ArticleWorld journal of emergency surgery : WJES2019
Development and validation of a novel predictive score for sepsis risk among trauma patients.
Article in World journal of emergency surgery : WJES, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis.BMC surgery · 2023Trial
- A Novel Risk-Prediction Scoring System for Sepsis among Patients with Acute Pancreatitis: A Retrospective Analysis of a Large Clinical Database.International journal of clinical practice · 2022Trial
- Development and validation of QuadraSynth ML: a novel classifier for sepsis prediction.Frontiers in cellular and infection microbiology · 2026Article
- Identifying early predictive and diagnostic biomarkers and exploring metabolic pathways for sepsis after trauma based on an untargeted metabolomics approach.Scientific reports · 2025Article
- Modified National Early Warning Scores (MNEWS) for Predicting the Outcomes of Suspected Sepsis Patients; A Prospective Cohort Study.Archives of academic emergency medicine · 2025Article
- The Sepsis Score Dilemma: Balancing Precision and Utility.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024Article
- The Clinical Utility of Albumin with Sequential Organ Failure Assessment (SOFA) in Improving 30-Day Mortality Prediction in Patients with Infection in the Emergency Department.Journal of clinical medicine · 2023Article
- In-hospital complications after trauma due to road traffic accidents.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2023Article
- Development and Validation of a Rapid and Efficient Prognostic Scoring System for Sepsis Based on Oxygenation Index, Lactate and Glasgow Coma Scale.Journal of inflammation research · 2023Article
- Dissecting contributions of individual systemic inflammatory response syndrome criteria from a prospective algorithm to the prediction and diagnosis of sepsis in a polytrauma cohort.Frontiers in medicine · 2023Article
- Identifying biomarkers deciphering sepsis from trauma-induced sterile inflammation and trauma-induced sepsis.Frontiers in immunology · 2023Article
- Assessing Insulin Sensitivity and Postprandial Triglyceridemic Response Phenotypes With a Mixed Macronutrient Tolerance Test.Frontiers in nutrition · 2022Article
- A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit.International journal of clinical practice · 2022Article
- Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS.Journal of clinical medicine · 2021Article
- Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients 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 methods: Clinical 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. Results: Based 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 ( Conclusions: We 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.
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