The prevalence of cirrhotic cardiomyopathy according to different diagnostic criteria
Bibliographic record
Abstract
BACKGROUND AND AIMS: Recently published criteria by 2019 Cirrhotic Cardiomyopathy Consortium set a lower threshold for reduced ejection fraction to diagnose systolic dysfunction in cirrhotic patients, and stress testing was replaced by echocardiography strain imaging. The criteria to diagnose diastolic dysfunction are in general concordant with the 2016 ASE/EACVI guidelines and differ considerably from the 2005 Montreal recommendations. We aimed to assess the prevalence of cirrhotic cardiomyopathy according to different diagnostic criteria. METHODS: Cirrhotic patients without another structural heart disease, arterial hypertension, portal vein thrombosis, HCC outside Milan criteria and presence of TIPS were enrolled. Speckle-tracking echocardiography was performed by EACVI certified investigators. RESULTS: A total of 122 patients with cirrhosis fulfilled the inclusion criteria. Overall prevalence of cirrhotic cardiomyopathy was similar for 2005 Montreal and 2019 CCC: 67.2% vs 55.7% (P = .09); and significantly higher compared to 2009 ASE/EACVI criteria: 67.2% vs 35.2% (P < .0001) and 55.7% vs 35.2% (P = .002) respectively. Significantly more patients had diastolic dysfunction according to the 2005 Montreal compared to the 2009 ASE/EACVI and 2019 CCC criteria: 64.8% vs 32.8% (P < .0001) and 64.8% vs 7.4% (P < .0001). Systolic dysfunction was more frequently diagnosed according to 2019 CCC criteria compared to 2005 Montreal (53.3% vs 16.4%,P < .0001) or ASE/EACVI criteria (53.3% vs 4.9%,P < .0001). CONCLUSION: Cirrhotic cardiomyopathy was present in around 60% of cirrhotic patients when applying the hepatological criteria. A considerably higher prevalence of systolic dysfunction according to the 2019 CCC criteria was observed. Long-term follow-up studies are needed to establish the validity of these criteria to predict clinically relevant outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".