Observational studyBMC emergency medicine2022
Outcome prediction for patients assessed by the medical emergency team: a retrospective cohort study.
Observational study in BMC emergency medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- A machine learning model for predicting short-term outcomes after rapid response system activation.Acute medicine & surgeryArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundMedical emergency teams (METs) have been implemented to reduce hospital mortality by the early recognition and treatment of potentially life-threatening conditions. The objective of this study was to establish a clinically useful association between clinical variables and mortality risk, among patients assessed by the MET, and further to design an easy-to-use risk score for the prediction of death within 30 days.
methodsObservational retrospective register study in a tertiary university hospital in Sweden, comprising 2,601 patients, assessed by the MET from 2010 to 2015. Patient registry data at the time of MET assessment was analysed from an epidemiological perspective, using univariable and multivariable analyses with death within 30 days as the outcome variable. Predictors of outcome were defined from age, gender, type of ward for admittance, previous medical history, acute medical condition, vital parameters and laboratory biomarkers. Identified factors independently associated with mortality were then used to develop a prognostic risk score for mortality.
resultsThe overall 30-day mortality was high (29.0%). We identified thirteen factors independently associated with 30-day mortality concerning; age, type of ward for admittance, vital parameters, laboratory biomarkers, previous medical history and acute medical condition. A MET risk score for mortality based on the impact of these individual thirteen factors in the model yielded a median (range) AUC of 0.780 (0.774-0.785) with good calibration. When corrected for optimism by internal validation, the score yielded a median (range) AUC of 0.768 (0.762-0.773).
conclusionsAmong clinical variables available at the time of MET assessment, thirteen factors were found to be independently associated with 30-day mortality. By applying a simple risk scoring system based on these individual factors, patients at higher risk of dying within 30 days after the MET assessment may be identified and treated earlier in the process.
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