Prevalence of ICU Delirium in Postoperative Pediatric Cardiac Surgery Patients
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to determine the prevalence of ICU delirium in children less than 18 years old that underwent cardiac surgery within the last 30 days. The secondary aim of the study was to identify risk factors associated with ICU delirium in postoperative pediatric cardiac surgical patients. DESIGN: A 1-day, multicenter point-prevalence study of delirium in pediatric postoperative cardiac surgery patients. SETTING: Twenty-seven pediatric cardiac and general critical care units caring for postoperative pediatric cardiac surgery patients in North America. PATIENTS: All children less than 18 years old hospitalized in the cardiac critical care units at 06:00 on a randomly selected, study day. INTERVENTIONS: Eligible children were screened for delirium using the Cornell Assessment of Pediatric Delirium by the study team in collaboration with the bedside nurse. MEASUREMENT AND MAIN RESULTS: Overall, 181 patients were enrolled and 40% (n = 73) screened positive for delirium. There were no statistically significant differences in patient demographic information, severity of defect or surgical procedure, past medical history, or postoperative day between patients screening positive or negative for delirium. Our bivariate analysis found those patients screening positive had a longer duration of mechanical ventilation (12.8 vs 5.1 d; p = 0.02); required more vasoactive support (55% vs 26%; p = 0.0009); and had a higher number of invasive catheters (4 vs 3 catheters; p = 0.001). Delirium-positive patients received more total opioid exposure (1.80 vs 0.36 mg/kg/d of morphine equivalents; p < 0.001), did not have an ambulation or physical therapy schedule (p = 0.02), had not been out of bed in the previous 24 hours (p < 0.0002), and parents were not at the bedside at time of data collection (p = 0.008). In the mixed-effects logistic regression analysis of modifiable risk factors, the following variables were associated with a positive delirium screen: 1) pain score, per point increase (odds ratio, 1.3; 1.06-1.60); 2) total opioid exposure, per mg/kg/d increase (odds ratio, 1.35; 1.06-1.73); 3) SBS less than 0 (odds ratio, 4.01; 1.21-13.27); 4) pain medication or sedative administered in the previous 4 hours (odds ratio, 3.49; 1.32-9.28); 5) no progressive physical therapy or ambulation schedule in their medical record (odds ratio, 4.40; 1.41-13.68); and 6) parents not at bedside at time of data collection (odds ratio, 2.31; 1.01-5.31). CONCLUSIONS: We found delirium to be a common problem after cardiac surgery with several important modifiable risk factors.
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How this classification was reachedexpand
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 | 0.000 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".