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Scores for sepsis detection and risk stratification - construction of a novel score using a statistical approach and validation of RETTS.

ABSTRACT: BACKGROUND:To allow early identification of patients at risk of sepsis in the emergency department (ED), a variety of risk stratification scores and/or triage systems are used. The first aim of this study was to develop a risk stratification score for sepsis based upon vital signs and biomarkers using a statistical approach. Second, we aimed to validate the Rapid Emergency Triage and Treatment System (RETTS) for sepsis. RETTS combines vital signs with symptoms for risk stratification. METHODS:We retrospectively analysed data from two prospective, observational, multicentre cohorts of patients from studies of biomarkers in ED. A candidate risk stratification score called Sepsis Heparin-binding protein-based Early Warning Score (SHEWS) was constructed using the Least Absolute Shrinkage and Selector Operator (LASSO) method. SHEWS and RETTS were compared to National Early Warning Score 2 (NEWS2) for infection-related organ dysfunction, intensive care or death within the first 72h after admission (i.e. sepsis). RESULTS:506 patients with a diagnosed infection constituted cohort A, in which SHEWS was derived and RETTS was validated. 435 patients constituted cohort B of whom 184 had a diagnosed infection where both scores were validated. In both cohorts (A and B), AUC for infection-related organ dysfunction, intensive care or death was higher for NEWS2, 0.80 (95% CI 0.76-0.84) and 0.69 (95% CI 0.63-0.74), than RETTS, 0.74 (95% CI 0.70-0.79) and 0.55 (95% CI 0.49-0.60), p = 0.05 and p <0.01, respectively. SHEWS had the highest AUC, 0.73 (95% CI 0.68-0.79) p = 0.32 in cohort B. CONCLUSIONS:Even with a statistical approach, we could not construct better risk stratification scores for sepsis than NEWS2. RETTS was inferior to NEWS2 for screening for sepsis.

SUBMITTER: Mellhammar L

PROVIDER: S-EPMC7032705 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Scores for sepsis detection and risk stratification - construction of a novel score using a statistical approach and validation of RETTS.

Mellhammar Lisa L Linder Adam A Tverring Jonas J Christensson Bertil B Boyd John H JH Åkesson Per P Kahn Fredrik F

PloS one 20200220 2

<h4>Background</h4>To allow early identification of patients at risk of sepsis in the emergency department (ED), a variety of risk stratification scores and/or triage systems are used. The first aim of this study was to develop a risk stratification score for sepsis based upon vital signs and biomarkers using a statistical approach. Second, we aimed to validate the Rapid Emergency Triage and Treatment System (RETTS) for sepsis. RETTS combines vital signs with symptoms for risk stratification.<h4 ...[more]

PMID: 32078640

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Project description:BackgroundPatients suffering from major trauma often experience complications such as sepsis. The early recognition of patients at high risk of sepsis after trauma is critical for precision therapy. We aimed to derive and validate a novel predictive score for sepsis risk using electronic medical record (EMR) data following trauma.Materials and methodsClinical and laboratory variables of 684 trauma patients within 24 h after admission were collected, including 411 patients in the training cohort and 273 in the validation cohort. The least absolute shrinkage and selection operator (LASSO) technique was adopted to identify variables contributing to the early prediction of traumatic sepsis. Then, we constructed a traumatic sepsis score (TSS) using a logistic regression model based on the variables selected in the LASSO analysis. Moreover, we evaluated the discrimination and calibration of the TSS using the area under the curve (AUC) and the Hosmer-Lemeshow (H-L) goodness-of-fit test.ResultsBased on the LASSO, seven variables (injury severity score, Glasgow Coma Scale, temperature, heart rate, albumin, international normalized ratio, and C-reaction protein) were selected for construction of the TSS. Our results indicated that the incidence of sepsis after trauma increased with an increasing TSS (P trend = 7.44 × 10-21 for the training cohort and P trend = 1.16 × 10-13 for the validation cohort). The areas under the receiver operating characteristic (ROC) curve of TSS were 0.799 (0.757-0.837) and 0.790 (0.736-0.836) for the training and validation datasets, respectively. The discriminatory power of our model was superior to that of a single variable and the sequential organ failure assessment (SOFA) score (P < 0.001). Moreover, the TSS was well calibrated (P > 0.05).ConclusionsWe developed and validated a novel TSS with good discriminatory power and calibration for the prediction of sepsis risk in trauma patients based on the EMR data.

Project description:BackgroundAutophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.Methods5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.ResultsSurvival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.ConclusionsWe constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

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Scores for sepsis detection and risk stratification - construction of a novel score using a statistical approach and validation of RETTS.

Publications

Scores for sepsis detection and risk stratification - construction of a novel score using a statistical approach and validation of RETTS.

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