Comorbidity, healthcare utilisation and process of care measures in patients with congenital heart disease in the UK: cross-sectional, population-based study with case–control analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of comorbidities, patterns of healthcare utilisation and primary care recording of clinical indicators in patients with congenital heart disease. PATIENTS AND METHODS: A population-based case-control study using data from general practices across the UK contributing data to the QRESEARCH primary care database. The subjects comprised 9952 patients with congenital heart disease and 29,837 matched controls. Outcome measures were prevalence of selected comorbidities; adjusted odds ratios for risk of comorbidities, healthcare utilisation and clinical indicator recording. RESULTS: The overall crude prevalence of congenital heart disease was 3.05 per 1000 patients (95% CI 2.99 to 3.11). Prevalence of key comorbidities in patients with congenital heart disease ranged from 2.4% (95% CI 2.1% to 2.7%) for epilepsy to 9.3% (95% CI 8.8% to 9.9%) for hypertension. After adjusting for smoking and deprivation, cases were significantly more likely than controls to have each of the cardiovascular comorbidities and an increased risk of diabetes, epilepsy and renal disease. Patients with congenital heart disease were more frequent users of primary care than controls. Patients with congenital heart disease were also more likely than controls to have lifestyle and risk factor measurements recorded in primary care, although overall levels of recording were low. CONCLUSIONS: There is a significant burden of comorbidity associated with congenital heart disease, and levels of primary care utilisation and referral to secondary care are high in this patient group. The predicted future expansion in the numbers of adults with congenital heart disease owing to improvements in survival will have implications for primary and secondary care, and not just tertiary centres offering specialist care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".