Risk factors associated with unplanned ICU admissions following paediatric surgery: A systematic review
Bibliographic record
Abstract
Background: Unplanned admissions to the intensive care unit (ICU) have important implications in the general management of patients. Research in this area has been conducted in the adult and non-surgical population. To date, there is no systematic review addressing risk factors in the paediatric surgical population. Objectives: To synthesise the information from studies that explore the risk factors associated with unplanned ICU admissions following surgery in children through a systematic review process. Methods: We conducted a systematic review of published literature (PROSPERO registration CRD42020163766), adhering to the Preferred Reporting of Observational Studies and Meta-Analysis (PRISMA) statement. The Population, Exposure, Comparator, Outcome (PECO) strategy used was based on: population - paediatric population, exposure - risk factors, comparator - other, and outcome - unplanned ICU admission. Data that reported on unplanned ICU admissions following paediatric surgery were extracted and analysed. Quality of the studies was assessed using the Newcastle-Ottawa Scale. Results: Seven studies were included in the data synthesis. Four studies were of good quality with the Newcastle-Ottawa Scale score ≥7 points. The pooled prevalence (95% confidence interval) estimate of unplanned ICU stay was 2.69% (0.05 - 8.6%) and ranged between 0.06% and 8.3%. Significant risk factors included abnormal sleep studies and the presence of comorbidities in adenotonsillectomy surgery. In the general surgical population, younger age, comorbidities and general anaesthesia were significant. Abdominal surgery and ear, nose and throat (ENT) surgery resulted in a higher risk of unplanned ICU admission. Owing to the heterogeneity of the data, a meta-analysis with risk prediction could not be performed. Conclusion: Significant patient, surgical and anaesthetic risk factors associated with unplanned ICU admission in children following surgery are described in this systematic review. A combination of these factors may direct planning toward anticipation of the need for a higher level of postoperative care. Further work to develop a predictive score for unplanned ICU stay is desirable. Contributions of the study: To date, we have not been able to find a systematic review addressing the risk factors associated with unplanned ICU admissions in paediatric surgical patients.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".