Quality and Safety Initiatives in a Pediatric and Congenital Heart Surgery Program in a Low- and Middle-Income Country: The Impact of International Collaboration
Bibliographic record
Abstract
INTRODUCTION: This study assessed the impact of a quality and safety (Q&S) improvement program on outcomes in pediatric and congenital heart surgery (PCHS) through an international non-governmental collaboration in a low-and-middle-income country (LMIC). METHODS: Surgical data from two distinct periods, PRE (January 2016 - December 2019) and POST (January 2020 - May 2024) Q&S implementation, were analyzed. Outcomes included 30-day mortality, urgency status, patient age, and procedure complexity using the Risk Adjustment for Congenital Heart Surgery (RACHS) 1 classification. RESULTS: A total of 4,297 surgeries were performed: 2,429 in the PRE and 1,868 in the POST era. Overall, 30-day mortality decreased significantly from 7.5% to 5.1% (P = 0.002), reaching 3.1% in 2024. Urgent surgeries increased from 28% to 44% (P < 0.0001), while mortality in elective and urgent cases dropped from 3.9% to 1.7% (P = 0.0007) and from 16.5% to 9.6% (P < 0.0001), respectively. A shift toward more neonatal and infant cases was observed, with significant reductions in mortality in both groups (P = 0.01). Case mix complexity also increased (RACHS categories 3-6), yet mortality declined across all RACHS strata. CONCLUSION: The introduction of Q&S initiatives led to marked improvements in PCHS outcomes, even amid growing case complexity and acuity. These findings highlight the value of structured protocols and sustained Q&S efforts and underscore the transformative role of international partnerships in strengthening surgical care in LMICs.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".