ArticleIntensive care medicine2015
A clinical prediction model to identify patients at high risk of death in the emergency department.
Article in Intensive care medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of capillary refill time in adult patients: a systematic review with meta-analysis.Critical care (London, England) · 2023Pooled it
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- The Ability of NEWS2 to Detect Sepsis in Adult Patients With Positive Blood Cultures.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2025Article
- Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.BMC emergency medicine · 2025Article
- The Scoring Model to Predict ICU Stay and Mortality After Emergency Admissions in Atrial Fibrillation: A Retrospective Study of 30 366 Patients.Clinical cardiology · 2025Article
- Final Diagnoses of Elderly Emergency Patients Presenting with Weakness and Fatigue in Southeastern Iran.Current aging science · 2025Article
- Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department.BioData mining · 2024Article
- Drinking from the Holy Grail-Does a Perfect Triage System Exist? And Where to Look for It?Journal of personalized medicine · 2024Review
- Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models.Scientific reports · 2023Article
- Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department.BioMed research international · 2023Article
- A Prospective Observational Study Conducted at a Single Center in Japan to Validate a Previously Developed Predictive Formula of In-Hospital Mortality for Patients Aged ≥65 Years with Endogenous Diseases Transported by Ambulance.Medical science monitor : international medical journal of experimental and clinical research · 2022Observational
- Practice of oxygen use in anesthesiology - a survey of the European Society of Anaesthesiology and Intensive Care.BMC anesthesiology · 2022Article
- Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (platelet lymphocyte age neutrophil sex) model.BMC infectious diseases · 2020Article
- Rapid Emergency Medicine Score as a Predictive Value for 30-day Outcome of Nonsurgical Patients Referred to the Emergency Department.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2020Article
- Bedside prediction of intradialytic hemodynamic instability in critically ill patients: the SOCRATE study.Annals of intensive care · 2020Article
- Article
- Article
- A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.Journal of general internal medicine · 2020Article
- Emergency Department to ICU Time Is Associated With Hospital Mortality: A Registry Analysis of 14,788 Patients From Six University Hospitals in The Netherlands.Critical care medicine · 2019Observational
- Prospective Validation of a Checklist to Predict Short-term Death in Older Patients After Emergency Department Admission in Australia and Ireland.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRapid assessment and intervention is important for the prognosis of acutely ill patients admitted to the emergency department (ED). The aim of this study was to prospectively develop and validate a model predicting the risk of in-hospital death based on all available information available at the time of ED admission and to compare its discriminative performance with a non-systematic risk estimate by the triaging first health-care provider.
methodsProspective cohort analysis based on a multivariable logistic regression for the probability of death.
resultsA total of 8,607 consecutive admissions of 7,680 patients admitted to the ED of a tertiary care hospital were analysed. Most frequent APACHE II diagnostic categories at the time of admission were neurological (2,052, 24%), trauma (1,522, 18%), infection categories [1,328, 15%; including sepsis (357, 4.1%), severe sepsis (249, 2.9%), septic shock (27, 0.3%)], cardiovascular (1,022, 12%), gastrointestinal (848, 10%) and respiratory (449, 5%). The predictors of the final model were age, prolonged capillary refill time, blood pressure, mechanical ventilation, oxygen saturation index, Glasgow coma score and APACHE II diagnostic category. The model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.92 and good internal validity. The model performed significantly better than non-systematic triaging of the patient.
conclusionsThe use of the prediction model can facilitate the identification of ED patients with higher mortality risk. The model performs better than a non-systematic assessment and may facilitate more rapid identification and commencement of treatment of patients at risk of an unfavourable outcome.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.