Risk Factors and Causes of Readmission to the Pediatric Intensive Care Unit After Cardiac Surgery
Bibliographic record
Abstract
Introduction Unplanned readmissions to the pediatric intensive care unit (PICU) following cardiac surgery remain poorly understood. Such readmissions are associated with increased morbidity and present significant challenges to healthcare. This study aimed to identify characteristics associated with unplanned PICU readmission during the same hospitalization following cardiac surgery. Method We conducted a retrospective analysis using prospectively collected data from a tertiary care children's hospital from January 2015 to January 2021. All pediatric patients who underwent cardiac surgery and were transferred alive from the PICU were included. Each patient readmitted to the PICU was matched to two controls based on age and PELOD score at the time of initial PICU admission postsurgery to identify factors associated with readmission among 72 variables. Results Of the 956 patients discharged alive from the PICU after cardiac surgery, 23 (2.4%) required unplanned readmission. Over half (12/23, 52.2%) of these readmissions occurred within 48 h of PICU discharge. Univariate analysis identified preoperative arrhythmias ( P = .037) and postoperative single-ventricle physiology ( P = .024) as potential risk factors for readmission. Male sex appeared to be a protective factor against unplanned PICU readmission ( P = .026). Although preoperative vasopressor requirement, postoperative altered ventricular function and known pleural effusion at PICU discharge were not significantly associated with PICU readmission, these factors showed a trend toward significance. After multivariate analysis, postoperative single-ventricle physiology remained the sole independent risk factor associated with unplanned PICU readmission with an odds ratio of 8.6 (95% confidence interval, 1.9-46.5). Conclusion While unscheduled PICU readmissions following cardiac surgery are rare, postoperative single-ventricle physiology emerged as an independent risk factor.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".