The use of Early Warning System Scores in Pre-Hospital and Emergency Department Settings to Predict Clinical Deterioration: s Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration when applied in the emergency department (ED) or pre-hospital setting. Methods: This systematic review and meta-analysis assessed the predictive abilities of five commonly used EWS scores: National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS) and Cardiac Arrest Risk Triage (CART). Outcomes of interest included admission to ICU, up-to-≥3-day and 30-day mortality. Pooled estimates were calculated using DerSimonian and Laird random-effects models, constructed by type of EWS score, cut-off points, outcomes, and study setting. Risk of bias was assessed using the Newcastle-Ottawa Scale. Meta-regressions investigated between study heterogeneity. Funnel plots tested for publication bias. Results: A total of 11,565 articles was identified, of which 15 were included. Eight and seven articles conducted in the ED and pre-hospital settings, respectively. In the ED, MEWS and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (Confidence Interval (CI) 2.35–6.99) to 6.48 (95% CI 1.83–22.89), p = 0.757. The ability of MEWS (cut-off point ≥ 3) to predict ICU admission had a similar pooled DOR of 5.54 (95% CI 2.02–15.21). In the pre-hospital setting, EWS scores failed to predict 30-day mortality. Using high cut-off points of 5, 7, or 9, their predictability improved when assessing up-to-≥3-day mortality with DOR ranging from 11.60 (95%, CI 9.75–13.88) to 20.37 (95% CI 13.16–31.52).Publication bias was not detected. Participants’ age explained 92% of between-study variance. Conclusion: EWS scores’ predictability of clinical deterioration improves when applied on patient populations that are already in the ED or hospital. The high thresholds used and the scores’ failure to predict 30-day mortality make them less suited for use in the pre-hospital setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".