Impact of COVID-19 pandemic on rates of congenital heart disease procedures among children: prospective cohort analyses of 26 270 procedures in 17 860 children using CVD-COVID-UK consortium record linkage data
Bibliographic record
Abstract
Background The COVID-19 pandemic necessitated major reallocation of healthcare services. Our aim was to assess the impact on paediatric congenital heart disease (CHD) procedures during different pandemic periods compared with the prepandemic period, to inform appropriate responses to future major health services disruptions. Methods and results We analysed 26 270 procedures from 17 860 children between 1 January 2018 and 31 March 2022 in England, linking them to primary/secondary care data. The study period included prepandemic and pandemic phases, with the latter including three restriction periods and corresponding relaxation periods. We compared procedure characteristics and outcomes between each pandemic period and the prepandemic period. There was a reduction in all procedures across all pandemic periods, with the largest reductions during the first, most severe restriction period (23 March 2020 to 23 June 2020), and the relaxation period following second restrictions (3 December 2020 to 4 January 2021) coinciding with winter pressures. During the first restrictions, median procedures per week dropped by 51 compared with the prepandemic period (80 vs 131 per week, p=4.98×10 −08 ). Elective procedures drove these reductions, falling from 96 to 44 per week (p=1.89×10 −06 ), while urgent (28 vs 27 per week, p=0.649) and life-saving/emergency procedures (7 vs 6 per week, p=0.198) remained unchanged. Cardiac surgery rates increased, and catheter-based procedure rates reduced during the pandemic. Procedures for children under 1 year were prioritised, especially during the first four pandemic periods. No evidence was found for differences in postprocedure complications (age-adjusted OR 1.1 (95% CI 0.9, 1.4)) or postprocedure mortality (age and case mix adjusted OR 0.9 (95% CI 0.6, 1.3)). Conclusions Prioritisation of urgent, emergency and life-saving procedures during the pandemic, particularly in infants, did not impact paediatric CHD postprocedure complications or mortality. This information is valuable for future major health services disruptions, though longer-term follow-up of the effects of delaying elective surgery is needed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".