Heart failure risk predictions in adult patients with congenital heart disease: a systematic review
Bibliographic record
Abstract
To summarise existing heart failure (HF) risk prediction models and describe the risk factors for HF-related adverse outcomes in adult patients with congenital heart disease (CHD). We performed a systematic search of MEDLINE, EMBASE and Cochrane databases from January 1996 to December 2018. Studies were eligible if they developed multivariable models for risk prediction of decompensated HF in adult patients with CHD (ACHD), death in patients with ACHD-HF or both, or if they reported corresponding predictors. A standardised form was used to extract information from selected studies. Twenty-five studies met the inclusion criteria and all studies were at moderate to high risk of bias. One study derived a model to predict the risk of a composite outcome (HF, death or arrhythmia) with a c-statistic of 0.85. Two studies applied an existing general HF model to patients with ACHD but did not report model performance. Twenty studies presented predictors of decompensated HF, and four examined patient characteristics associated with mortality (two reported predictors of both). A wide variation in population characteristics, outcome of interest and candidate risk factors was observed between studies. Although there were substantial inconsistencies regarding which patient characteristics were predictive of HF-related adverse outcomes, brain natriuretic peptide, New York Heart Association class and CHD lesion characteristics were shown to be important predictors. To date, evidence in the published literature is insufficient to accurately profile patients with ACHD. High-quality studies are required to develop a unique ACHD-HF prediction model and confirm the predictive roles of potential risk factors.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".