Perioperative Anesthesia-Related Complications and Risk Factors in Children: A Cross-Sectional Observation Study in Rwanda
Bibliographic record
Abstract
BACKGROUND: Despite an increasing awareness of the unmet burden of surgical conditions, information on perioperative complications in children remains limited especially in low-income countries such as Rwanda. The objective of this study was to estimate the prevalence of perioperative anesthesia-related adverse events and to explore potential risk factors associated with them among pediatric surgical patients in public referral hospitals in Rwanda. METHODS: Data were collected for all patients under 5 years of age undergoing surgery in 3 public referral hospitals in Rwanda from June to December 2015. Patient and family history, type of surgery, comorbidities, anesthesia technique, intraoperative adverse events and postoperative events in the postanesthesia care unit (PACU) were recorded. The incidence of perioperative adverse events was assessed and associated risk factors analyzed with univariate logistic regression. RESULTS: Of 354 patients enrolled in this study 11 children had a cardiac arrest. Six (1.7%) suffered an intraoperative cardiac arrest, 2 of whom (0.6%) died intraoperatively. In the PACU, 6 (1.8%) suffered a postoperative cardiac arrest, 5 of whom (1.5%) died in the PACU. One child had both an intraoperative cardiac arrest and then a cardiac arrest in PACU but survived. Eighty-nine children (25.1%) had an intraoperative adverse event, whereas 67 (20.6%) had an adverse event in PACU. A review of the cases where cardiac arrest or death occurred indicated that there were significant lapses in the expected standard of care. Age <1 week was associated with cardiac arrest or death. CONCLUSIONS: The rate of perioperative complications, including death, for children undergoing surgery in tertiary care hospitals in Rwanda was high. Quality improvement measures are needed to decrease this rate among surgical pediatric patients in this low resource setting.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".