Consequences and predictors of heart failure hospitalization in adults with congenital heart disease: A population-based analysis
Bibliographic record
Abstract
A special need for kidney dysfunction surveillance and immediate treatment may be most helpful to improve survival for patients after an incident HFH.Furthermore, repeated HFHs could be regarded as a sign of rapid disease progression and require appropriate treatment to reduce the likelihood of readmission in ACHD patients.The third question is: how does the readmission risk vary over time in 1-year post-discharge from the first HFH in ACHD patients?To this end, I explored the varying risk of readmission with time, the diagnoses prompting readmission at different time periods, and the risk factors for hospital readmission at different time period, within 1-year post-discharge from the first HFH.The Fine and Gray model was used to measure the cumulative incidence and the weekly risks of readmission, correcting for competing risks of death.Half of the ACHD-HF patients in this study were readmitted within 1-year after the first HF discharge.Weekly risk of first readmission declined 50% by week 8 and reached a plateau at week 27 after HF discharge.A three-phase model was hypothesized accordingly as the vigilance (1-8 weeks), transition (9-27 weeks) and plateau phase (28-52 weeks) in the 1-year period after the first HF discharge.Cardiovascular diseases were the most common readmission diagnoses and accounted for 61.68%, 50.32% and 43.17% of all the readmissions during week 1-8, 9-27 and 28-52, respectively.There were 15.17%, 19.94%, and 31.15%readmissions that were attributable to systemic diseases accordingly.Multinomial logistic regression was used to identify the determinants of readmission in different time periods post-discharge.Younger age and interventional VIII procedures in the past 12 months significantly decreased the readmission risk throughout the whole year, whereas, a hospital stay <5 days was associated with an early increased readmission risk.These findings suggest that efforts to prevent readmission need to account for the change in risk over time and disease-tailored strategies should be initiated to reduce readmissions.Taken together, the findings presented in this thesis will help to have a better understanding of the outcomes of HF and the corresponding determinants in ACHD patients, which is currently an understudied area, as well as shape clinical guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".