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Record W2803488466 · doi:10.1186/s13054-018-2027-8

Association between afterhours admission to the intensive care unit, strained capacity, and mortality: a retrospective cohort study

2018· article· en· W2803488466 on OpenAlexafffundabout
Adam M. Hall, Henry T. Stelfox, Xioaming Wang, Guanmin Chen, Danny J. Zuege, Peter Dodek, Allan Garland, Damon C. Scales, Luc Berthiaume, David A. Zygun, Sean M. Bagshaw

Bibliographic record

VenueCritical Care · 2018
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreCentre for Advancing Health OutcomesUniversity of ManitobaAlberta HealthUniversity of CalgaryUniversity of AlbertaUniversity of British ColumbiaSt. Paul's HospitalAlberta Health Services
FundersAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedicineIntensive care unitEmergency medicineRetrospective cohort studyCohortMultivariate analysisIntensive carePopulationAPACHE IICohort studyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Admission to the intensive care unit (ICU) outside daytime hours has been shown to be variably associated with increased morbidity and mortality. We aimed to describe the characteristics and outcomes of patients admitted to the ICU afterhours (22:00-06:59 h) in a large Canadian health region. We further hypothesized that the association between afterhours admission and mortality would be modified by indicators of strained ICU capacity. METHODS: This is a population-based cohort study of 12,265 adults admitted to nine ICUs in Alberta from June 2012 to December 2014. We used a path-analysis modeling strategy and mixed-effects multivariate regression analysis to evaluate direct and integrated associations (mediated through Acute Physiology and Chronic Health Evaluation (APACHE) II score) between afterhours admission (22:00-06:59 h) and ICU mortality. Further analysis examined the effects of strained ICU capacity and varied definitions of afterhours and weekend admissions. ICU occupancy ≥ 90% or clustering of admissions (≥ 0.15, defined as number of admissions 2 h before or after the index admission, divided by the number of ICU beds) were used as indicators of strained capacity. RESULTS: Of 12,265 admissions, 34.7% (n = 4251) occurred afterhours. The proportion of afterhours admissions varied amongst ICUs (range 26.7-37.8%). Patients admitted afterhours were younger (median (IQR) 58 (44-70) vs 60 (47-70) years, p < 0.0001), more likely to have a medical diagnosis (75.9% vs 72.1%, p < 0.0001), and had higher APACHE II scores (20.9 (8.6) vs 19.9 (8.3), p < 0.0001). Crude ICU mortality was greater for those admitted afterhours (15.9% vs 14.1%, p = 0.007), but following multivariate adjustment there was no direct or integrated effect on ICU mortality (odds ratio (OR) 1.024; 95% confidence interval (CI) 0.923-1.135, p = 0.658). Furthermore, direct and integrated analysis showed no association of afterhours admission and hospital mortality (p = 0.90) or hospital length of stay (LOS) (p = 0.27), although ICU LOS was shorter (p = 0.049). Early-morning admission (00:00-06:59 h) with ICU occupancy ≥ 90% was associated with short-term (≤ 7 days) and all-cause ICU mortality. CONCLUSIONS: One-third of critically ill patients are admitted to the ICU afterhours. Afterhours ICU admission was not associated with greater mortality risk in most circumstances but was sensitive to strained ICU capacity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.368
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations24
Published2018
Admission routes3
Has abstractyes

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