Epidemiology and Risk Factors for Constrictive Pericarditis in a Statewide Australian Cohort of Patients With Pericardial Disease
Bibliographic record
Abstract
BACKGROUND: Constrictive pericarditis is a rare but serious complication of pericardial disease, with limited longitudinal studies assessing its risk factors. In this study we evaluated the epidemiology and predictors of constrictive pericarditis in a large population-based cohort. METHODS: We conducted a retrospective cohort study of all hospitalized patients with pericardial disease from 2004 to 2021 using the Australian New South Wales Admitted Patient Data Collection database. Multivariable logistic regression identified risk factors for constrictive pericarditis at index admission with pericardial disease, whereas time-dependent Cox regression and the Fine-Gray method were used to assess risk factors during follow-up. RESULTS: Among 45,445 patients with pericardial disease, 763 (1.7%) developed constrictive pericarditis (median age 64.3 years; 63.4% men). The median time from cardiac surgery to diagnosis of constriction was 6 months and from autoimmune disease diagnosis it was 2 years. Of these patients, 530 (1.2%) had constriction at index presentation of pericardial disease and 233 (0.5%) developed constriction during follow-up. Constriction at index presentation was associated with older age, malignancy (odds ratio [OR] 1.5, 95% confidence interval [CI] 1.2-1.8), tuberculosis (OR 3.9, 95% CI 1.4-8.9), liver disease (OR 1.7, 95% CI 1.3-2.2), and heart failure (OR 2.7 95% CI 2.2-3.3). Constriction identified during follow-up was more common after hospitalization for heart failure (hazard ratio [HR] 5.3, 95% CI 3.4-8.2), nonconstrictive recurrent pericardial disease requiring hospitalization (HR 3.7, 95% CI 2.3-6.2), or pericardiocentesis (HR 3.6, 95% CI 2.7-4.8). CONCLUSIONS: In this large, contemporary cohort, constrictive pericarditis was rare but occurred more commonly after a diagnosis of tuberculosis, malignancy, liver disease, heart failure, recurrent pericardial disease, and pericardiocentesis. These findings highlight the importance of long-term vigilance when considering constriction in at-risk populations.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".