of dyspnea and/or confusion, a SpO2 cutoff up to 85% for ICU admission was not burdened by negative outcomes. In a pandemic context, the SpO2 cutoff of 92%, as a threshold for ICU admission, needs critical re-evaluation.
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Project description:BackgroundHemophagocytic lymphohistiocytosis (HLH) is a rare though often fatal hyperinflammatory syndrome mimicking sepsis in the critically ill. Diagnosis relies on the HLH-2004 criteria and HScore, both of which have been developed in pediatric or adult non-critically ill patients, respectively. Therefore, we aimed to determine the sensitivity and specificity of HLH-2004 criteria and HScore in a cohort of adult critically ill patients.MethodsIn this further analysis of a retrospective observational study, patients ≥ 18 years admitted to at least one adult ICU at Charité - Universitätsmedizin Berlin between January 2006 and August 2018 with hyperferritinemia of ≥ 500 μg/L were included. Patients' charts were reviewed for clinically diagnosed or suspected HLH. Receiver operating characteristics (ROC) analysis was performed to determine prediction accuracy.ResultsIn total, 2623 patients with hyperferritinemia were included, of whom 40 patients had HLH. We found the best prediction accuracy of HLH diagnosis for a cutoff of 4 fulfilled HLH-2004 criteria (95.0% sensitivity and 93.6% specificity) and HScore cutoff of 168 (100% sensitivity and 94.1% specificity). By adjusting HLH-2004 criteria cutoffs of both hyperferritinemia to 3000 μg/L and fever to 38.2 °C, sensitivity and specificity increased to 97.5% and 96.1%, respectively. Both a higher number of fulfilled HLH-2004 criteria [OR 1.513 (95% CI 1.372-1.667); p < 0.001] and a higher HScore [OR 1.011 (95% CI 1.009-1.013); p < 0.001] were significantly associated with in-hospital mortality.ConclusionsAn HScore cutoff of 168 revealed a sensitivity of 100% and a specificity of 94.1%, thereby providing slightly superior diagnostic accuracy compared to HLH-2004 criteria. Both HLH-2004 criteria and HScore proved to be of good diagnostic accuracy and consequently might be used for HLH diagnosis in critically ill patients.Clinical trial registrationThe study was registered with www.ClinicalTrials.gov (NCT02854943) on August 1, 2016.
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Project description:IntroductionRecently, the Kidney Disease: Improving Global Outcomes (KDIGO) proposed a new definition and classification of acute kidney injury (AKI) on the basis of the RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage renal failure) and AKIN (Acute Kidney Injury Network) criteria, but comparisons of the three criteria in critically ill patients are rare.MethodsWe prospectively analyzed a clinical database of 3,107 adult patients who were consecutively admitted to one of 30 intensive care units of 28 tertiary hospitals in Beijing from 1 March to 31 August 2012. AKI was defined by the RIFLE, AKIN, and KDIGO criteria. Receiver operating curves were used to compare the predictive ability for mortality, and logistic regression analysis was used for the calculation of odds ratios and 95% confidence intervals.ResultsThe rates of incidence of AKI using the RIFLE, AKIN, and KDIGO criteria were 46.9%, 38.4%, and 51%, respectively. KDIGO identified more patients than did RIFLE (51% versus 46.9%, P = 0.001) and AKIN (51% versus 38.4%, P <0.001). Compared with patients without AKI, in-hospital mortality was significantly higher for those diagnosed as AKI by using the RIFLE (27.8% versus 7%, P <0.001), AKIN (32.2% versus 7.1%, P <0.001), and KDIGO (27.4% versus 5.6%, P <0.001) criteria, respectively. There was no difference in AKI-related mortality between RIFLE and KDIGO (27.8% versus 27.4%, P = 0.815), but there was significant difference between AKIN and KDIGO (32.2% versus 27.4%, P = 0.006). The areas under the receiver operator characteristic curve for in-hospital mortality were 0.738 (P <0.001) for RIFLE, 0.746 (P <0.001) for AKIN, and 0.757 (P <0.001) for KDIGO. KDIGO was more predictive than RIFLE for in-hospital mortality (P <0.001), but there was no difference between KDIGO and AKIN (P = 0.12).ConclusionsA higher incidence of AKI was diagnosed according to KDIGO criteria. Patients diagnosed as AKI had a significantly higher in-hospital mortality than non-AKI patients, no matter which criteria were used. Compared with the RIFLE criteria, KDIGO was more predictive for in-hospital mortality, but there was no significant difference between AKIN and KDIGO.
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