Systematic Review/Meta-analysisPrediction of Early Adverse Events in Emergency Department Patients With Acute Heart Failure: A Systematic Review
Section snippets
Protocol and registration
The reporting of the search methods and results for this review follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for reporting of systematic reviews.5 The protocol was published on the University of York, Centres for Reviews and Dissemination Web site (registration number CRD42017067290).
Study eligibility criteria
Full-text randomized controlled trials and observational studies were considered for inclusion. Studies must have included ED patients with the clinical
Study selection
The results of the screening process are outlined in the PRISMA flow diagram shown in Figure 1. The original search identified a total of 1921 unique citations after removal of duplicates. Initial screening of titles resulted in 1651 records being excluded. Of the 270 articles screened according to abstract, 250 were excluded, leaving 20 articles for full-text review. Of these, 11 articles were excluded, leaving 9 articles describing 6 risk prediction tools included in the evidence synthesis.
Summary of main results
This review has identified 6 risk prediction tools that might be useful in predicting short-term adverse events and guiding disposition decision for ED patients with AHF. The hierarchy of evidence supporting implementation and use of a risk prediction tool should include derivation, internal validation, external validation, and evidence of effect on either patient-oriented or health services outcomes.18, 19 No risk prediction tool for AHF has achieved this level of supporting evidence. However,
Conclusions
Our systematic review of the literature identified several risk prediction scores for predicting mortality and other adverse events in ED AHF patients. Of these, the EHMRG and OHFRS are supported by the most robust bodies of evidence but differ in important ways with respect to their usability and the types of outcomes that the scores are designed to predict. These risk scores might provide useful prognostic information for ED AHF patients when making disposition decisions, but have yet to show
Funding Sources
This work was supported by a systematic review grant from the Alberta Health Services Emergency Strategic Clinical Network.
Disclosures
The authors have no conflicts of interest to disclose. Dr McRae is a coinvestigator with the Ottawa/Canadian Heart Failure Risk Score team.
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Cited by (16)
Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure
2022, International Journal of CardiologyCitation Excerpt :Using only 10 predictor variables, the model can predict mortality outcomes at two time points, and identifies those who are low risk, with 0% mortality at 7- and 30-days in those in the lowest risk quintile [9]. While other predictive models have been derived for use in patients with HF in the emergent setting, few have been prospectively validated, and none are used routinely in clinical practice [10]. Only the EHMRG model has been compared with and found to be superior to physician estimated risk, with c-indices of 0.71 vs. 0.81 for prediction of 7-day mortality in favor of the mathematical model [9,11].
Short-term mortality risk score for de novo acute heart failure (ESSIC-FEHF)
2020, European Journal of Internal MedicineCitation Excerpt :In addition, previously, predictive models and risk scores have been developed to predict poor outcomes for all patients who arrive at the ED with symptoms of AHF. Further, only some of them have been developed to stratify by risk of adverse outcomes in either outpatients or inpatients with heart failure [7]. This research team hypothesized that various different variables determine prognosis in the FEHF and that it is possible to combine them in a risk score to assess probability of death during the vulnerable phase after diagnosis of heart failure.
Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study
2019, Canadian Journal of CardiologyOutcomes of acute heart failure patients managed in the emergency department
2023, Canadian Journal of Emergency Medicine
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