Adverse Events Among Hospitalized Critically Ill Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to estimate the frequency and type of adverse events (AEs) among critically ill patients and identify patient and hospital factors associated with AEs and clinical and health care utilization consequences of AEs. MATERIALS AND METHODS: This retrospective cohort study includes patients admitted to 30 intensive care units (ICUs) in Alberta, Canada from May 2014 to April 2017. The main outcome was AEs derived from validated ICD-10, Canadian code algorithms for 18 AEs. Estimates of the proportion and rate of AEs are presented. The association between documented AEs and patient (eg, age, sex, comorbidities) and hospital (eg, ICU site and type, length of stay, readmission) variables are described using regression methods. RESULTS: Of 49,447 hospital admissions with admission to ICU, ≥1 AEs were documented in 12,549 (25%) admissions. The most common AEs were respiratory complications (10%) and hospital-acquired infections (9%). AEs were associated with having ≥2 comorbidities [odds ratio (OR)=1.4, 95% confidence interval (CI)=1.3-1.4], being admitted to the ICU from the operating room or another hospital ward (OR=1.8, 95% CI=1.7-2.0 and OR=2.7, 95% CI=2.5-3.0, respectively) and being readmitted to ICU during their hospital stay (OR=4.8, 95% CI=4.7-5.6). Patients with an AE stayed 5.4 days longer in ICU (95% CI=5.2-5.6 d, P<0.001), 18.2 days longer in hospital (95% CI=17.7-18.8 d, P<0.001) and had increased odds of hospital mortality (OR=1.5, 95% CI=1.4-1.6) than those without an AE. CONCLUSIONS: AEs are common among critically ill patients and certain factors are associated with AEs. Documented AEs are associated with longer stays and increased mortality.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".