SynthesisEmergency medicine journal : EMJ2022
Predicting inhospital admission at the emergency department: a systematic review.
Synthesis in Emergency medicine journal : EMJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Optimising Retraining Frequency for a Paediatric Emergency Department Admission Prediction Model: Development and Temporal Validation Using Real-World Data.Emergency medicine Australasia : EMA · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- Integrated analysis for electronic health records with structured and sporadic missingness.Journal of biomedical informatics · 2025Article
- Development and validation of a clinical prediction model for hospitalization after emergency department admission in patients with cancer.ESMO real world data and digital oncology · 2025Article
- Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.Healthcare informatics research · 2025Article
- Physician performance scores used to predict emergency department admission numbers and excessive admissions burden.BMJ health & care informatics · 2024Article
- Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A fast emergency department triage score based on mobility, mental status and oxygen saturation compared with the emergency severity index: a prospective cohort study.QJM : monthly journal of the Association of Physicians · 2023Observational
- Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review.Journal of personalized medicine · 2023Review
- Article
- A prospective, internal validation of an emergency patient triage tool for use in a low resource setting.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundED crowding has potential detrimental consequences for both patient care and staff. Advancing disposition can reduce crowding. This may be achieved by using prediction models for admission. This systematic review aims to present an overview of prediction models for admission at the ED. Furthermore, we aimed to identify the best prediction tool based on its performance, validation, calibration and clinical usability.
methodsWe included observational studies published in Embase.com, Medline Ovid, Cochrane CENTRAL, Web of Science Core Collection or Google scholar, in which admission models were developed or validated in a general medical population in European EDs including the UK. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist to assess quality of model development. Model performance was presented as discrimination and calibration. The search was performed on 11 October 2020.
resultsIn total, 18 539 articles were identified. We included 11 studies, describing 16 different models, comprising the development of 9 models and 12 external validations of 11 models. The risk of bias of the development studies was considered low to medium. Discrimination, as represented by the area under the curve ranged from 0.630 to 0.878. Calibration was assessed in seven models and was strong. The best performing models are the models of Lucke
conclusionNone of the models are yet implemented in EDs. Further research is needed to assess the applicability and implementation of the best performing models in the ED. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42017057975.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.