Patient centred variables with univariate associations with unplanned ICU admission: a systematic review
Bibliographic record
Abstract
BACKGROUND: Multiple predictive scores using Electronic Patient Record data have been developed for hospitalised patients at risk of clinical deterioration. Methods used to select patient centred variables for inclusion in these scores varies. We performed a systematic review to describe univariate associations with unplanned Intensive Care Unit (ICU) admission with the aim of assisting model development for future scores that predict clinical deterioration. METHODS: Data sources were MEDLINE, EMBASE, CINAHL, CENTRAL and the Cochrane Database of Systematic Reviews. Included studies were published since 2000 describing an association between patient centred variables and unplanned ICU admission determined using univariate analysis. Two authors independently screened titles, abstracts and full texts against inclusion and exclusion criteria. DistillerSR (Evidence Partners, Canada, Ottawa, Ontario) software was used to manage the data and identify duplicate search results. All screening and data extraction forms were implemented within DistillerSR. Study quality was assessed using an adapted version of the Newcastle-Ottawa Scale. Variables were analysed for strength of association with unplanned ICU admission. RESULTS: The database search yielded 1520 unique studies; 1462 were removed after title and abstract review; 57 underwent full text screening; 16 studies were included. One hundred and eighty nine variables with an evaluated univariate association with unplanned ICU admission were described. DISCUSSION: Being male, increasing age, a history of congestive cardiac failure or diabetes, a diagnosis of hepatic disease or having abnormal vital signs were all strongly associated with ICU admission. CONCLUSION: These findings will assist variable selection during the development of future models predicting unplanned ICU admission. TRIAL REGISTRATION: This study is a component of a larger body of work registered in the ISRCTN registry ( ISRCTN12518261 ).
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".