Dataset Information

Molecular RNA Correlates of the SOFA Score in Patients with Sepsis.

ABSTRACT: The most common scoring system for critically ill patients is the Sequential Organ Failure Assessment (SOFA) score. Little is known about specific molecular signaling networks underlying the SOFA criteria. We characterized these networks and identified specific key regulatory molecules. We prospectively studied seven patients with sepsis and six controls with high-throughput RNA sequencing (RNAseq). Quantitative reverse transcription PCR (RT-qPCR) confirmation was performed in a second independent cohort. Differentially and significantly expressed miRNAs and their target mRNA transcripts were filtered for admission SOFA criteria and marker RNAs for the respective criteria identified. We bioinformatically constructed molecular signaling networks specifically reflecting these criteria followed by RT-qPCR confirmation of RNAs with important regulatory functions in the networks in the second cohort. RNAseq identified 82 miRNAs (45% upregulated) and 3254 mRNAs (50% upregulated) differentially expressed between sepsis patients and controls. Bioinformatic analysis characterized 6 miRNAs and 76 mRNA target transcripts specific for the SOFA criteria. RT-qPCR validated miRNA and mRNAs included IGFBP2 (respiratory system); MMP9 and PDE4B (nervous system); PPARG (cardiovascular system); AKR1B1, ANXA1, and LNC2/NGAL (acute kidney injury); GFER/ALR (liver); and miR-30c-3p (coagulopathy). There are specific canonical networks underlying the SOFA score. Key regulatory miRNA and mRNA transcripts support its biologic validity.

SUBMITTER: Meidert AS

PROVIDER: S-EPMC8468706 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Molecular RNA Correlates of the SOFA Score in Patients with Sepsis.

Meidert Agnes S AS Buschmann Dominik D Brandes Florian F Kanev Kristiyan K Billaud Jean-Noël JN Borrmann Melanie M Witte Matthias M Kirchner Benedikt B Reithmair Marlene M Pfaffl Michael W MW Schelling Gustav G

Diagnostics (Basel, Switzerland) 20210909 9

The most common scoring system for critically ill patients is the Sequential Organ Failure Assessment (SOFA) score. Little is known about specific molecular signaling networks underlying the SOFA criteria. We characterized these networks and identified specific key regulatory molecules. We prospectively studied seven patients with sepsis and six controls with high-throughput RNA sequencing (RNAseq). Quantitative reverse transcription PCR (RT-qPCR) confirmation was performed in a second independe ...[more]

PMID: 34573990

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Project description:IntroductionSepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance.MethodsPatients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models.ResultA total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5-50%, LY and GNB models both showed positive net benefits.ConclusionThe two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.

Project description:ObjectivesTo compare the accuracy of the Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) Scores in predicting mortality among intensive care unit (ICU) patients with sepsis in a low-income and middle-income country.DesignA multicentre, cross-sectional study.SettingA total of 15 adult ICUs throughout Vietnam.ParticipantsWe included all patients aged ≥18 years who were admitted to ICUs for sepsis and who were still in ICUs from 00:00 to 23:59 of the specified study days (ie, 9 January, 3 April, 3 July and 9 October of the year 2019).Primary and secondary outcome measuresThe primary outcome was hospital all-cause mortality (hospital mortality). We also defined the secondary outcome as all-cause deaths in the ICU (ICU mortality).ResultsOf 252 patients, 40.1% died in hospitals, and 33.3% died in ICUs. SOFA Score (areas under the receiver operating characteristic curve (AUROC): 0.688 (95% CI 0.618 to 0.758); cut-off value≥7.5; PAUROC<0.001) and APACHE II Score (AUROC: 0.689 (95% CI 0.622 to 0.756); cut-off value ≥20.5; PAUROC<0.001) both had a poor discriminatory ability for predicting hospital mortality. However, the discriminatory ability for predicting ICU mortality of SOFA (AUROC: 0.713 (95% CI 0.643 to 0.783); cut-off value≥9.5; PAUROC<0.001) was fair and was better than that of APACHE II Score (AUROC: 0.672 (95% CI 0.603 to 0.742); cut-off value≥18.5; PAUROC<0.001). A SOFA Score≥8 (adjusted OR (AOR): 2.717; 95% CI 1.371 to 5.382) and an APACHE II Score≥21 (AOR: 2.668; 95% CI 1.338 to 5.321) were independently associated with an increased risk of hospital mortality. Additionally, a SOFA Score≥10 (AOR: 2.194; 95% CI 1.017 to 4.735) was an independent predictor of ICU mortality, in contrast to an APACHE II Score≥19, for which this role did not.ConclusionsIn this study, SOFA and APACHE II Scores were worthwhile in predicting mortality among ICU patients with sepsis. However, due to better discrimination for predicting ICU mortality, the SOFA Score was preferable to the APACHE II Score in predicting mortality.Clinical trials registry - India: CTRI/2019/01/016898.

Dataset Information

Molecular RNA Correlates of the SOFA Score in Patients with Sepsis.

Publications

Molecular RNA Correlates of the SOFA Score in Patients with Sepsis.

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