The Frequency of and Reasons for Hospital Readmission Post Percutaneous Coronary Intervention
Bibliographic record
Abstract
Objectives: The objective of this study was to determine the frequency of and reasons for six months unplanned readmission to hospital post Percutaneous Coronary Intervention (PCI). Background: PCI has become an important and effective way of treating heart disease; however the occurrence of hospital readmission post PCI is not well documented. Methods: The frequency of hospital readmissions were tracked for six months following PCI using the APPROACH registry database. The incidence of and reasons for hospital readmission were determined using the Capital Health Region Administrative Database and the ICD-10 coding for hospital readmission. Results: Of 2641 subjects, it was observed that 4.5% of patients were readmitted to hospital within six months of PCI and 18.6% of patients visited the ED for reasons directly related to PCI. The top reasons for readmission were chest pain (31.2%), atherosclerotic heart disease (24.3%), bleeding/complications with anticoagulation (10.9%), myocardial infarction (7.5%) and procedural complications (3.7%). Factors shown to be independent predictors of hospital readmission were congestive heart failure (p = 0.009), pulmonary disease (p = 0.008), malignancy (p = 0.002), liver disease (p = 0.012) and female gender (p = 0.015). Conclusions: The data indicates that while in-patient six months unplanned hospital readmission post PCI is relatively low, ED visits are substantial. The creation of a post PCI clinic and/or a post PCI hotline may prove to be useful in decreasing hospital visits post PCI. If patients are routinely followed up in the early post PCI period, access to health care may be improved, allowing complications to be observed sooner and care to be given quicker.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".